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ORCID21st AISTATS 2018: Playa Blanca, Lanzarote, Canary Islands, Spain
- Amos J. Storkey, Fernando Pérez-Cruz:
International Conference on Artificial Intelligence and Statistics, AISTATS 2018, 9-11 April 2018, Playa Blanca, Lanzarote, Canary Islands, Spain. Proceedings of Machine Learning Research 84, PMLR 2018 - Krzysztof Choromanski, Mark Rowland, Tamás Sarlós, Vikas Sindhwani, Richard E. Turner, Adrian Weller:
The Geometry of Random Features. 1-9 - Sungsoo Ahn, Michael Chertkov, Jinwoo Shin, Adrian Weller:
Gauged Mini-Bucket Elimination for Approximate Inference. 10-19 - Aleksander Madry, Slobodan Mitrovic, Ludwig Schmidt:
A Fast Algorithm for Separated Sparsity via Perturbed Lagrangians. 20-28 - Mark Rowland, Marc G. Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh:
An Analysis of Categorical Distributional Reinforcement Learning. 29-37 - Thomas Möllenhoff, Zhenzhang Ye, Tao Wu, Daniel Cremers:
Combinatorial Preconditioners for Proximal Algorithms on Graphs. 38-47 - Guy Uziel, Ran El-Yaniv:
Growth-Optimal Portfolio Selection under CVaR Constraints. 48-57 - Peng Xu, Bryan D. He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré:
Accelerated Stochastic Power Iteration. 58-67 - Woosang Lim, Rundong Du, Bo Dai, Kyomin Jung, Le Song, Haesun Park:
Multi-scale Nystrom Method. 68-76 - Satoshi Hara, Kohei Hayashi:
Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach. 77-85 - James R. Foulds:
Mixed Membership Word Embeddings for Computational Social Science. 86-95 - Emma Pierson, Sam Corbett-Davies, Sharad Goel:
Fast Threshold Tests for Detecting Discrimination. 96-105 - Juho Piironen, Aki Vehtari:
Iterative Supervised Principal Components. 106-114 - Chieh Wu, Stratis Ioannidis, Mario Sznaier, Xiangyu Li, David R. Kaeli, Jennifer G. Dy:
Iterative Spectral Method for Alternative Clustering. 115-123 - Dennis Forster, Jörg Lücke:
Can clustering scale sublinearly with its clusters? A variational EM acceleration of GMMs and k-means. 124-132 - Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos:
Parallelised Bayesian Optimisation via Thompson Sampling. 133-142 - Rahul G. Krishnan, Dawen Liang, Matthew D. Hoffman:
On the challenges of learning with inference networks on sparse, high-dimensional data. 143-151 - Makoto Yamada, Yuta Umezu, Kenji Fukumizu, Ichiro Takeuchi:
Post Selection Inference with Kernels. 152-160 - Joel Ratsaby:
On how complexity affects the stability of a predictor. 161-167 - Zhiqiang Xu, Xin Gao:
On Truly Block Eigensolvers via Riemannian Optimization. 168-177 - Heng Guo, Kaan Kara, Ce Zhang:
Layerwise Systematic Scan: Deep Boltzmann Machines and Beyond. 178-187 - Rajiv Khanna, Anastasios Kyrillidis:
IHT dies hard: Provable accelerated Iterative Hard Thresholding. 188-198 - Jinshan Zeng, Ke Ma, Yuan Yao:
Finding Global Optima in Nonconvex Stochastic Semidefinite Optimization with Variance Reduction. 199-207 - Dehan Kong, Howard D. Bondell, Weining Shen:
Outlier Detection and Robust Estimation in Nonparametric Regression. 208-216 - Luca Ambrogioni, Eric Maris:
Integral Transforms from Finite Data: An Application of Gaussian Process Regression to Fourier Analysis. 217-225 - Gabriele Abbati, Alessandra Tosi, Michael A. Osborne
, Seth R. Flaxman:
AdaGeo: Adaptive Geometric Learning for Optimization and Sampling. 226-234 - Xiand Gao, Xiaobo Li, Shuzhong Zhang:
Online Learning with Non-Convex Losses and Non-Stationary Regret. 235-243 - Christophe Dupuy, Francis R. Bach:
Learning Determinantal Point Processes in Sublinear Time. 244-257 - Kaiqing Zhang, Zhuoran Yang, Zhaoran Wang:
Nonlinear Structured Signal Estimation in High Dimensions via Iterative Hard Thresholding. 258-268 - Hiroyuki Kasai, Hiroyuki Sato, Bamdev Mishra:
Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis. 269-278 - Young Hun Jung, Ambuj Tewari:
Online Boosting Algorithms for Multi-label Ranking. 279-287 - Sijia Liu, Jie Chen, Pin-Yu Chen, Alfred O. Hero III:
Zeroth-Order Online Alternating Direction Method of Multipliers: Convergence Analysis and Applications. 288-297 - Paul Rolland, Jonathan Scarlett, Ilija Bogunovic, Volkan Cevher:
High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups. 298-307 - Jan Kremer, Fei Sha, Christian Igel:
Robust Active Label Correction. 308-316 - Amit Gruber, Chen Yanover, Tal El-Hay, Anders Sönnerborg, Vanni Borghi, Francesca Incardona, Yaara Goldschmidt:
Factorial HMMs with Collapsed Gibbs Sampling for Optimizing Long-term HIV Therapy. 317-326 - Pankaj Pansari, Chris Russell, M. Pawan Kumar:
Optimal Submodular Extensions for Marginal Estimation. 327-335 - Nir Rosenfeld, Amir Globerson:
Semi-Supervised Learning with Competitive Infection Models. 336-346 - Nir Rosenfeld, Yishay Mansour, Elad Yom-Tov:
Discriminative Learning of Prediction Intervals. 347-355 - Wenlin Wang, Zhe Gan, Wenqi Wang, Dinghan Shen, Jiaji Huang, Wei Ping, Sanjeev Satheesh, Lawrence Carin:
Topic Compositional Neural Language Model. 356-365 - Eric T. Nalisnick, Padhraic Smyth:
Learning Priors for Invariance. 366-375 - Scott Cheng-Hsin Yang, Yue Yu, Arash Givchi, Pei Wang, Wai Keen Vong, Patrick Shafto:
Optimal Cooperative Inference. 376-385 - David Liau, Zhao Song, Eric Price, Ger Yang:
Stochastic Multi-armed Bandits in Constant Space. 386-394 - Dehua Cheng, Natali Ruchansky, Yan Liu:
Matrix completability analysis via graph k-connectivity. 395-403 - Xiang Cheng, Fred (Farbod) Roosta, Stefan Palombo, Peter L. Bartlett, Michael W. Mahoney:
FLAG n' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods. 404-414 - Riikka Huusari, Hachem Kadri, Cécile Capponi:
Multi-view Metric Learning in Vector-valued Kernel Spaces. 415-424 - William Herlands, Edward McFowland, Andrew Gordon Wilson, Daniel B. Neill:
Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data. 425-434 - Jacopo Cavazza, Pietro Morerio, Benjamin D. Haeffele, Connor Lane, Vittorio Murino, René Vidal:
Dropout as a Low-Rank Regularizer for Matrix Factorization. 435-444 - Tianbao Yang, Zhe Li, Lijun Zhang:
A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer. 445-453 - Masaaki Takada, Taiji Suzuki, Hironori Fujisawa:
Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables. 454-463 - Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar Rätsch:
Boosting Variational Inference: an Optimization Perspective. 464-472 - Aurélien Bellet, Rachid Guerraoui, Mahsa Taziki, Marc Tommasi:
Personalized and Private Peer-to-Peer Machine Learning. 473-481 - Rose Yu, Max Guangyu Li, Yan Liu:
Tensor Regression Meets Gaussian Processes. 482-490 - Emanuel Laude, Tao Wu, Daniel Cremers:
A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization. 491-499 - Vivek Kumar Bagaria, Govinda M. Kamath, Vasilis Ntranos, Martin J. Zhang, David Tse:
Medoids in Almost-Linear Time via Multi-Armed Bandits. 500-509 - Zhiyang Wang, Ruida Zhou, Cong Shen:
Regional Multi-Armed Bandits. 510-518 - Yang Cao, Liyan Xie, Yao Xie, Huan Xu:
Nearly second-order optimality of online joint detection and estimation via one-sample update schemes. 519-528 - Or Sharir, Amnon Shashua:
Sum-Product-Quotient Networks. 529-537 - Sunil Gupta, Alistair Shilton, Santu Rana, Svetha Venkatesh:
Exploiting Strategy-Space Diversity for Batch Bayesian Optimization. 538-547 - Stéphan Clémençon, François Portier:
Beating Monte Carlo Integration: a Nonasymptotic Study of Kernel Smoothing Methods. 548-556 - Michel Besserve, Naji Shajarisales, Bernhard Schölkopf, Dominik Janzing:
Group invariance principles for causal generative models. 557-565 - Nataliya Sokolovska, Yann Chevaleyre, Jean-Daniel Zucker:
A Provable Algorithm for Learning Interpretable Scoring Systems. 566-574 - Hyunjik Kim, Yee Whye Teh:
Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes. 575-584 - Shinsaku Sakaue, Masakazu Ishihata, Shin-ichi Minato:
Efficient Bandit Combinatorial Optimization Algorithm with Zero-suppressed Binary Decision Diagrams. 585-594 - Hejia Zhang, Po-Hsuan Chen, Peter J. Ramadge:
Transfer Learning on fMRI Datasets. 595-603 - Chaofan Chen, Cynthia Rudin:
An Optimization Approach to Learning Falling Rule Lists. 604-612 - Courtney Paquette, Hongzhou Lin, Dmitriy Drusvyatskiy, Julien Mairal, Zaïd Harchaoui:
Catalyst for Gradient-based Nonconvex Optimization. 613-622 - Hongteng Xu, Dixin Luo, Xu Chen, Lawrence Carin:
Benefits from Superposed Hawkes Processes. 623-631 - Julian Katz-Samuels, Clayton Scott:
Nonparametric Preference Completion. 632-641 - Mathieu Sinn, Ambrish Rawat:
Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training. 642-651 - Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton:
Efficient and principled score estimation with Nyström kernel exponential families. 652-660 - Liqun Chen, Shuyang Dai, Yunchen Pu, Erjin Zhou, Chunyuan Li, Qinliang Su, Changyou Chen, Lawrence Carin:
Symmetric Variational Autoencoder and Connections to Adversarial Learning. 661-669 - Sergey Bartunov, Dmitry P. Vetrov:
Few-shot Generative Modelling with Generative Matching Networks. 670-678 - Tianyu Li, Guillaume Rabusseau, Doina Precup:
Nonlinear Weighted Finite Automata. 679-688 - Hugh Salimbeni, Stefanos Eleftheriadis, James Hensman:
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models. 689-697 - Iñigo Urteaga, Chris Wiggins:
Variational inference for the multi-armed contextual bandit. 698-706 - Robert M. Gower, Nicolas Le Roux, Francis R. Bach:
Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods. 707-715 - Michal Derezinski, Manfred K. Warmuth:
Subsampling for Ridge Regression via Regularized Volume Sampling. 716-725 - Pavel Izmailov, Alexander Novikov, Dmitry Kropotov:
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition. 726-735 - Michal Derezinski, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer:
Batch-Expansion Training: An Efficient Optimization Framework. 736-744 - Zi Wang, Clement Gehring, Pushmeet Kohli, Stefanie Jegelka:
Batched Large-scale Bayesian Optimization in High-dimensional Spaces. 745-754 - Feras Saad, Vikash Mansinghka:
Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time Series. 755-764 - Renbo Zhao, Volkan Cevher:
Stochastic Three-Composite Convex Minimization with a Linear Operator. 765-774 - Cynthia Rudin, Yining Wang:
Direct Learning to Rank And Rerank. 775-783 - Olivier Bachem, Mario Lucic, Silvio Lattanzi:
One-shot Coresets: The Case of k-Clustering. 784-792 - Lingfei Wu, Ian En-Hsu Yen, Jinfeng Yi, Fangli Xu, Qi Lei, Michael Witbrock:
Random Warping Series: A Random Features Method for Time-Series Embedding. 793-802 - Sanghamitra Dutta, Gauri Joshi, Soumyadip Ghosh, Parijat Dube, Priya Nagpurkar:
Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD. 803-812 - Futoshi Futami, Issei Sato, Masashi Sugiyama:
Variational Inference based on Robust Divergences. 813-822 - Aditya Grover, Ramki Gummadi, Miguel Lázaro-Gredilla, Dale Schuurmans, Stefano Ermon:
Variational Rejection Sampling. 823-832 - Aditya Grover, Todor M. Markov, Peter M. Attia, Norman Jin, Nicolas Perkins, Bryan Cheong, Michael H. Chen, Zi Yang, Stephen J. Harris, William C. Chueh, Stefano Ermon:
Best arm identification in multi-armed bandits with delayed feedback. 833-842 - Liyuan Xu, Junya Honda, Masashi Sugiyama:
A fully adaptive algorithm for pure exploration in linear bandits. 843-851 - Rajat Sen, Karthikeyan Shanmugam, Sanjay Shakkottai:
Contextual Bandits with Stochastic Experts. 852-861 - Sven Schmit, Carlos Riquelme:
Human Interaction with Recommendation Systems. 862-870 - I (Eli) Chien, Chung-Yi Lin, I-Hsiang Wang:
Community Detection in Hypergraphs: Optimal Statistical Limit and Efficient Algorithms. 871-879 - Mathieu Blondel, Vivien Seguy, Antoine Rolet:
Smooth and Sparse Optimal Transport. 880-889 - Ilija Bogunovic, Junyao Zhao, Volkan Cevher:
Robust Maximization of Non-Submodular Objectives. 890-899 - Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf:
Cause-Effect Inference by Comparing Regression Errors. 900-909 - Alexis Bellot, Mihaela van der Schaar:
Tree-based Bayesian Mixture Model for Competing Risks. 910-918 - Julien Pérolat, Bilal Piot, Olivier Pietquin:
Actor-Critic Fictitious Play in Simultaneous Move Multistage Games. 919-928 - Antonio Sutera, Célia Châtel, Gilles Louppe, Louis Wehenkel, Pierre Geurts:
Random Subspace with Trees for Feature Selection Under Memory Constraints. 929-937 - Jakob Runge:
Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information. 938-947 - Tomi Silander, Janne Leppä-aho, Elias Jääsaari, Teemu Roos:
Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures. 948-957 - Achintya Kundu, Francis R. Bach, Chiranjib Bhattacharyya:
Convex Optimization over Intersection of Simple Sets: improved Convergence Rate Guarantees via an Exact Penalty Approach. i - Christian A. Naesseth, Scott W. Linderman, Rajesh Ranganath, David M. Blei:
Variational Sequential Monte Carlo. 968-977 - Masaaki Imaizumi, Takanori Maehara, Yuichi Yoshida:
Statistically Efficient Estimation for Non-Smooth Probability Densities. 978-987 - Xu Hu, Guillaume Obozinski:
SDCA-Powered Inexact Dual Augmented Lagrangian Method for Fast CRF Learning. 988-997 - Mathurin Massias, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon:
Generalized Concomitant Multi-Task Lasso for Sparse Multimodal Regression. 998-1007 - Atsushi Nitanda, Taiji Suzuki:
Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models. 1008-1016 - Jianqing Fan, Wenyan Gong, Chris Junchi Li, Qiang Sun:
Statistical Sparse Online Regression: A Diffusion Approximation Perspective. 1017-1026 - Fanhua Shang, Yuanyuan Liu, Kaiwen Zhou, James Cheng, Kelvin Kai Wing Ng, Yuichi Yoshida:
Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization. 1027-1036 - Lawrence M. Murray, Daniel Lundén, Jan Kudlicka, David Broman, Thomas B. Schön:
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs. 1037-1046 - Pritish Mohapatra, C. V. Jawahar, M. Pawan Kumar:
Learning to Round for Discrete Labeling Problems. 1047-1056 - Reinhard Heckel, Max Simchowitz, Kannan Ramchandran, Martin J. Wainwright:
Approximate ranking from pairwise comparisons. 1057-1066 - Michael C. Hughes, Gabriel Hope, Leah Weiner, Thomas H. McCoy Jr., Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez:
Semi-Supervised Prediction-Constrained Topic Models. 1067-1076 - Yichen Wang, Evangelos A. Theodorou, Apurv Verma, Le Song:
A Stochastic Differential Equation Framework for Guiding Online User Activities in Closed Loop. 1077-1086 - Pan Xu, Tianhao Wang, Quanquan Gu:
Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time Algorithms. 1087-1096 - Xiao Zhang, Lingxiao Wang, Quanquan Gu:
A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery. 1097-1107 - Hongyi Ding, Mohammad Emtiyaz Khan, Issei Sato, Masashi Sugiyama:
Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling. 1108-1116 - Masayuki Karasuyama, Hiroshi Mamitsuka:
Factor Analysis on a Graph. 1117-1126 - Junxiang Chen, Yale Chang, Peter J. Castaldi, Michael H. Cho, Brian Hobbs, Jennifer G. Dy:
Crowdclustering with Partition Labels. 1127-1136 - Ruiyi Zhang, Chunyuan Li, Changyou Chen, Lawrence Carin:
Learning Structural Weight Uncertainty for Sequential Decision-Making. 1137-1146 - Jiahao Xie, Hui Qian, Zebang Shen, Chao Zhang:
Towards Memory-Friendly Deterministic Incremental Gradient Method. 1147-1156 - Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan:
Optimality of Approximate Inference Algorithms on Stable Instances. 1157-1166 - Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic, Seth R. Flaxman:
Bayesian Approaches to Distribution Regression. 1167-1176 - Marko Mitrovic, Moran Feldman, Andreas Krause, Amin Karbasi:
Submodularity on Hypergraphs: From Sets to Sequences. 1177-1184 - Bowei Yan, Purnamrita Sarkar, Xiuyuan Cheng:
Provable Estimation of the Number of Blocks in Block Models. 1185-1194 - Michael T. Smith, Mauricio A. Álvarez, Max Zwiessele, Neil D. Lawrence:
Differentially Private Regression with Gaussian Processes. 1195-1203 - Sai Praneeth Reddy Karimireddy, Sebastian U. Stich, Martin Jaggi:
Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems. 1204-1213 - Jakub M. Tomczak, Max Welling:
VAE with a VampPrior. 1214-1223 - Avi Pfeffer, Brian E. Ruttenberg, William Kretschmer, Alison O'Connor:
Structured Factored Inference for Probabilistic Programming. 1224-1232 - Sashank J. Reddi, Manzil Zaheer, Suvrit Sra, Barnabás Póczos, Francis R. Bach, Ruslan Salakhutdinov, Alexander J. Smola:
A Generic Approach for Escaping Saddle points. 1233-1242 - Nathan Kallus, Angela Zhou:
Policy Evaluation and Optimization with Continuous Treatments. 1243-1251 - Alan Lazarus, Dirk Husmeier, Theodore Papamarkou:
Multiphase MCMC Sampling for Parameter Inference in Nonlinear Ordinary Differential Equations. 1252-1260 - Xinkun Nie, Xiaoying Tian, Jonathan Taylor, James Zou:
Why Adaptively Collected Data Have Negative Bias and How to Correct for It. 1261-1269 - Sheng Chen, Arindam Banerjee:
Sparse Linear Isotonic Models. 1270-1279 - Jean-Yves Franceschi, Alhussein Fawzi, Omar Fawzi:
Robustness of classifiers to uniform $\ell_p$ and Gaussian noise. 1280-1288 - Xi Tan, Vinayak A. Rao, Jennifer Neville:
Nested CRP with Hawkes-Gaussian Processes. 1289-1298 - Huaian Diao, Zhao Song, Wen Sun, David P. Woodruff:
Sketching for Kronecker Product Regression and P-splines. 1299-1308 - Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson, Ryan P. Adams:
Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models. 1309-1317 - Chris Hickey, Graham Cormode:
Cheap Checking for Cloud Computing: Statistical Analysis via Annotated Data Streams. 1318-1326 - Shashank Singh, Barnabás Póczos, Jian Ma:
Minimax Reconstruction Risk of Convolutional Sparse Dictionary Learning. 1327-1336 - Michael Arbel, Arthur Gretton:
Kernel Conditional Exponential Family. 1337-1346 - Chandrashekar Lakshminarayanan, Csaba Szepesvári:
Linear Stochastic Approximation: How Far Does Constant Step-Size and Iterate Averaging Go? 1347-1355 - Yining Wang, Simon S. Du, Sivaraman Balakrishnan, Aarti Singh:
Stochastic Zeroth-order Optimization in High Dimensions. 1356-1365 - Yuzhe Ma, Robert Nowak, Philippe Rigollet, Xuezhou Zhang, Xiaojin Zhu:
Teacher Improves Learning by Selecting a Training Subset. 1366-1375 - Penporn Koanantakool, Alnur Ali, Ariful Azad, Aydin Buluç, Dmitriy Morozov, Leonid Oliker, Katherine A. Yelick, Sang-Yun Oh:
Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation. 1376-1386 - Pranjal Awasthi, Bahman Kalantari, Yikai Zhang:
Robust Vertex Enumeration for Convex Hulls in High Dimensions. 1387-1396 - Taiji Suzuki:
Fast generalization error bound of deep learning from a kernel perspective. 1397-1406 - Jacob R. Gardner, Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, Andrew Gordon Wilson:
Product Kernel Interpolation for Scalable Gaussian Processes. 1407-1416 - Mohammadreza Soltani, Chinmay Hegde:
Towards Provable Learning of Polynomial Neural Networks Using Low-Rank Matrix Estimation. 1417-1426 - Patrick Jähnichen, Florian Wenzel, Marius Kloft, Stephan Mandt:
Scalable Generalized Dynamic Topic Models. 1427-1435 - Michael Riis Andersen, Ole Winther, Lars Kai Hansen, Russell A. Poldrack, Oluwasanmi Koyejo:
Bayesian Structure Learning for Dynamic Brain Connectivity. 1436-1446 - Mark Eisen, Aryan Mokhtari, Alejandro Ribeiro:
Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method. 1447-1455 - Gauthier Gidel, Fabian Pedregosa, Simon Lacoste-Julien:
Frank-Wolfe Splitting via Augmented Lagrangian Method. 1456-1465 - Asish Ghoshal, Jean Honorio:
Learning linear structural equation models in polynomial time and sample complexity. 1466-1475 - Jerry Chee, Panos Toulis:
Convergence diagnostics for stochastic gradient descent with constant learning rate. 1476-1485 - Asish Ghoshal, Jean Honorio:
Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity. 1486-1494 - Seung-Jean Kim, Johan Lim, Joong-Ho Won:
Nonparametric Sharpe Ratio Function Estimation in Heteroscedastic Regression Models via Convex Optimization. 1495-1504 - Brahim Khalil Abid, Robert M. Gower:
Stochastic algorithms for entropy-regularized optimal transport problems. 1505-1512 - Steffen Grünewälder:
Plug-in Estimators for Conditional Expectations and Probabilities. 1513-1521 - Francois Belletti, Alex Beutel, Sagar Jain, Ed Huai-hsin Chi:
Factorized Recurrent Neural Architectures for Longer Range Dependence. 1522-1530 - Yixi Xu, Jean Honorio, Xiao Wang:
On the Statistical Efficiency of Compositional Nonparametric Prediction. 1531-1539 - Nutan Chen, Alexej Klushyn, Richard Kurle, Xueyan Jiang, Justin Bayer, Patrick van der Smagt:
Metrics for Deep Generative Models. 1540-1550 - Marwa El Halabi, Francis R. Bach, Volkan Cevher:
Combinatorial Penalties: Which structures are preserved by convex relaxations? 1551-1560 - Stephen Mussmann, Percy Liang:
Generalized Binary Search For Split-Neighborly Problems. 1561-1569 - Jussi Viinikka, Ralf Eggeling, Mikko Koivisto:
Intersection-Validation: A Method for Evaluating Structure Learning without Ground Truth. 1570-1578 - Debdeep Pati, Anirban Bhattacharya, Yun Yang:
On Statistical Optimality of Variational Bayes. 1579-1588 - Jason Ge, Zhaoran Wang, Mengdi Wang, Han Liu:
Minimax-Optimal Privacy-Preserving Sparse PCA in Distributed Systems. 1589-1598 - Shinji Ito, Daisuke Hatano, Hanna Sumita, Akihiro Yabe, Takuro Fukunaga, Naonori Kakimura, Ken-ichi Kawarabayashi:
Online Regression with Partial Information: Generalization and Linear Projection. 1599-1607 - Aude Genevay, Gabriel Peyré, Marco Cuturi:
Learning Generative Models with Sinkhorn Divergences. 1608-1617 - Scott W. Linderman, Gonzalo E. Mena, Hal James Cooper, Liam Paninski, John P. Cunningham:
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference. 1618-1627 - Lirong Xue, Samory Kpotufe:
Achieving the time of 1-NN, but the accuracy of k-NN. 1628-1636 - Khan Mohammad Al Farabi, Somdeb Sarkhel, Deepak Venugopal:
Efficient Weight Learning in High-Dimensional Untied MLNs. 1637-1645 - Harikrishna Narasimhan:
Learning with Complex Loss Functions and Constraints. 1646-1654 - Saverio Salzo, Lorenzo Rosasco, Johan A. K. Suykens:
Solving lp-norm regularization with tensor kernels. 1655-1663 - Omer Gottesman, Weiwei Pan, Finale Doshi-Velez:
Weighted Tensor Decomposition for Learning Latent Variables with Partial Data. 1664-1672 - Eralp Turgay, Doruk Öner, Cem Tekin:
Multi-objective Contextual Bandit Problem with Similarity Information. 1673-1681 - Hong Ge, Kai Xu, Zoubin Ghahramani:
Turing: Composable inference for probabilistic programming. 1682-1690 - Beilun Wang, Arshdeep Sekhon, Yanjun Qi:
Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure. 1691-1700 - Sanket Kamthe, Marc Peter Deisenroth:
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control. 1701-1710 - Bai Jiang:
Approximate Bayesian Computation with Kullback-Leibler Divergence as Data Discrepancy. 1711-1721 - Ruben Martinez-Cantin, Kevin Tee, Michael McCourt:
Practical Bayesian optimization in the presence of outliers. 1722-1731 - Mehryar Mohri, Scott Yang:
Competing with Automata-based Expert Sequences. 1732-1740 - Devavrat Shah, Christina E. Lee:
Reducing Crowdsourcing to Graphon Estimation, Statistically. 1741-1750 - Ershad Banijamali, Rui Shu, Mohammad Ghavamzadeh, Hung Bui, Ali Ghodsi:
Robust Locally-Linear Controllable Embedding. 1751-1759 - Karthik Abinav Sankararaman, Aleksandrs Slivkins:
Combinatorial Semi-Bandits with Knapsacks. 1760-1770 - David Alvarez-Melis, Tommi S. Jaakkola, Stefanie Jegelka:
Structured Optimal Transport. 1771-1780 - Shiqing Yu, Mathias Drton, Ali Shojaie:
Graphical Models for Non-Negative Data Using Generalized Score Matching. 1781-1790 - Bin Gu, Zhouyuan Huo, Heng Huang:
Asynchronous Doubly Stochastic Group Regularized Learning. 1791-1800 - Ching-An Cheng, Byron Boots:
Convergence of Value Aggregation for Imitation Learning. 1801-1809 - Dylan J. Foster, Karthik Sridharan, Daniel Reichman:
Inference in Sparse Graphs with Pairwise Measurements and Side Information. 1810-1818 - Arkabandhu Chowdhury, Christopher M. Jermaine:
Parallel and Distributed MCMC via Shepherding Distributions. 1819-1827 - Pedro Mercado, Antoine Gautier, Francesco Tudisco, Matthias Hein:
The Power Mean Laplacian for Multilayer Graph Clustering. 1828-1838 - Sumeet Katariya, Lalit Jain, Nandana Sengupta, James Evans, Robert Nowak:
Adaptive Sampling for Coarse Ranking. 1839-1848 - Ehsan Kazemi, Lin Chen, Sanjoy Dasgupta, Amin Karbasi:
Comparison Based Learning from Weak Oracles. 1849-1858 - Xuhui Fan, Bin Li, Scott A. Sisson:
The Binary Space Partitioning-Tree Process. 1859-1867 - Mihai Cucuringu, Hemant Tyagi:
On denoising modulo 1 samples of a function. 1868-1876 - Morteza Noshad, Alfred O. Hero III:
Scalable Hash-Based Estimation of Divergence Measures. 1877-1885 - Aryan Mokhtari, Hamed Hassani, Amin Karbasi:
Conditional Gradient Method for Stochastic Submodular Maximization: Closing the Gap. 1886-1895 - Lin Chen, Hamed Hassani, Amin Karbasi:
Online Continuous Submodular Maximization. 1896-1905 - Ferdian Jovan, Jeremy L. Wyatt, Nick Hawes:
Efficient Bayesian Methods for Counting Processes in Partially Observable Environments. 1906-1913 - Michael Shvartsman, Narayanan Sundaram, Mikio Aoi, Adam Charles, Theodore L. Willke, Jonathan D. Cohen:
Matrix-normal models for fMRI analysis. 1914-1923 - Jeffrey Pennington, Samuel S. Schoenholz, Surya Ganguli:
The emergence of spectral universality in deep networks. 1924-1932 - Matt Olfat, Anil Aswani:
Spectral Algorithms for Computing Fair Support Vector Machines. 1933-1942 - He Zhao, Piyush Rai, Lan Du, Wray L. Buntine:
Bayesian Multi-label Learning with Sparse Features and Labels, and Label Co-occurrences. 1943-1951 - Rahi Kalantari, Joydeep Ghosh, Mingyuan Zhou:
Nonparametric Bayesian sparse graph linear dynamical systems. 1952-1960 - Jaan Altosaar, Rajesh Ranganath, David M. Blei:
Proximity Variational Inference. 1961-1969 - Yuxin Chen, Oisin Mac Aodha, Shihan Su, Pietro Perona, Yisong Yue:
Near-Optimal Machine Teaching via Explanatory Teaching Sets. 1970-1978 - Siddarth Srinivasan, Geoffrey J. Gordon, Byron Boots:
Learning Hidden Quantum Markov Models. 1979-1987 - Shiau Hong Lim, Gregory Calvez:
Labeled Graph Clustering via Projected Gradient Descent. 1988-1997 - Dong Yin, Ashwin Pananjady, Maximilian Lam, Dimitris S. Papailiopoulos, Kannan Ramchandran, Peter L. Bartlett:
Gradient Diversity: a Key Ingredient for Scalable Distributed Learning. 1998-2007 - Yuting Ye, Lihua Lei, Cheng Ju:
HONES: A Fast and Tuning-free Homotopy Method For Online Newton Step. 2008-2017 - Krzysztof Onak, Xiaorui Sun:
Probability-Revealing Samples. 2018-2026 - Tatsunori Hashimoto, Steve Yadlowsky, John C. Duchi:
Derivative Free Optimization Via Repeated Classification. 2027-2036 - Yanning Shen, Tianyi Chen, Georgios B. Giannakis:
Online Ensemble Multi-kernel Learning Adaptive to Non-stationary and Adversarial Environments. 2037-2046 - Chendi Huang, Yuan Yao:
A Unified Dynamic Approach to Sparse Model Selection. 2047-2055 - Costis Daskalakis, Christos Tzamos, Manolis Zampetakis:
Bootstrapping EM via Power EM and Convergence in the Naive Bayes Model. 2056-2064 - Yingzhen Yang:
Dimensionality Reduced $\ell^{0}$-Sparse Subspace Clustering. 2065-2074
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