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ORCIDNeurIPS 2019: Vancouver, BC, Canada
- Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alché-Buc, Emily B. Fox, Roman Garnett:
Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada. 2019 - Risto Vuorio, Shao-Hua Sun, Hexiang Hu, Joseph J. Lim:
Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation. 1-12 - Jiasen Lu, Dhruv Batra, Devi Parikh, Stefan Lee:
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. 13-23 - Liwei Wu, Shuqing Li, Cho-Jui Hsieh, James L. Sharpnack:
Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers. 24-34 - Jiawang Bian, Zhichao Li, Naiyan Wang, Huangying Zhan, Chunhua Shen, Ming-Ming Cheng, Ian D. Reid:
Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video. 35-45 - Hyeonwoo Yu, Beomhee Lee:
Zero-shot Learning via Simultaneous Generating and Learning. 46-56 - Brian Lubars, Chenhao Tan:
Ask not what AI can do, but what AI should do: Towards a framework of task delegability. 57-67 - Niki Parmar, Prajit Ramachandran, Ashish Vaswani, Irwan Bello, Anselm Levskaya, Jonathon Shlens:
Stand-Alone Self-Attention in Vision Models. 68-80 - Ruben Villegas, Arkanath Pathak, Harini Kannan, Dumitru Erhan, Quoc V. Le, Honglak Lee:
High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks. 81-91 - Matthias Minderer, Chen Sun, Ruben Villegas, Forrester Cole, Kevin P. Murphy, Honglak Lee:
Unsupervised learning of object structure and dynamics from videos. 92-102 - Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Dehao Chen, Mia Xu Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Yonghui Wu, Zhifeng Chen:
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism. 103-112 - Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, Sergey Levine:
Meta-Learning with Implicit Gradients. 113-124 - Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry:
Adversarial Examples Are Not Bugs, They Are Features. 125-136 - Vineet Kosaraju, Amir Sadeghian, Roberto Martín-Martín, Ian D. Reid, Hamid Rezatofighi, Silvio Savarese:
Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks. 137-146 - Xiaosong Zhang, Fang Wan, Chang Liu, Rongrong Ji, Qixiang Ye:
FreeAnchor: Learning to Match Anchors for Visual Object Detection. 147-155 - Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu:
Private Hypothesis Selection. 156-167 - Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman:
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. 168-180 - Mark Bun, Thomas Steinke:
Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation. 181-191 - Paroma Varma, Frederic Sala, Shiori Sagawa, Jason Alan Fries, Daniel Y. Fu, Saelig Khattar, Ashwini Ramamoorthy, Ke Xiao, Kayvon Fatahalian, James Priest, Christopher Ré:
Multi-Resolution Weak Supervision for Sequential Data. 192-203 - Duc Tam Nguyen, Maximilian Dax, Chaithanya Kumar Mummadi, Thi-Phuong-Nhung Ngo, Thi Hoai Phuong Nguyen, Zhongyu Lou, Thomas Brox:
DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-supervision. 204-214 - Vladimir V. Kniaz, Vladimir A. Knyaz, Fabio Remondino:
The Point Where Reality Meets Fantasy: Mixed Adversarial Generators for Image Splice Detection. 215-226 - Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong:
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle. 227-238 - Chao Yang, Xiaojian Ma, Wenbing Huang, Fuchun Sun, Huaping Liu, Junzhou Huang, Chuang Gan:
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement. 239-249 - Kimia Nadjahi, Alain Durmus, Umut Simsekli, Roland Badeau:
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance. 250-260 - Soheil Kolouri, Kimia Nadjahi, Umut Simsekli, Roland Badeau, Gustavo K. Rohde:
Generalized Sliced Wasserstein Distances. 261-272 - Thanh Huy Nguyen, Umut Simsekli, Mert Gürbüzbalaban, Gaël Richard:
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise. 273-283 - Sefi Bell-Kligler, Assaf Shocher, Michal Irani:
Blind Super-Resolution Kernel Estimation using an Internal-GAN. 284-293 - Alexandre Louis Lamy, Ziyuan Zhong:
Noise-tolerant fair classification. 294-305 - Bingzhe Wu, Shiwan Zhao, Chaochao Chen, Haoyang Xu, Li Wang, Xiaolu Zhang, Guangyu Sun, Jun Zhou:
Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection. 306-316 - Xueting Li, Sifei Liu, Shalini De Mello, Xiaolong Wang, Jan Kautz, Ming-Hsuan Yang:
Joint-task Self-supervised Learning for Temporal Correspondence. 317-327 - Justin Domke:
Provable Gradient Variance Guarantees for Black-Box Variational Inference. 328-337 - Justin Domke, Daniel Sheldon:
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation. 338-347 - David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P. Lillicrap, Gregory Wayne:
Experience Replay for Continual Learning. 348-358 - Boris Hanin, David Rolnick:
Deep ReLU Networks Have Surprisingly Few Activation Patterns. 359-368 - Peter Anderson, Ayush Shrivastava, Devi Parikh, Dhruv Batra, Stefan Lee:
Chasing Ghosts: Instruction Following as Bayesian State Tracking. 369-379 - Yu Sun, Jiaming Liu, Ulugbek Kamilov:
Block Coordinate Regularization by Denoising. 380-390 - Tatjana Chavdarova, Gauthier Gidel, François Fleuret, Simon Lacoste-Julien:
Reducing Noise in GAN Training with Variance Reduced Extragradient. 391-401 - Zihan Li, Matthias Fresacher, Jonathan Scarlett:
Learning Erdos-Renyi Random Graphs via Edge Detecting Queries. 402-412 - Hisham Husain, Richard Nock, Robert C. Williamson:
A Primal-Dual link between GANs and Autoencoders. 413-422 - Congchao Wang, Yizhi Wang, Yinxue Wang, Chiung-Ting Wu, Guoqiang Yu:
muSSP: Efficient Min-cost Flow Algorithm for Multi-object Tracking. 423-432 - Qiming Zhang, Jing Zhang, Wei Liu, Dacheng Tao:
Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation. 433-443 - Patrick Putzky, Max Welling:
Invert to Learn to Invert. 444-454 - Nikolaos Tziavelis, Ioannis Giannakopoulos, Katerina Doka, Nectarios Koziris, Panagiotis Karras:
Equitable Stable Matchings in Quadratic Time. 455-465 - Maxime Bucher, Tuan-Hung Vu, Matthieu Cord, Patrick Pérez:
Zero-Shot Semantic Segmentation. 466-477 - Chengzhi Mao, Ziyuan Zhong, Junfeng Yang, Carl Vondrick, Baishakhi Ray:
Metric Learning for Adversarial Robustness. 478-489 - Qiangeng Xu, Weiyue Wang, Duygu Ceylan, Radomír Mech, Ulrich Neumann:
DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction. 490-500 - Zijun Gao, Yanjun Han, Zhimei Ren, Zhengqing Zhou:
Batched Multi-armed Bandits Problem. 501-511 - Fan-Yun Sun, Meng Qu, Jordan Hoffmann, Chin-Wei Huang, Jian Tang:
vGraph: A Generative Model for Joint Community Detection and Node Representation Learning. 512-522 - Garrett Bernstein, Daniel Sheldon:
Differentially Private Bayesian Linear Regression. 523-533 - Yitian Yuan, Lin Ma, Jingwen Wang, Wei Liu, Wenwu Zhu:
Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos. 534-544 - Bichuan Guo, Yuxing Han, Jiangtao Wen:
AGEM: Solving Linear Inverse Problems via Deep Priors and Sampling. 545-556 - Changqing Zhang, Zongbo Han, Yajie Cui, Huazhu Fu, Joey Tianyi Zhou, Qinghua Hu:
CPM-Nets: Cross Partial Multi-View Networks. 557-567 - Xihui Liu, Guojun Yin, Jing Shao, Xiaogang Wang, Hongsheng Li:
Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis. 568-578 - Andrey Kolobov, Yuval Peres, Cheng Lu, Eric Horvitz:
Staying up to Date with Online Content Changes Using Reinforcement Learning for Scheduling. 579-589 - Nikolas Ioannou, Celestine Mendler-Dünner, Thomas P. Parnell:
SySCD: A System-Aware Parallel Coordinate Descent Algorithm. 590-600 - Artem Sobolev, Dmitry P. Vetrov:
Importance Weighted Hierarchical Variational Inference. 601-613 - Robert M. Gower, Dmitry Kovalev, Felix Lieder, Peter Richtárik:
RSN: Randomized Subspace Newton. 614-623 - Yuhui Wang, Hao He, Xiaoyang Tan, Yaozhong Gan:
Trust Region-Guided Proximal Policy Optimization. 624-634 - Dina Bashkirova, Ben Usman, Kate Saenko:
Adversarial Self-Defense for Cycle-Consistent GANs. 635-645 - Othmane Sebbouh, Nidham Gazagnadou, Samy Jelassi, Francis R. Bach, Robert M. Gower:
Towards closing the gap between the theory and practice of SVRG. 646-656 - Armin Lederer, Jonas Umlauft, Sandra Hirche:
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control. 657-667 - Chunjin Song, Zhijie Wu, Yang Zhou, Minglun Gong, Hui Huang:
ETNet: Error Transition Network for Arbitrary Style Transfer. 668-677 - Max Vladymyrov:
No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms. 678-687 - Shaojie Bai, J. Zico Kolter, Vladlen Koltun:
Deep Equilibrium Models. 688-699 - Gamaleldin F. Elsayed, Simon Kornblith, Quoc V. Le:
Saccader: Improving Accuracy of Hard Attention Models for Vision. 700-712 - Miaoyan Wang, Yuchen Zeng:
Multiway clustering via tensor block models. 713-723 - Wang Chi Cheung:
Regret Minimization for Reinforcement Learning with Vectorial Feedback and Complex Objectives. 724-734 - Yong Guo, Yin Zheng, Mingkui Tan, Qi Chen, Jian Chen, Peilin Zhao, Junzhou Huang:
NAT: Neural Architecture Transformer for Accurate and Compact Architectures. 735-747 - Ruidi Chen, Ioannis Ch. Paschalidis:
Selecting Optimal Decisions via Distributionally Robust Nearest-Neighbor Regression. 748-758 - Xuanyi Dong, Yi Yang:
Network Pruning via Transformable Architecture Search. 759-770 - Junbang Liang, Ming C. Lin, Vladlen Koltun:
Differentiable Cloth Simulation for Inverse Problems. 771-780 - Aaron Schein, Scott W. Linderman, Mingyuan Zhou
, David M. Blei, Hanna M. Wallach:
Poisson-Randomized Gamma Dynamical Systems. 781-792 - Gengshan Yang, Deva Ramanan:
Volumetric Correspondence Networks for Optical Flow. 793-803 - Adrian V. Dalca, Marianne Rakic, John V. Guttag, Mert R. Sabuncu:
Learning Conditional Deformable Templates with Convolutional Networks. 804-816 - Han Liu, Zhizhong Han, Yu-Shen Liu, Ming Gu:
Fast Low-rank Metric Learning for Large-scale and High-dimensional Data. 817-827 - Zhao Song, Ruosong Wang, Lin F. Yang, Hongyang Zhang, Peilin Zhong:
Efficient Symmetric Norm Regression via Linear Sketching. 828-838 - Rémi Cadène, Corentin Dancette, Hédi Ben-Younes, Matthieu Cord, Devi Parikh:
RUBi: Reducing Unimodal Biases for Visual Question Answering. 839-850 - Jinwoo Choi, Chen Gao, Joseph C. E. Messou, Jia-Bin Huang:
Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition. 851-863 - Yichao Zhou, Haozhi Qi, Jingwei Huang, Yi Ma:
NeurVPS: Neural Vanishing Point Scanning via Conic Convolution. 864-873 - Jianlong Chang, Xinbang Zhang, Yiwen Guo, Gaofeng Meng, Shiming Xiang, Chunhong Pan:
DATA: Differentiable ArchiTecture Approximation. 874-884 - Tingting Qiao, Jing Zhang, Duanqing Xu, Dacheng Tao:
Learn, Imagine and Create: Text-to-Image Generation from Prior Knowledge. 885-895 - Miao Zhang, Jingjing Li, Ji Wei, Yongri Piao, Huchuan Lu:
Memory-oriented Decoder for Light Field Salient Object Detection. 896-906 - Xuesong Niu, Hu Han, Shiguang Shan, Xilin Chen:
Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition. 907-917 - Natalia Neverova, David Novotný, Andrea Vedaldi:
Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels. 918-926 - Chris Wendler, Markus Püschel, Dan Alistarh:
Powerset Convolutional Neural Networks. 927-938 - Arsenii Vanunts, Alexey Drutsa:
Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer. 939-951 - Hadrien Hendrikx, Francis R. Bach, Laurent Massoulié:
An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums. 952-962 - Zhijian Liu, Haotian Tang, Yujun Lin, Song Han:
Point-Voxel CNN for Efficient 3D Deep Learning. 963-973 - Mohamed Akrout, Collin Wilson, Peter Conway Humphreys, Timothy P. Lillicrap, Douglas B. Tweed:
Deep Learning without Weight Transport. 974-982 - Aadirupa Saha, Aditya Gopalan:
Combinatorial Bandits with Relative Feedback. 983-993 - Tao Sun, Yuejiao Sun, Dongsheng Li, Qing Liao:
General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme. 994-1004 - Leonidas J. Guibas, Qixing Huang, Zhenxiao Liang:
A Condition Number for Joint Optimization of Cycle-Consistent Networks. 1005-1015 - Nicki Skafte Detlefsen, Søren Hauberg:
Explicit Disentanglement of Appearance and Perspective in Generative Models. 1016-1026 - Hédi Hadiji:
Polynomial Cost of Adaptation for X-Armed Bandits. 1027-1036 - Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang:
Learning to Propagate for Graph Meta-Learning. 1037-1048 - Sepehr Assadi, Eric Balkanski, Renato Paes Leme:
Secretary Ranking with Minimal Inversions. 1049-1061 - Siqi Liu, Milos Hauskrecht:
Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes. 1062-1072 - Chi Zhang, Baoxiong Jia, Feng Gao, Yixin Zhu, Hongjing Lu, Song-Chun Zhu:
Learning Perceptual Inference by Contrasting. 1073-1085 - Yu-Chia Chen, Marina Meila:
Selecting the independent coordinates of manifolds with large aspect ratios. 1086-1095 - Zhengyang Shen, François-Xavier Vialard, Marc Niethammer:
Region-specific Diffeomorphic Metric Mapping. 1096-1106 - Chengguang Xu, Ehsan Elhamifar:
Deep Supervised Summarization: Algorithm and Application to Learning Instructions. 1107-1118 - Vincent Sitzmann, Michael Zollhöfer, Gordon Wetzstein:
Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations. 1119-1130 - Brett Daley, Christopher Amato:
Reconciling λ-Returns with Experience Replay. 1131-1140 - Fengxiang He, Tongliang Liu, Dacheng Tao:
Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence. 1141-1150 - Max Simchowitz, Kevin G. Jamieson:
Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs. 1151-1160 - Mitsuru Kusumoto, Takuya Inoue, Gentaro Watanabe, Takuya Akiba, Masanori Koyama:
A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation. 1161-1170 - Paul Hongsuck Seo, Geeho Kim, Bohyung Han:
Combinatorial Inference against Label Noise. 1171-1181 - Chao Qu, Shie Mannor, Huan Xu, Yuan Qi, Le Song, Junwu Xiong:
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning. 1182-1191 - Shuang Wu, Guanrui Wang, Pei Tang, Feng Chen, Luping Shi:
Convolution with even-sized kernels and symmetric padding. 1192-1203 - Dong Liu, Haochen Zhang, Zhiwei Xiong:
On The Classification-Distortion-Perception Tradeoff. 1204-1213 - Dominic Richards, Patrick Rebeschini:
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up. 1214-1225 - Holden Lee, Oren Mangoubi, Nisheeth K. Vishnoi:
Online sampling from log-concave distributions. 1226-1237 - Maria-Florina Balcan, Travis Dick, Ritesh Noothigattu, Ariel D. Procaccia:
Envy-Free Classification. 1238-1248 - Jack Serrino, Max Kleiman-Weiner, David C. Parkes, Josh Tenenbaum:
Finding Friend and Foe in Multi-Agent Games. 1249-1259 - Shibani Santurkar, Andrew Ilyas, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry:
Image Synthesis with a Single (Robust) Classifier. 1260-1271 - Shupeng Gui, Haotao Wang, Haichuan Yang, Chen Yu, Zhangyang Wang, Ji Liu:
Model Compression with Adversarial Robustness: A Unified Optimization Framework. 1283-1294 - Jianwei Yang, Zhile Ren, Chuang Gan, Hongyuan Zhu, Devi Parikh:
Cross-channel Communication Networks. 1295-1304 - Brandon Yang, Gabriel Bender, Quoc V. Le, Jiquan Ngiam:
CondConv: Conditionally Parameterized Convolutions for Efficient Inference. 1305-1316 - Danfei Xu, Roberto Martín-Martín, De-An Huang, Yuke Zhu, Silvio Savarese, Li Fei-Fei:
Regression Planning Networks. 1317-1327 - Mingming Gong, Yanwu Xu, Chunyuan Li, Kun Zhang, Kayhan Batmanghelich:
Twin Auxilary Classifiers GAN. 1328-1337 - Carl Yang, Peiye Zhuang, Wenhan Shi, Alan Luu, Pan Li:
Conditional Structure Generation through Graph Variational Generative Adversarial Nets. 1338-1349 - Chen Tessler, Guy Tennenholtz, Shie Mannor:
Distributional Policy Optimization: An Alternative Approach for Continuous Control. 1350-1360 - Edith Cohen, Ofir Geri:
Sampling Sketches for Concave Sublinear Functions of Frequencies. 1361-1371 - Pei Wang, Nuno Vasconcelos:
Deliberative Explanations: visualizing network insecurities. 1372-1383 - Eugène Ndiaye, Ichiro Takeuchi:
Computing Full Conformal Prediction Set with Approximate Homotopy. 1384-1393 - Stephan Rabanser, Stephan Günnemann, Zachary C. Lipton:
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift. 1394-1406 - Siyuan Li, Rui Wang, Minxue Tang, Chongjie Zhang:
Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards. 1407-1417 - Minne Li, Lisheng Wu, Jun Wang, Haitham Bou-Ammar:
Multi-View Reinforcement Learning. 1418-1429 - Thang Vu, Hyunjun Jang, Trung X. Pham, Chang Dong Yoo:
Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution. 1430-1440 - Jian Sun, Zongben Xu:
Neural Diffusion Distance for Image Segmentation. 1441-1451 - Mete Ozay:
Fine-grained Optimization of Deep Neural Networks. 1452-1462 - Magauiya Zhussip, Shakarim Soltanayev, Se Young Chun:
Extending Stein's unbiased risk estimator to train deep denoisers with correlated pairs of noisy images. 1463-1473 - Chris Russell, Matteo Toso, Neill D. F. Campbell:
Fixing Implicit Derivatives: Trust-Region Based Learning of Continuous Energy Functions. 1474-1484 - Pascal Mettes, Elise van der Pol, Cees Snoek:
Hyperspherical Prototype Networks. 1485-1495 - Ivan Glasser, Ryan Sweke, Nicola Pancotti, Jens Eisert, J. Ignacio Cirac:
Expressive power of tensor-network factorizations for probabilistic modeling. 1496-1508 - Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, Partha P. Talukdar:
HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs. 1509-1520 - Zhize Li:
SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points. 1521-1531 - Pan Zhou, Xiaotong Yuan, Huan Xu, Shuicheng Yan, Jiashi Feng:
Efficient Meta Learning via Minibatch Proximal Update. 1532-1542 - Antoine Wehenkel, Gilles Louppe:
Unconstrained Monotonic Neural Networks. 1543-1553 - Chundi Liu, Guang Wei Yu, Maksims Volkovs, Cheng Chang, Himanshu Rai, Junwei Ma, Satya Krishna Gorti:
Guided Similarity Separation for Image Retrieval. 1554-1564 - Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Aréchiga, Tengyu Ma:
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss. 1565-1576 - Yuan Deng, Jon Schneider, Balasubramanian Sivan:
Strategizing against No-regret Learners. 1577-1585 - Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chen:
D-VAE: A Variational Autoencoder for Directed Acyclic Graphs. 1586-1598 - Mikhail Yurochkin, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, Justin M. Solomon:
Hierarchical Optimal Transport for Document Representation. 1599-1609 - Rui Li:
Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes. 1610-1619 - Boyi Li, Felix Wu, Kilian Q. Weinberger, Serge J. Belongie:
Positional Normalization. 1620-1632 - Shengyuan Hu, Tao Yu, Chuan Guo, Wei-Lun Chao, Kilian Q. Weinberger:
A New Defense Against Adversarial Images: Turning a Weakness into a Strength. 1633-1644 - Xiangyu Xu, Li Siyao, Wenxiu Sun, Qian Yin, Ming-Hsuan Yang:
Quadratic Video Interpolation. 1645-1654 - Bao Wang, Zuoqiang Shi, Stanley J. Osher:
ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies. 1655-1665 - Benjamin Planche, Xuejian Rong, Ziyan Wu, Srikrishna Karanam, Harald Kosch, Yingli Tian, Jan Ernst, Andreas Hutter:
Incremental Scene Synthesis. 1666-1676 - Shikun Liu, Andrew J. Davison, Edward Johns:
Self-Supervised Generalisation with Meta Auxiliary Learning. 1677-1687 - Zongsheng Yue, Hongwei Yong, Qian Zhao, Deyu Meng, Lei Zhang:
Variational Denoising Network: Toward Blind Noise Modeling and Removal. 1688-1699 - Yasutoshi Ida, Yasuhiro Fujiwara, Hisashi Kashima:
Fast Sparse Group Lasso. 1700-1708 - Lin Song, Yanwei Li, Zeming Li, Gang Yu, Hongbin Sun, Jian Sun, Nanning Zheng:
Learnable Tree Filter for Structure-preserving Feature Transform. 1709-1719 - Yuki Yoshida, Masato Okada:
Data-Dependence of Plateau Phenomenon in Learning with Neural Network - Statistical Mechanical Analysis. 1720-1728 - Talfan Evans, Neil Burgess:
Coordinated hippocampal-entorhinal replay as structural inference. 1729-1741 - Hao Zheng, Faming Fang, Guixu Zhang:
Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction. 1742-1752 - Aaron Defazio, Léon Bottou:
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning. 1753-1763 - Aaron Defazio:
On the Curved Geometry of Accelerated Optimization. 1764-1773 - Jiezhang Cao, Langyuan Mo, Yifan Zhang, Kui Jia, Chunhua Shen, Mingkui Tan:
Multi-marginal Wasserstein GAN. 1774-1784 - Kamil Ciosek, Quan Vuong, Robert Tyler Loftin, Katja Hofmann:
Better Exploration with Optimistic Actor Critic. 1785-1796 - Matthew Schlegel, Wesley Chung, Daniel Graves, Jian Qian, Martha White:
Importance Resampling for Off-policy Prediction. 1797-1807 - Songbai Yan, Kamalika Chaudhuri, Tara Javidi:
The Label Complexity of Active Learning from Observational Data. 1808-1817 - Khurram Javed, Martha White:
Meta-Learning Representations for Continual Learning. 1818-1828 - Haichao Zhang, Jianyu Wang:
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training. 1829-1839 - Scott Gigante, Adam S. Charles, Smita Krishnaswamy, Gal Mishne:
Visualizing the PHATE of Neural Networks. 1840-1851 - Alex X. Lu, Amy X. Lu, Wiebke Schormann, Marzyeh Ghassemi, David W. Andrews, Alan M. Moses:
The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers. 1852-1860 - Changxiao Cai, Gen Li, H. Vincent Poor, Yuxin Chen:
Nonconvex Low-Rank Tensor Completion from Noisy Data. 1861-1872 - Gautam Goel, Yiheng Lin, Haoyuan Sun, Adam Wierman:
Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization. 1873-1883 - Weizhe Hua, Yuan Zhou, Christopher De Sa, Zhiru Zhang, G. Edward Suh:
Channel Gating Neural Networks. 1884-1894 - Guruprasad Raghavan, Matt Thomson:
Neural networks grown and self-organized by noise. 1895-1905 - Xinyang Chen, Sinan Wang, Bo Fu, Mingsheng Long, Jianmin Wang:
Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning. 1906-1916 - Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, Deyu Meng:
Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting. 1917-1928 - Fuhai Chen, Rongrong Ji, Jiayi Ji, Xiaoshuai Sun, Baochang Zhang, Xuri Ge, Yongjian Wu, Feiyue Huang, Yan Wang:
Variational Structured Semantic Inference for Diverse Image Captioning. 1929-1939 - Zhiao Huang, Fangchen Liu, Hao Su:
Mapping State Space using Landmarks for Universal Goal Reaching. 1940-1950 - Ximei Wang, Ying Jin, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Transferable Normalization: Towards Improving Transferability of Deep Neural Networks. 1951-1961 - Giacomo De Palma, Bobak Toussi Kiani, Seth Lloyd:
Random deep neural networks are biased towards simple functions. 1962-1974 - Niv Nayman, Asaf Noy, Tal Ridnik, Itamar Friedman, Rong Jin, Lihi Zelnik-Manor:
XNAS: Neural Architecture Search with Expert Advice. 1975-1985 - Wei-Da Chen, Shan-Hung Wu:
CNN2: Viewpoint Generalization via a Binocular Vision. 1986-1998 - Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson:
Generalized Off-Policy Actor-Critic. 1999-2009 - Shangtong Zhang, Shimon Whiteson:
DAC: The Double Actor-Critic Architecture for Learning Options. 2010-2020 - Tao Yu, Christopher De Sa:
Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models. 2021-2031 - Matan Atzmon, Niv Haim, Lior Yariv, Ofer Israelov, Haggai Maron, Yaron Lipman:
Controlling Neural Level Sets. 2032-2041 - Cyrille W. Combettes, Sebastian Pokutta:
Blended Matching Pursuit. 2042-2052 - Difan Zou, Quanquan Gu:
An Improved Analysis of Training Over-parameterized Deep Neural Networks. 2053-2062 - Bowen Li, Xiaojuan Qi, Thomas Lukasiewicz, Philip H. S. Torr:
Controllable Text-to-Image Generation. 2063-2073 - Wenlin Wang, Chenyang Tao, Zhe Gan, Guoyin Wang, Liqun Chen, Xinyuan Zhang, Ruiyi Zhang, Qian Yang, Ricardo Henao, Lawrence Carin:
Improving Textual Network Learning with Variational Homophilic Embeddings. 2074-2085 - Peilin Zhong, Yuchen Mo, Chang Xiao, Pengyu Chen, Changxi Zheng:
Rethinking Generative Mode Coverage: A Pointwise Guaranteed Approach. 2086-2097 - Ruoqi Shen, Yin Tat Lee:
The Randomized Midpoint Method for Log-Concave Sampling. 2098-2109 - Su Young Lee, Sung-Ik Choi, Sae-Young Chung:
Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update. 2110-2119 - Takahiro Omi, Naonori Ueda, Kazuyuki Aihara:
Fully Neural Network based Model for General Temporal Point Processes. 2120-2129 - Zhonghui You, Kun Yan, Jinmian Ye, Meng Ma, Ping Wang:
Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks. 2130-2141 - Faidra Georgia Monachou, Itai Ashlagi:
Discrimination in Online Markets: Effects of Social Bias on Learning from Reviews and Policy Design. 2142-2152 - Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, Yaron Lipman:
Provably Powerful Graph Networks. 2153-2164 - Arsalan Sharif-Nassab, Saber Salehkaleybar, S. Jamaloddin Golestani:
Order Optimal One-Shot Distributed Learning. 2165-2174 - Jie Hu, Rongrong Ji, Shengchuan Zhang, Xiaoshuai Sun, Qixiang Ye, Chia-Wen Lin, Qi Tian:
Information Competing Process for Learning Diversified Representations. 2175-2186 - Sören Laue, Matthias Mitterreiter, Joachim Giesen:
GENO - GENeric Optimization for Classical Machine Learning. 2187-2198 - Alexis Bellot, Mihaela van der Schaar:
Conditional Independence Testing using Generative Adversarial Networks. 2199-2208 - Aviv Rosenberg, Yishay Mansour:
Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function. 2209-2218 - Xiangyu Zheng, Song Xi Chen:
Partitioning Structure Learning for Segmented Linear Regression Trees. 2219-2228 - Xindian Ma, Peng Zhang, Shuai Zhang, Nan Duan, Yuexian Hou, Ming Zhou, Dawei Song:
A Tensorized Transformer for Language Modeling. 2229-2239 - Jen Ning Lim, Makoto Yamada, Bernhard Schölkopf, Wittawat Jitkrittum:
Kernel Stein Tests for Multiple Model Comparison. 2240-2250 - Amir Dezfouli, Hassan Ashtiani, Omar Ghattas, Richard Nock, Peter Dayan, Cheng Soon Ong:
Disentangled behavioural representations. 2251-2260 - Quanfu Fan, Chun-Fu (Richard) Chen, Hilde Kuehne, Marco Pistoia, David D. Cox:
More Is Less: Learning Efficient Video Representations by Big-Little Network and Depthwise Temporal Aggregation. 2261-2270 - Dror Simon, Michael Elad:
Rethinking the CSC Model for Natural Images. 2271-2281 - Weishi Shi, Qi Yu:
Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning. 2282-2291 - Deepak Pathak, Christopher Lu, Trevor Darrell, Phillip Isola, Alexei A. Efros:
Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity. 2292-2302 - Kristof Meding, Dominik Janzing, Bernhard Schölkopf, Felix A. Wichmann:
Perceiving the arrow of time in autoregressive motion. 2303-2314 - Ofir Nachum, Yinlam Chow, Bo Dai, Lihong Li:
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections. 2315-2325 - Eliya Nachmani, Lior Wolf:
Hyper-Graph-Network Decoders for Block Codes. 2326-2336 - Adrian Rivera Cardoso, He Wang, Huan Xu:
Large Scale Markov Decision Processes with Changing Rewards. 2337-2347 - Srinath Sridhar, Davis Rempe, Julien Valentin, Sofien Bouaziz, Leonidas J. Guibas:
Multiview Aggregation for Learning Category-Specific Shape Reconstruction. 2348-2359 - Virag Shah, Ramesh Johari, Jose H. Blanchet:
Semi-Parametric Dynamic Contextual Pricing. 2360-2370 - Alan Kuhnle:
Interlaced Greedy Algorithm for Maximization of Submodular Functions in Nearly Linear Time. 2371-2381 - Rebekka Burkholz, Alina Dubatovka:
Initialization of ReLUs for Dynamical Isometry. 2382-2392 - Jie Ding, A. Robert Calderbank, Vahid Tarokh:
Gradient Information for Representation and Modeling. 2393-2402 - Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, Vahid Tarokh:
SpiderBoost and Momentum: Faster Variance Reduction Algorithms. 2403-2413 - Xiyang Liu, Sewoong Oh:
Minimax Optimal Estimation of Approximate Differential Privacy on Neighboring Databases. 2414-2425 - Ayan Chakrabarti, Benjamin Moseley:
Backprop with Approximate Activations for Memory-efficient Network Training. 2426-2435 - Zhihao Xia, Ayan Chakrabarti:
Training Image Estimators without Image Ground Truth. 2436-2446 - Lisha Chen, Hui Su, Qiang Ji:
Deep Structured Prediction for Facial Landmark Detection. 2447-2457 - Xiuyuan Lu, Benjamin Van Roy:
Information-Theoretic Confidence Bounds for Reinforcement Learning. 2458-2466 - Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara:
Transfer Anomaly Detection by Inferring Latent Domain Representations. 2467-2477 - Huaian Diao, Zhao Song, David P. Woodruff, Xin Yang:
Total Least Squares Regression in Input Sparsity Time. 2478-2489 - Hongzi Mao, Parimarjan Negi, Akshay Narayan, Hanrui Wang, Jiacheng Yang, Haonan Wang, Ryan Marcus, Ravichandra Addanki, Mehrdad Khani Shirkoohi, Songtao He, Vikram Nathan, Frank Cangialosi, Shaileshh Bojja Venkatakrishnan, Wei-Hung Weng, Song Han, Tim Kraska, Mohammad Alizadeh:
Park: An Open Platform for Learning-Augmented Computer Systems. 2490-2502 - Claudia Shi, David M. Blei, Victor Veitch:
Adapting Neural Networks for the Estimation of Treatment Effects. 2503-2513 - Hanjun Dai, Yujia Li, Chenglong Wang, Rishabh Singh, Po-Sen Huang, Pushmeet Kohli:
Learning Transferable Graph Exploration. 2514-2525 - Ryan J. Tibshirani, Rina Foygel Barber, Emmanuel J. Candès, Aaditya Ramdas:
Conformal Prediction Under Covariate Shift. 2526-2536 - Chen Dan, Hong Wang, Hongyang Zhang, Yuchen Zhou, Pradeep Ravikumar:
Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation. 2537-2548 - Haowei He, Gao Huang, Yang Yuan:
Asymmetric Valleys: Beyond Sharp and Flat Local Minima. 2549-2560 - Yixing Xu, Yunhe Wang, Hanting Chen, Kai Han, Chunjing Xu, Dacheng Tao, Chang Xu:
Positive-Unlabeled Compression on the Cloud. 2561-2570 - Boxin Zhao, Y. Samuel Wang, Mladen Kolar:
Direct Estimation of Differential Functional Graphical Models. 2571-2581 - Chenri Ni, Nontawat Charoenphakdee, Junya Honda, Masashi Sugiyama:
On the Calibration of Multiclass Classification with Rejection. 2582-2592 - Pratyusha Sharma, Deepak Pathak, Abhinav Gupta:
Third-Person Visual Imitation Learning via Decoupled Hierarchical Controller. 2593-2603 - Zhuoning Yuan, Yan Yan, Rong Jin, Tianbao Yang:
Stagewise Training Accelerates Convergence of Testing Error Over SGD. 2604-2614 - Takuya Hiraoka, Takahisa Imagawa, Tatsuya Mori, Takashi Onishi, Yoshimasa Tsuruoka:
Learning Robust Options by Conditional Value at Risk Optimization. 2615-2625 - Yi Xu, Rong Jin, Tianbao Yang:
Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems. 2626-2636 - Lili Su, Pengkun Yang:
On Learning Over-parameterized Neural Networks: A Functional Approximation Perspective. 2637-2646 - Fuwen Tan, Paola Cascante-Bonilla, Xiaoxiao Guo, Hui Wu, Song Feng, Vicente Ordonez:
Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries. 2647-2657 - Chaoyou Fu, Xiang Wu, Yibo Hu, Huaibo Huang, Ran He:
Dual Variational Generation for Low Shot Heterogeneous Face Recognition. 2670-2679 - Mitchell Wortsman, Ali Farhadi, Mohammad Rastegari:
Discovering Neural Wirings. 2680-2690 - Baekjin Kim, Ambuj Tewari:
On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems. 2691-2700 - Jaemin Yoo, Minyong Cho, Taebum Kim, U Kang:
Knowledge Extraction with No Observable Data. 2701-2710 - Matthew J. Holland:
PAC-Bayes under potentially heavy tails. 2711-2720 - Ting-I Hsieh, Yi-Chen Lo, Hwann-Tzong Chen, Tyng-Luh Liu:
One-Shot Object Detection with Co-Attention and Co-Excitation. 2721-2730 - Shuai Zhang, Yi Tay, Lina Yao, Qi Liu:
Quaternion Knowledge Graph Embeddings. 2731-2741 - Yuxian Meng, Wei Wu, Fei Wang, Xiaoya Li, Ping Nie, Fan Yin, Muyu Li, Qinghong Han, Xiaofei Sun, Jiwei Li:
Glyce: Glyph-vectors for Chinese Character Representations. 2742-2753 - Yihan Jiang, Hyeji Kim, Himanshu Asnani, Sreeram Kannan, Sewoong Oh, Pramod Viswanath:
Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels. 2754-2764 - Weijiang Yu, Jingwen Zhou, Weihao Yu, Xiaodan Liang, Nong Xiao:
Heterogeneous Graph Learning for Visual Commonsense Reasoning. 2765-2775 - Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht:
Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning. 2776-2787 - Sascha Saralajew, Lars Holdijk, Maike Rees, Ebubekir Asan, Thomas Villmann:
Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components. 2788-2799 - Santtu Tikka, Antti Hyttinen, Juha Karvanen:
Identifying Causal Effects via Context-specific Independence Relations. 2800-2810 - Wang-Zhou Dai, Qiu-Ling Xu, Yang Yu, Zhi-Hua Zhou:
Bridging Machine Learning and Logical Reasoning by Abductive Learning. 2811-2822 - Zihan Zhang, Xiangyang Ji:
Regret Minimization for Reinforcement Learning by Evaluating the Optimal Bias Function. 2823-2832 - Belhal Karimi, Hoi-To Wai, Eric Moulines, Marc Lavielle:
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods. 2833-2843 - Sulaiman A. Alghunaim, Kun Yuan, Ali H. Sayed:
A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization. 2844-2854 - Rinu Boney, Norman Di Palo, Mathias Berglund, Alexander Ilin, Juho Kannala, Antti Rasmus, Harri Valpola:
Regularizing Trajectory Optimization with Denoising Autoencoders. 2855-2865 - Alexej Klushyn, Nutan Chen, Richard Kurle, Botond Cseke, Patrick van der Smagt:
Learning Hierarchical Priors in VAEs. 2866-2875 - Sivan Sabato:
Epsilon-Best-Arm Identification in Pay-Per-Reward Multi-Armed Bandits. 2876-2886 - Matteo Turchetta, Felix Berkenkamp, Andreas Krause:
Safe Exploration for Interactive Machine Learning. 2887-2897 - Charles Corbière, Nicolas Thome, Avner Bar-Hen, Matthieu Cord, Patrick Pérez:
Addressing Failure Prediction by Learning Model Confidence. 2898-2909 - ChangYong Oh, Jakub M. Tomczak, Efstratios Gavves, Max Welling:
Combinatorial Bayesian Optimization using the Graph Cartesian Product. 2910-2920 - Juyeon Heo, Sunghwan Joo, Taesup Moon:
Fooling Neural Network Interpretations via Adversarial Model Manipulation. 2921-2932 - Lénaïc Chizat, Edouard Oyallon, Francis R. Bach:
On Lazy Training in Differentiable Programming. 2933-2943 - Parimala Kancharla, Sumohana S. Channappayya:
Quality Aware Generative Adversarial Networks. 2944-2954 - Marcel Hirt, Petros Dellaportas, Alain Durmus:
Copula-like Variational Inference. 2955-2967 - Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini:
Implicit Regularization for Optimal Sparse Recovery. 2968-2979 - Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Zhiwei Steven Wu:
Locally Private Gaussian Estimation. 2980-2989 - Xiaoming Yu, Yuanqi Chen, Shan Liu, Thomas H. Li, Ge Li:
Multi-mapping Image-to-Image Translation via Learning Disentanglement. 2990-2999 - Yusuke Tanaka, Toshiyuki Tanaka, Tomoharu Iwata, Takeshi Kurashima, Maya Okawa, Yasunori Akagi, Hiroyuki Toda:
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs. 3000-3010 - Zhiqing Sun, Zhuohan Li, Haoqing Wang, Di He, Zi Lin, Zhi-Hong Deng:
Fast Structured Decoding for Sequence Models. 3011-3020 - Gedas Bertasius, Christoph Feichtenhofer, Du Tran, Jianbo Shi, Lorenzo Torresani:
Learning Temporal Pose Estimation from Sparsely-Labeled Videos. 3021-3032 - Sindy Löwe, Peter O'Connor, Bastiaan S. Veeling:
Putting An End to End-to-End: Gradient-Isolated Learning of Representations. 3033-3045 - Hongteng Xu, Dixin Luo, Lawrence Carin:
Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching. 3046-3056 - Satoshi Tsutsui, Yanwei Fu, David J. Crandall:
Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition. 3057-3066 - Simon Ramstedt, Chris Pal:
Real-Time Reinforcement Learning. 3067-3076 - Alexander Peysakhovich, Christian Kroer, Adam Lerer:
Robust Multi-agent Counterfactual Prediction. 3077-3087 - Mohammad Emtiyaz Khan, Alexander Immer, Ehsan Abedi, Maciej Korzepa:
Approximate Inference Turns Deep Networks into Gaussian Processes. 3088-3098 - Patrick Kidger, Patric Bonnier, Imanol Pérez Arribas, Cristopher Salvi, Terry J. Lyons:
Deep Signature Transforms. 3099-3109 - Yogev Bar-On, Yishay Mansour:
Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits. 3110-3120 - Ming Yu, Zhuoran Yang, Mladen Kolar, Zhaoran Wang:
Convergent Policy Optimization for Safe Reinforcement Learning. 3121-3133 - Emilien Dupont, Arnaud Doucet, Yee Whye Teh:
Augmented Neural ODEs. 3134-3144 - Min-hwan Oh, Garud Iyengar:
Thompson Sampling for Multinomial Logit Contextual Bandits. 3145-3155 - Wei Wang, Zheng Dang, Yinlin Hu, Pascal Fua, Mathieu Salzmann:
Backpropagation-Friendly Eigendecomposition. 3156-3164 - Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu:
FastSpeech: Fast, Robust and Controllable Text to Speech. 3165-3174 - Giovanni Chierchia, Benjamin Perret:
Ultrametric Fitting by Gradient Descent. 3175-3186 - Hanrui Zhang, Yu Cheng, Vincent Conitzer:
Distinguishing Distributions When Samples Are Strategically Transformed. 3187-3195 - Gauthier Gidel, Francis R. Bach, Simon Lacoste-Julien:
Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks. 3196-3206 - Yan Zhang, Jonathon S. Hare, Adam Prügel-Bennett:
Deep Set Prediction Networks. 3207-3217 - Zelda E. Mariet, Yaniv Ovadia, Jasper Snoek:
DppNet: Approximating Determinantal Point Processes with Deep Networks. 3218-3229 - Sai Qian Zhang, Qi Zhang, Jieyu Lin:
Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control. 3230-3239 - Ya-Chien Chang, Nima Roohi, Sicun Gao:
Neural Lyapunov Control. 3240-3249 - Vincent Cohen-Addad, Niklas Hjuler, Nikos Parotsidis, David Saulpic, Chris Schwiegelshohn:
Fully Dynamic Consistent Facility Location. 3250-3260 - Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, Samuel R. Bowman:
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems. 3261-3275 - Jiaqi Ma, Weijing Tang, Ji Zhu, Qiaozhu Mei:
A Flexible Generative Framework for Graph-based Semi-supervised Learning. 3276-3285 - Georgios Detorakis, Sourav Dutta, Abhishek Khanna, Matthew Jerry, Suman Datta, Emre Neftci:
Inherent Weight Normalization in Stochastic Neural Networks. 3286-3297 - Su Jia, Viswanath Nagarajan, Fatemeh Navidi, R. Ravi:
Optimal Decision Tree with Noisy Outcomes. 3298-3308 - Eunbyung Park, Junier B. Oliva:
Meta-Curvature. 3309-3319 - Nathan Kallus, Masatoshi Uehara:
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning. 3320-3329 - Zhen Zhang, Yijian Xiang, Lingfei Wu, Bing Xue, Arye Nehorai:
KerGM: Kernelized Graph Matching. 3330-3341 - Maithra Raghu, Chiyuan Zhang, Jon M. Kleinberg, Samy Bengio:
Transfusion: Understanding Transfer Learning for Medical Imaging. 3342-3352 - Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John P. Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein:
Adversarial training for free! 3353-3364 - Jun Sun, Tianyi Chen, Georgios B. Giannakis, Zaiyue Yang:
Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients. 3365-3375 - Vaishak Belle, Brendan Juba:
Implicitly learning to reason in first-order logic. 3376-3386 - Kevin J. Liang, Guoyin Wang, Yitong Li, Ricardo Henao, Lawrence Carin:
Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods. 3387-3398 - Yongkai Wu, Lu Zhang, Xintao Wu, Hanghang Tong:
PC-Fairness: A Unified Framework for Measuring Causality-based Fairness. 3399-3409 - Jianchun Chen, Lingjing Wang, Xiang Li, Yi Fang:
Arbicon-Net: Arbitrary Continuous Geometric Transformation Networks for Image Registration. 3410-3420 - Nathan Kallus, Angela Zhou:
Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds. 3421-3432 - Nathan Kallus, Angela Zhou:
The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric. 3433-3443 - Sharon Zhou, Mitchell L. Gordon, Ranjay Krishna, Austin Narcomey, Li Fei-Fei, Michael S. Bernstein:
HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models. 3444-3456 - Pierre C. Bellec, Arun K. Kuchibhotla:
First order expansion of convex regularized estimators. 3457-3468 - Kamalika Chaudhuri, Jacob Imola, Ashwin Machanavajjhala:
Capacity Bounded Differential Privacy. 3469-3478 - Trevor Campbell, Xinglong Li:
Universal Boosting Variational Inference. 3479-3490 - Preetum Nakkiran, Gal Kaplun, Dimitris Kalimeris, Tristan Yang, Benjamin L. Edelman, Fred Zhang, Boaz Barak:
SGD on Neural Networks Learns Functions of Increasing Complexity. 3491-3501 - Shuang Li, Gongguo Tang, Michael B. Wakin:
The Landscape of Non-convex Empirical Risk with Degenerate Population Risk. 3502-3512 - Antonio Ginart, Melody Y. Guan, Gregory Valiant, James Zou:
Making AI Forget You: Data Deletion in Machine Learning. 3513-3526 - David Durfee, Ryan M. Rogers:
Practical Differentially Private Top-k Selection with Pay-what-you-get Composition. 3527-3537 - Yaniv Romano, Evan Patterson, Emmanuel J. Candès:
Conformalized Quantile Regression. 3538-3548 - Seungki Min, Costis Maglaras, Ciamac C. Moallemi:
Thompson Sampling with Information Relaxation Penalties. 3549-3558 - Andrew Bennett, Nathan Kallus, Tobias Schnabel:
Deep Generalized Method of Moments for Instrumental Variable Analysis. 3559-3569 - Benjamin J. Lengerich, Bryon Aragam, Eric P. Xing:
Learning Sample-Specific Models with Low-Rank Personalized Regression. 3570-3580 - Hsin-Ying Lee, Xiaodong Yang, Ming-Yu Liu, Ting-Chun Wang, Yu-Ding Lu, Ming-Hsuan Yang, Jan Kautz:
Dancing to Music. 3581-3591 - Hattie Zhou, Janice Lan, Rosanne Liu, Jason Yosinski:
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask. 3592-3602 - Yilun Du, Igor Mordatch:
Implicit Generation and Modeling with Energy Based Models. 3603-3613 - Janice Lan, Rosanne Liu, Hattie Zhou, Jason Yosinski:
LCA: Loss Change Allocation for Neural Network Training. 3614-3624 - Christopher Thomas, Adriana Kovashka:
Predicting the Politics of an Image Using Webly Supervised Data. 3625-3637 - Lingyu Liang, Lianwen Jin, Yong Xu:
Adaptive GNN for Image Analysis and Editing. 3638-3649 - Tavor Z. Baharav, David Tse:
Ultra Fast Medoid Identification via Correlated Sequential Halving. 3650-3659 - Phuong Ha Nguyen, Lam M. Nguyen, Marten van Dijk:
Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD. 3660-3669 - Edgar Dobriban, Sifan Liu:
Asymptotics for Sketching in Least Squares Regression. 3670-3680 - Xue Bin Peng, Michael Chang, Grace Zhang, Pieter Abbeel, Sergey Levine:
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies. 3681-3692 - Kevin Bello, Jean Honorio:
Exact inference in structured prediction. 3693-3702 - Cheng Fu, Huili Chen, Haolan Liu, Xinyun Chen, Yuandong Tian, Farinaz Koushanfar, Jishen Zhao:
Coda: An End-to-End Neural Program Decompiler. 3703-3714 - Gunpil Hwang, Seohyeon Kim, Hyeon-Min Bae:
Bat-G net: Bat-inspired High-Resolution 3D Image Reconstruction using Ultrasonic Echoes. 3715-3726 - Sharan Vaswani, Aaron Mishkin, Issam H. Laradji, Mark Schmidt, Gauthier Gidel, Simon Lacoste-Julien:
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates. 3727-3740 - Dominik Linzner, Michael Schmidt, Heinz Koeppl:
Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data. 3741-3751 - Devin Reich, Ariel Todoki, Rafael Dowsley, Martine De Cock, Anderson C. A. Nascimento:
Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation. 3752-3764 - Jonathan R. Ullman, Adam Sealfon:
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy. 3765-3775 - Qianli Ma, Jiawei Zheng, Sen Li, Gary W. Cottrell:
Learning Representations for Time Series Clustering. 3776-3786 - Ananya Kumar, Percy Liang, Tengyu Ma:
Verified Uncertainty Calibration. 3787-3798 - Wenhao Zhang, Si Wu, Brent Doiron, Tai Sing Lee:
A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits. 3799-3808 - Yunji Kim, Seonghyeon Nam, In Cho, Seon Joo Kim:
Unsupervised Keypoint Learning for Guiding Class-Conditional Video Prediction. 3809-3819 - Yiwen Guo, Ziang Yan, Changshui Zhang:
Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks. 3820-3829 - Difan Zou, Pan Xu, Quanquan Gu:
Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction. 3830-3841 - Qitian Wu, Zixuan Zhang, Xiaofeng Gao, Junchi Yan, Guihai Chen:
Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling. 3842-3851 - Xing Yan, Qi Wu, Wen Zhang:
Cross-sectional Learning of Extremal Dependence among Financial Assets. 3852-3862 - Yujia Jin, Aaron Sidford:
Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG. 3863-3873 - Jonathan Ho, Evan Lohn, Pieter Abbeel:
Compression with Flows via Local Bits-Back Coding. 3874-3883 - Rob Brekelmans, Daniel Moyer, Aram Galstyan, Greg Ver Steeg:
Exact Rate-Distortion in Autoencoders via Echo Noise. 3884-3895 - Qianqian Xu, Xinwei Sun, Zhiyong Yang, Xiaochun Cao, Qingming Huang, Yuan Yao:
iSplit LBI: Individualized Partial Ranking with Ties via Split LBI. 3896-3906 - Aleksis Pirinen, Erik Gärtner, Cristian Sminchisescu:
Domes to Drones: Self-Supervised Active Triangulation for 3D Human Pose Reconstruction. 3907-3917 - Shangyu Chen, Wenya Wang, Sinno Jialin Pan:
MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization. 3918-3928 - Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, Timo Aila:
Improved Precision and Recall Metric for Assessing Generative Models. 3929-3938 - Jiajin Li, Sen Huang, Anthony Man-Cho So:
A First-Order Algorithmic Framework for Distributionally Robust Logistic Regression. 3939-3949 - Yikang Li, Tao Ma, Yeqi Bai, Nan Duan, Sining Wei, Xiaogang Wang:
PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph. 3950-3960 - Quentin Bertrand, Mathurin Massias, Alexandre Gramfort, Joseph Salmon:
Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso. 3961-3972 - Han Zhu, Daqing Chang, Ziru Xu, Pengye Zhang, Xiang Li, Jie He, Han Li, Jian Xu, Kun Gai:
Joint Optimization of Tree-based Index and Deep Model for Recommender Systems. 3973-3982 - Ravichandra Addanki, Shaileshh Bojja Venkatakrishnan, Shreyan Gupta, Hongzi Mao, Mohammad Alizadeh:
Learning Generalizable Device Placement Algorithms for Distributed Machine Learning. 3983-3993 - Liyuan Xu, Junya Honda, Gang Niu, Masashi Sugiyama:
Uncoupled Regression from Pairwise Comparison Data. 3994-4004 - Ruibing Hou, Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen:
Cross Attention Network for Few-shot Classification. 4005-4016 - Qing Qu, Xiao Li, Zhihui Zhu:
A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution. 4017-4028 - Linfeng Zhang, Zhanhong Tan, Jiebo Song, Jingwei Chen, Chenglong Bao, Kaisheng Ma:
SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models. 4029-4038 - Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay:
Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs. 4039-4049 - Anette Hunziker, Yuxin Chen, Oisin Mac Aodha, Manuel Gomez Rodriguez, Andreas Krause, Pietro Perona, Yisong Yue, Adish Singla:
Teaching Multiple Concepts to a Forgetful Learner. 4050-4060 - Frank Ban, David P. Woodruff, Qiuyi (Richard) Zhang:
Regularized Weighted Low Rank Approximation. 4061-4071 - Paul K. Rubenstein, Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya O. Tolstikhin:
Practical and Consistent Estimation of f-Divergences. 4072-4082 - Ryoma Sato, Makoto Yamada, Hisashi Kashima:
Approximation Ratios of Graph Neural Networks for Combinatorial Problems. 4083-4092 - Tianbo Li, Yiping Ke:
Thinning for Accelerating the Learning of Point Processes. 4093-4103 - Maksim Kuznetsov, Daniil Polykovskiy, Dmitry P. Vetrov, Alexander Zhebrak:
A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models. 4104-4114 - Mikko A. Heikkilä, Joonas Jälkö, Onur Dikmen, Antti Honkela:
Differentially Private Markov Chain Monte Carlo. 4115-4125 - Suraj Srinivas, François Fleuret:
Full-Gradient Representation for Neural Network Visualization. 4126-4135 - Iordanis Kerenidis, Jonas Landman, Alessandro Luongo, Anupam Prakash:
q-means: A quantum algorithm for unsupervised machine learning. 4136-4146 - Sebastian Tschiatschek, Ahana Ghosh, Luis Haug, Rati Devidze, Adish Singla:
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints. 4147-4157 - Frederik Kunstner, Philipp Hennig, Lukas Balles:
Limitations of the empirical Fisher approximation for natural gradient descent. 4158-4169 - Ruho Kondo, Keisuke Kawano, Satoshi Koide, Takuro Kutsuna:
Flow-based Image-to-Image Translation with Feature Disentanglement. 4170-4180 - Alhussein Fawzi, Mateusz Malinowski, Hamza Fawzi, Omar Fawzi:
Learning dynamic polynomial proofs. 4181-4190 - Vincent Le Guen, Nicolas Thome:
Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models. 4191-4203 - Boris Knyazev, Graham W. Taylor, Mohamed R. Amer:
Understanding Attention and Generalization in Graph Neural Networks. 4204-4214 - Satoshi Hara, Atsushi Nitanda, Takanori Maehara:
Data Cleansing for Models Trained with SGD. 4215-4224 - Shuo Chen, Lei Luo, Jian Yang, Chen Gong, Jun Li, Heng Huang:
Curvilinear Distance Metric Learning. 4225-4234 - Yaqi Xie, Ziwei Xu, Kuldeep S. Meel, Mohan S. Kankanhalli, Harold Soh:
Embedding Symbolic Knowledge into Deep Networks. 4235-4245 - Raanan Y. Rohekar, Yaniv Gurwicz, Shami Nisimov, Gal Novik:
Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections. 4246-4256 - Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel:
Efficient Graph Generation with Graph Recurrent Attention Networks. 4257-4267 - Mahesh Chandra Mukkamala, Peter Ochs:
Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms. 4268-4278 - Heliang Zheng, Jianlong Fu, Zheng-Jun Zha, Jiebo Luo:
Learning Deep Bilinear Transformation for Fine-grained Image Representation. 4279-4288 - Kazuki Osawa, Siddharth Swaroop, Mohammad Emtiyaz Khan, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota:
Practical Deep Learning with Bayesian Principles. 4289-4301 - Cyprien de Masson d'Autume, Shakir Mohamed, Mihaela Rosca, Jack W. Rae:
Training Language GANs from Scratch. 4302-4313 - Christopher Nemeth, Fredrik Lindsten, Maurizio Filippone, James Hensman:
Pseudo-Extended Markov chain Monte Carlo. 4314-4324 - James Jordon, Jinsung Yoon, Mihaela van der Schaar:
Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate. 4325-4334 - Alberto Maria Metelli, Amarildo Likmeta, Marcello Restelli:
Propagating Uncertainty in Reinforcement Learning via Wasserstein Barycenters. 4335-4347 - Christopher Beckham, Sina Honari, Vikas Verma, Alex Lamb, Farnoosh Ghadiri, R. Devon Hjelm, Yoshua Bengio, Chris Pal:
On Adversarial Mixup Resynthesis. 4348-4359 - Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle:
A Geometric Perspective on Optimal Representations for Reinforcement Learning. 4360-4371 - Joshua K. Lee, Prasanna Sattigeri, Gregory W. Wornell:
Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks. 4372-4382 - Jingjing Xu, Xu Sun, Zhiyuan Zhang, Guangxiang Zhao, Junyang Lin:
Understanding and Improving Layer Normalization. 4383-4393 - Hongjoon Ahn, Sungmin Cha, Donggyu Lee, Taesup Moon:
Uncertainty-based Continual Learning with Adaptive Regularization. 4394-4404 - Yali Du, Lei Han, Meng Fang, Ji Liu, Tianhong Dai, Dacheng Tao:
LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning. 4405-4416 - Mathias Perslev, Michael Hejselbak Jensen, Sune Darkner, Poul Jørgen Jennum, Christian Igel:
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging. 4417-4428 - Jason M. Altschuler, Francis R. Bach, Alessandro Rudi, Jonathan Niles-Weed:
Massively scalable Sinkhorn distances via the Nyström method. 4429-4439 - Yue Yu, Jiaxiang Wu, Longbo Huang:
Double Quantization for Communication-Efficient Distributed Optimization. 4440-4451 - Bryon Aragam, Arash A. Amini, Qing Zhou:
Globally optimal score-based learning of directed acyclic graphs in high-dimensions. 4452-4464 - Ivana Balazevic, Carl Allen, Timothy M. Hospedales:
Multi-relational Poincaré Graph Embeddings. 4465-4475 - Philip Paquette, Yuchen Lu, Steven Bocco, Max O. Smith, Satya Ortiz-Gagne, Jonathan K. Kummerfeld, Joelle Pineau, Satinder Singh, Aaron C. Courville:
No-Press Diplomacy: Modeling Multi-Agent Gameplay. 4476-4487 - Yaqi Duan, Zheng Tracy Ke, Mengdi Wang:
State Aggregation Learning from Markov Transition Data. 4488-4497 - Charles T. Marx, Richard L. Phillips, Sorelle A. Friedler, Carlos Scheidegger, Suresh Venkatasubramanian:
Disentangling Influence: Using disentangled representations to audit model predictions. 4498-4508 - David Janz, Jiri Hron, Przemyslaw Mazur, Katja Hofmann, José Miguel Hernández-Lobato, Sebastian Tschiatschek:
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning. 4509-4518 - Théo Ryffel, David Pointcheval, Francis R. Bach, Edouard Dufour-Sans, Romain Gay:
Partially Encrypted Deep Learning using Functional Encryption. 4519-4530 - David Martínez-Rubio, Varun Kanade, Patrick Rebeschini:
Decentralized Cooperative Stochastic Bandits. 4531-4542 - Gonzalo Mena, Jonathan Niles-Weed:
Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem. 4543-4553 - Shirin Jalali, Carl J. Nuzman, Iraj Saniee:
Efficient Deep Approximation of GMMs. 4554-4562 - Yifan Sun, Yaqi Duan, Hao Gong, Mengdi Wang:
Learning low-dimensional state embeddings and metastable clusters from time series data. 4563-4572 - Xu Wang, Jingming He, Lin Ma:
Exploiting Local and Global Structure for Point Cloud Semantic Segmentation with Contextual Point Representations. 4573-4583 - Creighton Heaukulani, Mark van der Wilk:
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes. 4584-4594 - Rahul Singh, Maneesh Sahani, Arthur Gretton:
Kernel Instrumental Variable Regression. 4595-4607 - Hugo Caselles-Dupré, Michaël Garcia Ortiz, David Filliat:
Symmetry-Based Disentangled Representation Learning requires Interaction with Environments. 4608-4617 - Supratik Paul, Vitaly Kurin, Shimon Whiteson:
Fast Efficient Hyperparameter Tuning for Policy Gradient Methods. 4618-4628 - Ian Char, Youngseog Chung, Willie Neiswanger, Kirthevasan Kandasamy, Oak Nelson, Mark D. Boyer, Egemen Kolemen:
Offline Contextual Bayesian Optimization. 4629-4640 - Candice Schumann, Zhi Lang, Jeffrey S. Foster, John P. Dickerson:
Making the Cut: A Bandit-based Approach to Tiered Interviewing. 4641-4651 - Jean-Yves Franceschi, Aymeric Dieuleveut, Martin Jaggi:
Unsupervised Scalable Representation Learning for Multivariate Time Series. 4652-4663 - Tao Tu, John W. Paisley, Stefan Haufe, Paul Sajda:
A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI. 4664-4673 - Bryan Wilder, Eric Ewing, Bistra Dilkina, Milind Tambe:
End to end learning and optimization on graphs. 4674-4685 - Fan Yang, Liu Leqi, Yifan Wu, Zachary Chase Lipton, Pradeep Ravikumar, Tom M. Mitchell, William W. Cohen:
Game Design for Eliciting Distinguishable Behavior. 4686-4695 - Rafael Müller, Simon Kornblith, Geoffrey E. Hinton:
When does label smoothing help? 4696-4705 - Harsh Gupta, R. Srikant, Lei Ying:
Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning. 4706-4715 - Lixin Fan, Kam Woh Ng, Chee Seng Chan:
Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks. 4716-4725 - Cole L. Hurwitz, Kai Xu, Akash Srivastava, Alessio Paolo Buccino, Matthias H. Hennig:
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference. 4726-4738 - Huaian Diao, Rajesh Jayaram, Zhao Song, Wen Sun, David P. Woodruff:
Optimal Sketching for Kronecker Product Regression and Low Rank Approximation. 4739-4750 - Ilias Diakonikolas, Themis Gouleakis, Christos Tzamos:
Distribution-Independent PAC Learning of Halfspaces with Massart Noise. 4751-4762 - Ronen Basri, David W. Jacobs, Yoni Kasten, Shira Kritchman:
The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies. 4763-4772 - Xingyu Lin, Harjatin Singh Baweja, George Kantor, David Held:
Adaptive Auxiliary Task Weighting for Reinforcement Learning. 4773-4784 - Soumya Basu, Rajat Sen, Sujay Sanghavi, Sanjay Shakkottai:
Blocking Bandits. 4785-4794 - Wei Qian, Yuqian Zhang, Yudong Chen:
Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities. 4795-4803 - Yuan Deng, Jon Schneider, Balasubramanian Sivan:
Prior-Free Dynamic Auctions with Low Regret Buyers. 4804-4814 - Taewan Kim, Joydeep Ghosh:
On Single Source Robustness in Deep Fusion Models. 4815-4826 - Andrew Bennett, Nathan Kallus:
Policy Evaluation with Latent Confounders via Optimal Balance. 4827-4837 - Rajat Sen, Hsiang-Fu Yu, Inderjit S. Dhillon:
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting. 4838-4847 - Chen Xing, Negar Rostamzadeh, Boris N. Oreshkin, Pedro O. Pinheiro:
Adaptive Cross-Modal Few-shot Learning. 4848-4858 - Ioannis Koutis, Huong Le:
Spectral Modification of Graphs for Improved Spectral Clustering. 4859-4868 - Ines Chami, Zhitao Ying, Christopher Ré, Jure Leskovec:
Hyperbolic Graph Convolutional Neural Networks. 4869-4880 - Shali Jiang, Roman Garnett, Benjamin Moseley:
Cost Effective Active Search. 4881-4890 - Jian Qian, Ronan Fruit, Matteo Pirotta, Alessandro Lazaric:
Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs. 4891-4900 - Xiao Sun, Jungwook Choi, Chia-Yu Chen, Naigang Wang, Swagath Venkataramani, Vijayalakshmi Srinivasan, Xiaodong Cui, Wei Zhang, Kailash Gopalakrishnan:
Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks. 4901-4910 - Guang-He Lee, Yang Yuan, Shiyu Chang, Tommi S. Jaakkola:
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers. 4911-4922 - Ruqi Zhang, Christopher De Sa:
Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees. 4923-4932 - Ari S. Morcos, Haonan Yu, Michela Paganini, Yuandong Tian:
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers. 4933-4943 - Chuan Guo, Ali Mousavi, Xiang Wu, Daniel Niels Holtmann-Rice, Satyen Kale, Sashank J. Reddi, Sanjiv Kumar:
Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces. 4944-4954 - Suman Kalyan Bera, Deeparnab Chakrabarty, Nicolas Flores, Maryam Negahbani:
Fair Algorithms for Clustering. 4955-4966 - Xin Guo, Anran Hu, Renyuan Xu, Junzi Zhang:
Learning Mean-Field Games. 4967-4977 - Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul N. Whatmough:
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers. 4978-4990 - Eric Jonas:
Deep imitation learning for molecular inverse problems. 4991-5001 - Chi Han, Jiayuan Mao, Chuang Gan, Josh Tenenbaum, Jiajun Wu:
Visual Concept-Metaconcept Learning. 5002-5013 - Ting-Chun Wang, Ming-Yu Liu, Andrew Tao, Guilin Liu, Bryan Catanzaro, Jan Kautz:
Few-shot Video-to-Video Synthesis. 5014-5025 - Weiyang Liu, Zhen Liu, James M. Rehg, Le Song:
Neural Similarity Learning. 5026-5037 - Yikang Shen, Shawn Tan, Seyed Arian Hosseini, Zhouhan Lin, Alessandro Sordoni, Aaron C. Courville:
Ordered Memory. 5038-5049 - David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel:
MixMatch: A Holistic Approach to Semi-Supervised Learning. 5050-5060 - Jingjing Wang, Sun Sun, Yaoliang Yu:
Multivariate Triangular Quantile Maps for Novelty Detection. 5061-5072 - Sharon Qian, Yaron Singer:
Fast Parallel Algorithms for Statistical Subset Selection Problems. 5073-5082 - Anton Bakhtin, Laurens van der Maaten, Justin Johnson, Laura Gustafson, Ross B. Girshick:
PHYRE: A New Benchmark for Physical Reasoning. 5083-5094 - Ji Xu, Daniel J. Hsu:
On the number of variables to use in principal component regression. 5095-5104 - Jicong Fan, Lijun Ding, Yudong Chen, Madeleine Udell:
Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery. 5105-5115 - Ifigeneia Apostolopoulou, Scott W. Linderman, Kyle Miller, Artur Dubrawski:
Mutually Regressive Point Processes. 5116-5127 - Gautier Izacard, Sreyas Mohan, Carlos Fernandez-Granda:
Data-driven Estimation of Sinusoid Frequencies. 5128-5138 - Yue Wang, Ziyu Jiang, Xiaohan Chen, Pengfei Xu, Yang Zhao, Yingyan Lin, Zhangyang Wang:
E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings. 5139-5151 - Tianjun Zhang, Zhewei Yao, Amir Gholami, Joseph E. Gonzalez, Kurt Keutzer, Michael W. Mahoney, George Biros:
ANODEV2: A Coupled Neural ODE Framework. 5152-5162 - Jayadev Acharya, Sourbh Bhadane, Piotr Indyk, Ziteng Sun:
Estimating Entropy of Distributions in Constant Space. 5163-5174 - Micah Carroll, Rohin Shah, Mark K. Ho, Tom Griffiths, Sanjit A. Seshia, Pieter Abbeel, Anca D. Dragan:
On the Utility of Learning about Humans for Human-AI Coordination. 5175-5186 - Gabriele Farina, Chun Kai Ling, Fei Fang, Tuomas Sandholm:
Efficient Regret Minimization Algorithm for Extensive-Form Correlated Equilibrium. 5187-5197 - Zhengyuan Zhou, Renyuan Xu, Jose H. Blanchet:
Learning in Generalized Linear Contextual Bandits with Stochastic Delays. 5198-5209 - Saeed Mahloujifar, Xiao Zhang, Mohammad Mahmoody, David Evans:
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness. 5210-5221 - Gabriele Farina, Christian Kroer, Tuomas Sandholm:
Optimistic Regret Minimization for Extensive-Form Games via Dilated Distance-Generating Functions. 5222-5232 - Erik Nijkamp, Mitch Hill, Song-Chun Zhu, Ying Nian Wu:
Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model. 5233-5243 - Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, Xifeng Yan:
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting. 5244-5254 - Pang Wei Koh, Kai-Siang Ang, Hubert H. K. Teo, Percy Liang:
On the Accuracy of Influence Functions for Measuring Group Effects. 5255-5265 - Yandong Wen, Bhiksha Raj, Rita Singh:
Face Reconstruction from Voice using Generative Adversarial Networks. 5266-5275 - Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel:
Incremental Few-Shot Learning with Attention Attractor Networks. 5276-5286 - Ivan Stelmakh, Nihar B. Shah, Aarti Singh:
On Testing for Biases in Peer Review. 5287-5297 - Chanho Eom, Bumsub Ham:
Learning Disentangled Representation for Robust Person Re-identification. 5298-5309 - Nixie S. Lesmana, Xuan Zhang, Xiaohui Bei:
Balancing Efficiency and Fairness in On-Demand Ridesourcing. 5310-5320 - Yulia Rubanova, Tian Qi Chen, David Duvenaud:
Latent Ordinary Differential Equations for Irregularly-Sampled Time Series. 5321-5331 - Yiqi Zhong, Cho-Ying Wu, Suya You, Ulrich Neumann:
Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion. 5332-5342 - Guillaume Charpiat, Nicolas Girard, Loris Felardos, Yuliya Tarabalka:
Input Similarity from the Neural Network Perspective. 5343-5352 - Marko Mitrovic, Ehsan Kazemi, Moran Feldman, Andreas Krause, Amin Karbasi:
Adaptive Sequence Submodularity. 5353-5364 - Adam Gaier, David Ha:
Weight Agnostic Neural Networks. 5365-5379 - C. Daniel Freeman, David Ha, Luke Metz:
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction. 5380-5391 - Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux:
Reducing the variance in online optimization by transporting past gradients. 5392-5403 - Daniel McDuff, Shuang Ma, Yale Song, Ashish Kapoor:
Characterizing Bias in Classifiers using Generative Models. 5404-5415 - Yunwen Lei, Peng Yang, Ke Tang, Ding-Xuan Zhou:
Optimal Stochastic and Online Learning with Individual Iterates. 5416-5426 - Ashudeep Singh, Thorsten Joachims:
Policy Learning for Fairness in Ranking. 5427-5437 - Alexander Irpan, Kanishka Rao, Konstantinos Bousmalis, Chris Harris, Julian Ibarz, Sergey Levine:
Off-Policy Evaluation via Off-Policy Classification. 5438-5449 - Corinna Cortes, Mehryar Mohri, Dmitry Storcheus:
Regularized Gradient Boosting. 5450-5459 - Atilim Gunes Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Saeid Naderiparizi, Andreas Munk, Jialin Liu, Bradley Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip H. S. Torr, Victor W. Lee, Kyle Cranmer, Prabhat, Frank Wood:
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. 5460-5473 - Harald Steck:
Markov Random Fields for Collaborative Filtering. 5474-5485 - Edward Raff:
A Step Toward Quantifying Independently Reproducible Machine Learning Research. 5486-5496 - David Eriksson, Michael Pearce, Jacob R. Gardner, Ryan Turner, Matthias Poloczek:
Scalable Global Optimization via Local Bayesian Optimization. 5497-5508 - Jinsung Yoon, Daniel Jarrett, Mihaela van der Schaar:
Time-series Generative Adversarial Networks. 5509-5519 - Qian Yang, Zhouyuan Huo, Wenlin Wang, Heng Huang, Lawrence Carin:
Ouroboros: On Accelerating Training of Transformer-Based Language Models. 5520-5530 - Shen-Huan Lyu, Liang Yang, Zhi-Hua Zhou:
A Refined Margin Distribution Analysis for Forest Representation Learning. 5531-5541 - Amir Najafi, Shin-ichi Maeda, Masanori Koyama, Takeru Miyato:
Robustness to Adversarial Perturbations in Learning from Incomplete Data. 5542-5552 - Kohei Hayashi, Taiki Yamaguchi, Yohei Sugawara, Shin-ichi Maeda:
Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks. 5553-5563 - Wei Deng, Xiao Zhang, Faming Liang, Guang Lin:
An Adaptive Empirical Bayesian Method for Sparse Deep Learning. 5564-5574 - Binghui Peng, Wei Chen:
Adaptive Influence Maximization with Myopic Feedback. 5575-5584 - Yiren Zhao, Xitong Gao, Daniel Bates, Robert D. Mullins, Cheng-Zhong Xu:
Focused Quantization for Sparse CNNs. 5585-5594 - Dinesh Garg, Shajith Ikbal, Santosh K. Srivastava, Harit Vishwakarma, Hima P. Karanam, L. Venkata Subramaniam:
Quantum Embedding of Knowledge for Reasoning. 5595-5605 - Vrettos Moulos:
Optimal Best Markovian Arm Identification with Fixed Confidence. 5606-5615 - Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill:
Limiting Extrapolation in Linear Approximate Value Iteration. 5616-5625 - Andrea Zanette, Mykel J. Kochenderfer, Emma Brunskill:
Almost Horizon-Free Structure-Aware Best Policy Identification with a Generative Model. 5626-5635 - Mahdi Karami, Dale Schuurmans, Jascha Sohl-Dickstein, Laurent Dinh, Daniel Duckworth:
Invertible Convolutional Flow. 5636-5646 - Philippe Casgrain:
A Latent Variational Framework for Stochastic Optimization. 5647-5657 - Xiaoling Hu, Fuxin Li, Dimitris Samaras, Chao Chen:
Topology-Preserving Deep Image Segmentation. 5658-5669 - Aming Wu, Linchao Zhu, Yahong Han, Yi Yang:
Connective Cognition Network for Directional Visual Commonsense Reasoning. 5670-5680 - Vikas K. Garg, Tamar Pichkhadze:
Online Markov Decoding: Lower Bounds and Near-Optimal Approximation Algorithms. 5681-5691 - Francisco M. Garcia, Philip S. Thomas:
A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning. 5692-5701 - Xiao Liu, Xiaolong Zou, Zilong Ji, Gengshuo Tian, Yuanyuan Mi, Tiejun Huang, K. Y. Michael Wong, Si Wu:
Push-pull Feedback Implements Hierarchical Information Retrieval Efficiently. 5702-5711 - Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, Wenwu Zhu:
Learning Disentangled Representations for Recommendation. 5712-5723 - Simon S. Du, Kangcheng Hou, Ruslan Salakhutdinov, Barnabás Póczos, Ruosong Wang, Keyulu Xu:
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels. 5724-5734 - Hui Guan, Lin Ning, Zhen Lin, Xipeng Shen, Huiyang Zhou, Seung-Hwan Lim:
In-Place Zero-Space Memory Protection for CNN. 5735-5744 - Bin Shi, Simon S. Du, Weijie J. Su, Michael I. Jordan:
Acceleration via Symplectic Discretization of High-Resolution Differential Equations. 5745-5753 - Zhilin Yang, Zihang Dai, Yiming Yang, Jaime G. Carbonell, Ruslan Salakhutdinov, Quoc V. Le:
XLNet: Generalized Autoregressive Pretraining for Language Understanding. 5754-5764 - Jianghong Shi, Eric Shea-Brown, Michael A. Buice:
Comparison Against Task Driven Artificial Neural Networks Reveals Functional Properties in Mouse Visual Cortex. 5765-5775 - Hoi-To Wai, Mingyi Hong, Zhuoran Yang, Zhaoran Wang, Kexin Tang:
Variance Reduced Policy Evaluation with Smooth Function Approximation. 5776-5787 - Ben Adlam, Corinna Cortes, Mehryar Mohri, Ningshan Zhang:
Learning GANs and Ensembles Using Discrepancy. 5788-5799 - Tiantian Fang, Alexander G. Schwing:
Co-Generation with GANs using AIS based HMC. 5800-5811 - Ronghui You, Zihan Zhang, Ziye Wang, Suyang Dai, Hiroshi Mamitsuka, Shanfeng Zhu:
AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification. 5812-5822 - Himanshu Sahni, Toby Buckley, Pieter Abbeel, Ilya Kuzovkin:
Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory GANs. 5823-5833 - Kecheng Zheng, Zheng-Jun Zha, Wei Wei:
Abstract Reasoning with Distracting Features. 5834-5845 - Zhiyong Yang, Qianqian Xu, Yangbangyan Jiang, Xiaochun Cao, Qingming Huang:
Generalized Block-Diagonal Structure Pursuit: Learning Soft Latent Task Assignment against Negative Transfer. 5846-5857 - Florian Tramèr, Dan Boneh:
Adversarial Training and Robustness for Multiple Perturbations. 5858-5868 - Gi-Soo Kim, Myunghee Cho Paik:
Doubly-Robust Lasso Bandit. 5869-5879 - Yangbangyan Jiang, Qianqian Xu, Zhiyong Yang, Xiaochun Cao, Qingming Huang:
DM2C: Deep Mixed-Modal Clustering. 5880-5890 - Xuezhe Ma, Xiang Kong, Shanghang Zhang, Eduard H. Hovy:
MaCow: Masked Convolutional Generative Flow. 5891-5900 - Drew A. Hudson, Christopher D. Manning:
Learning by Abstraction: The Neural State Machine. 5901-5914 - Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar:
Adaptive Gradient-Based Meta-Learning Methods. 5915-5926 - Kai Zheng, Haipeng Luo, Ilias Diakonikolas, Liwei Wang:
Equipping Experts/Bandits with Long-term Memory. 5927-5937 - Wenhao Yang, Xiang Li, Zhihua Zhang:
A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning. 5938-5948 - Mikhail Yurochkin, Zhiwei Fan, Aritra Guha, Paraschos Koutris, XuanLong Nguyen:
Scalable inference of topic evolution via models for latent geometric structures. 5949-5959 - Siqi Wang, Yijie Zeng, Xinwang Liu, En Zhu, Jianping Yin, Chuanfu Xu, Marius Kloft:
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network. 5960-5973 - Yonatan Geifman, Ran El-Yaniv:
Deep Active Learning with a Neural Architecture Search. 5974-5984 - Chris Criscitiello, Nicolas Boumal:
Efficiently escaping saddle points on manifolds. 5985-5995 - Jiong Zhang, Hsiang-Fu Yu, Inderjit S. Dhillon:
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks. 5996-6006 - Asiri Wijesinghe, Qing Wang:
DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters. 6007-6018 - Wonjae Kim, Yoonho Lee:
Learning Dynamics of Attention: Human Prior for Interpretable Machine Reasoning. 6019-6030 - Mareike Hartmann, Yova Kementchedjhieva, Anders Søgaard:
Comparing Unsupervised Word Translation Methods Step by Step. 6031-6041 - Tianyu Guo, Chang Xu, Boxin Shi, Chao Xu, Dacheng Tao:
Learning from Bad Data via Generation. 6042-6053 - Florian Scheidegger, Luca Benini, Costas Bekas, A. Cristiano I. Malossi:
Constrained deep neural network architecture search for IoT devices accounting for hardware calibration. 6054-6064 - Yihe Dong, Samuel B. Hopkins, Jerry Li:
Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection. 6065-6075 - Yanyao Shen, Sujay Sanghavi:
Iterative Least Trimmed Squares for Mixed Linear Regression. 6076-6086 - Yu Qi, Bin Liu, Yueming Wang, Gang Pan:
Dynamic Ensemble Modeling Approach to Nonstationary Neural Decoding in Brain-Computer Interfaces. 6087-6096 - Qing Wang, Yingru Li, Jiechao Xiong, Tong Zhang:
Divergence-Augmented Policy Optimization. 6097-6108 - Alessio Ansuini, Alessandro Laio, Jakob H. Macke, Davide Zoccolan:
Intrinsic dimension of data representations in deep neural networks. 6109-6119 - Zhao Song, David P. Woodruff, Peilin Zhong:
Towards a Zero-One Law for Column Subset Selection. 6120-6131 - Yi Tay, Anh Tuan Luu, Aston Zhang, Shuohang Wang, Siu Cheung Hui:
Compositional De-Attention Networks. 6132-6142 - Jian Ni, Shanghang Zhang, Haiyong Xie:
Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning. 6143-6154 - Zeyuan Allen-Zhu, Yuanzhi Li, Yingyu Liang:
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers. 6155-6166 - Sangwoo Mo, Chiheon Kim, Sungwoong Kim, Minsu Cho, Jinwoo Shin:
Mining GOLD Samples for Conditional GANs. 6167-6178 - Jie Song, Yixin Chen, Xinchao Wang, Chengchao Shen, Mingli Song:
Deep Model Transferability from Attribution Maps. 6179-6189 - Derek Yang, Li Zhao, Zichuan Lin, Tao Qin, Jiang Bian, Tie-Yan Liu:
Fully Parameterized Quantile Function for Distributional Reinforcement Learning. 6190-6199 - Guy Lorberbom, Tommi S. Jaakkola, Andreea Gane, Tamir Hazan:
Direct Optimization through arg max for Discrete Variational Auto-Encoder. 6200-6211 - Zichuan Lin, Li Zhao, Derek Yang, Tao Qin, Tie-Yan Liu, Guangwen Yang:
Distributional Reward Decomposition for Reinforcement Learning. 6212-6221 - Yilun Xu, Peng Cao, Yuqing Kong, Yizhou Wang:
L_DMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise. 6222-6233 - Motonobu Kanagawa, Philipp Hennig:
Convergence Guarantees for Adaptive Bayesian Quadrature Methods. 6234-6245 - Dan Zhang, Anna Khoreva:
Progressive Augmentation of GANs. 6246-6256 - Ali Kavis, Kfir Y. Levy, Francis R. Bach, Volkan Cevher:
UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization. 6257-6266 - Aaron Klein, Zhenwen Dai, Frank Hutter, Neil D. Lawrence, Javier González:
Meta-Surrogate Benchmarking for Hyperparameter Optimization. 6267-6277 - Xinyun Chen, Yuandong Tian:
Learning to Perform Local Rewriting for Combinatorial Optimization. 6278-6289 - Rahma Chaabouni, Eugene Kharitonov, Emmanuel Dupoux, Marco Baroni:
Anti-efficient encoding in emergent communication. 6290-6300 - Abraham Traoré, Maxime Berar, Alain Rakotomamonjy:
Singleshot : a scalable Tucker tensor decomposition. 6301-6312 - Yiren Wang, Yingce Xia, Fei Tian, Fei Gao, Tao Qin, ChengXiang Zhai, Tie-Yan Liu:
Neural Machine Translation with Soft Prototype. 6313-6322 - Nicki Skafte Detlefsen, Martin Jørgensen, Søren Hauberg:
Reliable training and estimation of variance networks. 6323-6333 - Weiwei Liu:
Copula Multi-label Learning. 6334-6343 - Martin Trapp, Robert Peharz, Hong Ge, Franz Pernkopf, Zoubin Ghahramani:
Bayesian Learning of Sum-Product Networks. 6344-6355 - Robert Pinsler, Jonathan Gordon, Eric T. Nalisnick, José Miguel Hernández-Lobato:
Bayesian Batch Active Learning as Sparse Subset Approximation. 6356-6367 - Zengfeng Huang, Ziyue Huang, Yilei Wang, Ke Yi:
Optimal Sparsity-Sensitive Bounds for Distributed Mean Estimation. 6368-6378 - Xiaohan Ding, Guiguang Ding, Xiangxin Zhou, Yuchen Guo, Jungong Han, Ji Liu:
Global Sparse Momentum SGD for Pruning Very Deep Neural Networks. 6379-6391 - Tomasz Kusmierczyk, Joseph Sakaya, Arto Klami:
Variational Bayesian Decision-making for Continuous Utilities. 6392-6402 - Ryo Karakida, Shotaro Akaho, Shun-ichi Amari:
The Normalization Method for Alleviating Pathological Sharpness in Wide Neural Networks. 6403-6413 - Natasa Tagasovska, David Lopez-Paz:
Single-Model Uncertainties for Deep Learning. 6414-6425 - Eran Malach, Shai Shalev-Shwartz:
Is Deeper Better only when Shallow is Good? 6426-6435 - Matteo Togninalli, M. Elisabetta Ghisu, Felipe Llinares-López, Bastian Rieck, Karsten M. Borgwardt:
Wasserstein Weisfeiler-Lehman Graph Kernels. 6436-6446 - Qi Dou, Daniel Coelho de Castro, Konstantinos Kamnitsas, Ben Glocker:
Domain Generalization via Model-Agnostic Learning of Semantic Features. 6447-6458 - Lukas Hoyer, Mauricio Munoz, Prateek Katiyar, Anna Khoreva, Volker Fischer:
Grid Saliency for Context Explanations of Semantic Segmentation. 6459-6470 - Ioannis Panageas, Georgios Piliouras, Xiao Wang:
First-order methods almost always avoid saddle points: The case of vanishing step-sizes. 6471-6480 - Michael Arbel, Anna Korba, Adil Salim, Arthur Gretton:
Maximum Mean Discrepancy Gradient Flow. 6481-6491 - Sajin Sasy, Olga Ohrimenko:
Oblivious Sampling Algorithms for Private Data Analysis. 6492-6503 - Zaiqiao Meng, Shangsong Liang, Jinyuan Fang, Teng Xiao:
Semi-supervisedly Co-embedding Attributed Networks. 6504-6513 - Roman Beliy, Guy Gaziv, Assaf Hoogi, Francesca Strappini, Tal Golan, Michal Irani:
From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI. 6514-6524 - Natasa Tagasovska, Damien Ackerer, Thibault Vatter:
Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders. 6525-6537 - Tobias Sommer Thune, Nicolò Cesa-Bianchi, Yevgeny Seldin:
Nonstochastic Multiarmed Bandits with Unrestricted Delays. 6538-6547 - Lars Maaløe, Marco Fraccaro, Valentin Liévin, Ole Winther:
BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling. 6548-6558 - Bolin Wei, Ge Li, Xin Xia, Zhiyi Fu, Zhi Jin:
Code Generation as a Dual Task of Code Summarization. 6559-6569 - Ron Shapira Weber, Matan Eyal, Nicki Skafte Detlefsen, Oren Shriki, Oren Freifeld:
Diffeomorphic Temporal Alignment Nets. 6570-6581 - Cheng-Chun Hsu, Kuang-Jui Hsu, Chung-Chi Tsai, Yen-Yu Lin, Yung-Yu Chuang:
Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior. 6582-6593 - Gilad Yehudai, Ohad Shamir:
On the Power and Limitations of Random Features for Understanding Neural Networks. 6594-6604 - Tianyuan Jin, Jieming Shi, Xiaokui Xiao, Enhong Chen:
Efficient Pure Exploration in Adaptive Round model. 6605-6614 - Taufik Xu, Chongxuan Li, Jun Zhu, Bo Zhang:
Multi-objects Generation with Amortized Structural Regularization. 6615-6625 - Karlis Freivalds, Emils Ozolins, Agris Sostaks:
Neural Shuffle-Exchange Networks - Sequence Processing in O(n log n) Time. 6626-6637 - Yukang Chen, Tong Yang, Xiangyu Zhang, Gaofeng Meng, Xinyu Xiao, Jian Sun:
DetNAS: Backbone Search for Object Detection. 6638-6648 - Adil Salim, Dmitry Kovalev, Peter Richtárik:
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates. 6649-6661 - Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, Sungwoong Kim:
Fast AutoAugment. 6662-6672 - Zeyuan Allen-Zhu, Yuanzhi Li, Zhao Song:
On the Convergence Rate of Training Recurrent Neural Networks. 6673-6685 - Ben Deverett, Ryan Faulkner, Meire Fortunato, Gregory Wayne, Joel Z. Leibo:
Interval timing in deep reinforcement learning agents. 6686-6695 - Roi Livni, Yishay Mansour:
Graph-based Discriminators: Sample Complexity and Expressiveness. 6696-6705 - Stanislav Fort, Stanislaw Jastrzebski:
Large Scale Structure of Neural Network Loss Landscapes. 6706-6714 - Mike Gartrell, Victor-Emmanuel Brunel, Elvis Dohmatob, Syrine Krichene:
Learning Nonsymmetric Determinantal Point Processes. 6715-6725 - Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan:
Hypothesis Set Stability and Generalization. 6726-6736 - Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni:
Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. 6737-6746 - Seppo Virtanen, Mark A. Girolami:
Precision-Recall Balanced Topic Modelling. 6747-6756 - Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi Koyejo:
Learning Sparse Distributions using Iterative Hard Thresholding. 6757-6766 - Iryna Korshunova, Hanchen Xiong, Mateusz Fedoryszak, Lucas Theis:
Discriminative Topic Modeling with Logistic LDA. 6767-6777 - Shouvanik Chakrabarti, Yiming Huang, Tongyang Li, Soheil Feizi, Xiaodi Wu:
Quantum Wasserstein Generative Adversarial Networks. 6778-6789 - Joan Serrà, Santiago Pascual, Carlos Segura:
Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion. 6790-6800 - Ho Chung Leon Law, Peilin Zhao, Leung Sing Chan, Junzhou Huang, Dino Sejdinovic:
Hyperparameter Learning via Distributional Transfer. 6801-6812 - Akinori Tanaka:
Discriminator optimal transport. 6813-6823 - David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, Jan Gasthaus:
High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes. 6824-6834 - Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama:
Are Anchor Points Really Indispensable in Label-Noise Learning? 6835-6846 - Fenglin Liu, Yuanxin Liu, Xuancheng Ren, Xiaodong He, Xu Sun:
Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations. 6847-6857 - Marco Cuturi, Olivier Teboul, Jean-Philippe Vert:
Differentiable Ranking and Sorting using Optimal Transport. 6858-6868 - Gaël Letarte, Pascal Germain, Benjamin Guedj, François Laviolette:
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks. 6869-6879 - Chenwei Ding, Mingming Gong, Kun Zhang, Dacheng Tao:
Likelihood-Free Overcomplete ICA and Applications In Causal Discovery. 6880-6890 - Dongdong Ge, Haoyue Wang, Zikai Xiong, Yinyu Ye:
Interior-Point Methods Strike Back: Solving the Wasserstein Barycenter Problem. 6891-6902 - Denis Mazur, Vage Egiazarian, Stanislav Morozov, Artem Babenko:
Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs. 6903-6913 - Boris Muzellec, Marco Cuturi:
Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections. 6914-6925 - Huizhuo Yuan, Xiangru Lian, Chris Junchi Li, Ji Liu, Wenqing Hu:
Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent. 6926-6935 - Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos:
On the convergence of single-call stochastic extra-gradient methods. 6936-6946 - Shagun Ajmera, Shreya Rajagopal, Razi Rehman, Devarajan Sridharan:
Infra-slow brain dynamics as a marker for cognitive function and decline. 6947-6958 - Rui Zhang, Hanghang Tong:
Robust Principal Component Analysis with Adaptive Neighbors. 6959-6967 - Samuli Laine, Tero Karras, Jaakko Lehtinen, Timo Aila:
High-Quality Self-Supervised Deep Image Denoising. 6968-6978 - Sebastian Goldt, Madhu Advani, Andrew M. Saxe, Florent Krzakala, Lenka Zdeborová:
Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup. 6979-6989 - Yuan Liu, Zehong Shen, Zhixuan Lin, Sida Peng, Hujun Bao, Xiaowei Zhou:
GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs. 6990-7001 - Mark Herbster, James Robinson:
Online Prediction of Switching Graph Labelings with Cluster Specialists. 7002-7012 - Fan Zhou, Tengfei Li, Haibo Zhou, Hongtu Zhu, Jieping Ye:
Graph-Based Semi-Supervised Learning with Non-ignorable Non-response. 7013-7023 - Andreas Kirsch, Joost van Amersfoort, Yarin Gal:
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning. 7024-7035 - Yaniv Blumenfeld, Dar Gilboa, Daniel Soudry:
A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off. 7036-7046 - Marek Petrik, Reazul Hasan Russel:
Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs. 7047-7056 - Alexis Conneau, Guillaume Lample:
Cross-lingual Language Model Pretraining. 7057-7067 - Cornelius Schröder, Ben James, Leon Lagnado, Philipp Berens:
Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon Synapse. 7068-7078 - Maxence Ernoult, Julie Grollier, Damien Querlioz, Yoshua Bengio, Benjamin Scellier:
Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input. 7079-7089 - Nicolas Keriven, Gabriel Peyré:
Universal Invariant and Equivariant Graph Neural Networks. 7090-7099 - Jaehyeok Shin, Aaditya Ramdas, Alessandro Rinaldo:
Are sample means in multi-armed bandits positively or negatively biased? 7100-7109 - Abi Komanduru, Jean Honorio:
On the Correctness and Sample Complexity of Inverse Reinforcement Learning. 7110-7119 - Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson:
VIREL: A Variational Inference Framework for Reinforcement Learning. 7120-7134 - Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, Nicu Sebe:
First Order Motion Model for Image Animation. 7135-7145 - Laurence Aitchison:
Tensor Monte Carlo: Particle Methods for the GPU era. 7146-7155 - Alban Laflaquière, Michaël Garcia Ortiz:
Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction. 7156-7166 - Jiabin Liu, Bo Wang, Zhiquan Qi, Yingjie Tian, Yong Shi:
Learning from Label Proportions with Generative Adversarial Networks. 7167-7177 - Debmalya Mandal, Ariel D. Procaccia, Nisarg Shah, David P. Woodruff:
Efficient and Thrifty Voting by Any Means Necessary. 7178-7189 - Can Qin, Haoxuan You, Lichen Wang, C.-C. Jay Kuo, Yun Fu:
PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation. 7190-7201 - Xiangyi Chen, Sijia Liu, Kaidi Xu, Xingguo Li, Xue Lin, Mingyi Hong, David D. Cox:
ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization. 7202-7213 - Erwan Lecarpentier, Emmanuel Rachelson:
Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning. 7214-7223 - Akihiro Kishimoto, Beat Buesser, Bei Chen, Adi Botea:
Depth-First Proof-Number Search with Heuristic Edge Cost and Application to Chemical Synthesis Planning. 7224-7234 - Nika Haghtalab, Cameron Musco, Bo Waggoner:
Toward a Characterization of Loss Functions for Distribution Learning. 7235-7244 - Sebastian Mair, Ulf Brefeld:
Coresets for Archetypal Analysis. 7245-7253 - Adam Bielski, Paolo Favaro:
Emergence of Object Segmentation in Perturbed Generative Models. 7254-7264 - Xiyang Hu, Cynthia Rudin, Margo I. Seltzer:
Optimal Sparse Decision Trees. 7265-7273 - Yue Sun, Nicolas Flammarion, Maryam Fazel:
Escaping from saddle points on Riemannian manifolds. 7274-7284 - Sicheng Zhao, Bo Li, Xiangyu Yue, Yang Gu, Pengfei Xu, Runbo Hu, Hua Chai, Kurt Keutzer:
Multi-source Domain Adaptation for Semantic Segmentation. 7285-7298 - Carlo Ciliberto, Francis R. Bach, Alessandro Rudi:
Localized Structured Prediction. 7299-7309 - Sarath Yasodharan, Patrick Loiseau:
Nonzero-sum Adversarial Hypothesis Testing Games. 7310-7320 - David Sabbagh, Pierre Ablin, Gaël Varoquaux, Alexandre Gramfort, Denis A. Engemann:
Manifold-regression to predict from MEG/EEG brain signals without source modeling. 7321-7332 - Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, Kalyan Veeramachaneni:
Modeling Tabular data using Conditional GAN. 7333-7343