default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCID26th AISTATS 2023: Valencia, Spain
- Francisco J. R. Ruiz, Jennifer G. Dy, Jan-Willem van de Meent:
International Conference on Artificial Intelligence and Statistics, 25-27 April 2023, Palau de Congressos, Valencia, Spain. Proceedings of Machine Learning Research 206, PMLR 2023 - Shinsaku Sakaue, Taihei Oki:
Improved Generalization Bound and Learning of Sparsity Patterns for Data-Driven Low-Rank Approximation. 1-10 - Marvin Schmitt, Stefan T. Radev, Paul-Christian Bürkner:
Meta-Uncertainty in Bayesian Model Comparison. 11-29 - Jie Shen:
PAC Learning of Halfspaces with Malicious Noise in Nearly Linear Time. 30-46 - Jia Lin Hau, Marek Petrik, Mohammad Ghavamzadeh:
Entropic Risk Optimization in Discounted MDPs. 47-76 - Elias Samuel Wirth, Thomas Kerdreux, Sebastian Pokutta:
Acceleration of Frank-Wolfe Algorithms with Open-Loop Step-Sizes. 77-100 - Lianke Qin, Zhao Song, Lichen Zhang, Danyang Zhuo:
An Online and Unified Algorithm for Projection Matrix Vector Multiplication with Application to Empirical Risk Minimization. 101-156 - Jieyu Zhang, Linxin Song, Alex Ratner:
Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision. 157-171 - Aleksandr Beznosikov, Eduard Gorbunov, Hugo Berard, Nicolas Loizou:
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods. 172-235 - Aristeidis Panos, Ioannis Kosmidis, Petros Dellaportas:
Scalable marked point processes for exchangeable and non-exchangeable event sequences. 236-252 - Martin Jankowiak:
Bayesian Variable Selection in a Million Dimensions. 253-282 - Yulai Zhao, Jianshu Chen, Simon S. Du:
Blessing of Class Diversity in Pre-training. 283-305 - Rayyan Ahmad Khan, Martin Kleinsteuber:
Barlow Graph Auto-Encoder for Unsupervised Network Embedding. 306-322 - Karim Tit, Teddy Furon, Mathias Rousset:
Gradient-Informed Neural Network Statistical Robustness Estimation. 323-334 - Andi Nika, Adish Singla, Goran Radanovic:
Online Defense Strategies for Reinforcement Learning Against Adaptive Reward Poisoning. 335-358 - Vivien Cabannes, Stefano Vigogna:
A Case of Exponential Convergence Rates for SVM. 359-374 - Ruitu Xu, Yifei Min, Tianhao Wang, Michael I. Jordan, Zhaoran Wang, Zhuoran Yang:
Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models via Reinforcement Learning. 375-407 - Simon Bartels, Kristoffer Stensbo-Smidt
, Pablo Moreno-Muñoz, Wouter Boomsma, Jes Frellsen, Søren Hauberg:
Adaptive Cholesky Gaussian Processes. 408-452 - Sing-Yuan Yeh, Fu-Chieh Chang, Chang-Wei Yueh, Pei-Yuan Wu, Alberto Bernacchia, Sattar Vakili:
Sample Complexity of Kernel-Based Q-Learning. 453-469 - Hadrien Hendrikx:
A principled framework for the design and analysis of token algorithms. 470-489 - Mohsen Heidari, Wojciech Szpankowski:
Learning k-qubit Quantum Operators via Pauli Decomposition. 490-504 - Shiwei Zeng, Jie Shen:
Semi-Verified PAC Learning from the Crowd. 505-522 - Michal Sharoni, Sivan Sabato:
On the Capacity Limits of Privileged ERM. 523-534 - Kyungsu Lee, Haeyun Lee, Jae Youn Hwang:
USIM Gate: UpSampling Module for Segmenting Precise Boundaries concerning Entropy. 535-562 - Yang Yang, Gennaro Gala, Robert Peharz:
Bayesian Structure Scores for Probabilistic Circuits. 563-575 - Tomas Geffner, Justin Domke:
Langevin Diffusion Variational Inference. 576-593 - Javad Azizi, Ofer Meshi, Masrour Zoghi, Maryam Karimzadehgan:
Overcoming Prior Misspecification in Online Learning to Rank. 594-614 - Xun Qian, Hanze Dong, Tong Zhang, Peter Richtárik:
Catalyst Acceleration of Error Compensated Methods Leads to Better Communication Complexity. 615-649 - Kei Ishikawa, Niao He:
Kernel Conditional Moment Constraints for Confounding Robust Inference. 650-674 - Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi, Yasuhiro Fujiwara:
Meta-learning for Robust Anomaly Detection. 675-691 - Yuka Hashimoto, Masahiro Ikeda, Hachem Kadri:
Learning in RKHM: a C*-Algebraic Twist for Kernel Machines. 692-708 - Sebastian Bordt, Ulrike von Luxburg:
From Shapley Values to Generalized Additive Models and back. 709-745 - Milan Kuzmanovic, Tobias Hatt, Stefan Feuerriegel:
Estimating Conditional Average Treatment Effects with Missing Treatment Information. 746-766 - Zenan Ling, Xingyu Xie, Qiuhao Wang, Zongpeng Zhang, Zhouchen Lin:
Global Convergence of Over-parameterized Deep Equilibrium Models. 767-787 - Zhaozhuo Xu, Zhao Song, Anshumali Shrivastava:
A Tale of Two Efficient Value Iteration Algorithms for Solving Linear MDPs with Large Action Space. 788-836 - Vinod Kumar Chauhan, Soheila Molaei, Marzia Hoque Tania, Anshul Thakur, Tingting Zhu, David A. Clifton:
Adversarial De-confounding in Individualised Treatment Effects Estimation. 837-849 - Tom Hess, Ron Visbord, Sivan Sabato:
Fast Distributed k-Means with a Small Number of Rounds. 850-874 - Xinwei Sun, Xiangyu Zheng, Jim Weinstein:
A New Causal Decomposition Paradigm towards Health Equity. 875-890 - Arshak Minasyan, Tigran Galstyan, Sona Hunanyan, Arnak S. Dalalyan:
Matching Map Recovery with an Unknown Number of Outliers. 891-906 - Taejin Kim, Shubhranshu Singh, Nikhil Madaan, Carlee Joe-Wong:
Characterizing Internal Evasion Attacks in Federated Learning. 907-921 - Duc Nguyen, Anderson Ye Zhang:
Optimal and Private Learning from Human Response Data. 922-958 - Samuel Stanton, Wesley J. Maddox, Andrew Gordon Wilson:
Bayesian Optimization with Conformal Prediction Sets. 959-986 - Quan Xiao, Han Shen, Wotao Yin, Tianyi Chen:
Alternating Projected SGD for Equality-constrained Bilevel Optimization. 987-1023 - Sivan Sabato:
Improved Robust Algorithms for Learning with Discriminative Feature Feedback. 1024-1036 - Giannis Nikolentzos, Michail Chatzianastasis, Michalis Vazirgiannis:
Weisfeiler and Leman go Hyperbolic: Learning Distance Preserving Node Representations. 1037-1054 - Michal Grudzien, Grigory Malinovsky, Peter Richtárik:
Can 5th Generation Local Training Methods Support Client Sampling? Yes! 1055-1092 - Raul Astudillo, Zhiyuan (Jerry) Lin, Eytan Bakshy, Peter I. Frazier:
qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization. 1093-1114 - Natraj Raman, Daniele Magazzeni, Sameena Shah:
Bayesian Hierarchical Models for Counterfactual Estimation. 1115-1128 - Xianyang Zhang, Trisha Dawn:
Sequential Gradient Descent and Quasi-Newton's Method for Change-Point Analysis. 1129-1143 - Runzhe Wan, Lin Ge, Rui Song:
Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework. 1144-1173 - Carles Domingo-Enrich, Raaz Dwivedi, Lester Mackey:
Compress Then Test: Powerful Kernel Testing in Near-linear Time. 1174-1218 - Hanni Cheng, Haosi Zheng, Ya Cong, Weihao Jiang, Shiliang Pu:
Select and Optimize: Learning to aolve large-scale TSP instances. 1219-1231 - Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan:
Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity. 1232-1300 - Ansuman Banerjee, Shayak Chakraborty, Sourav Chakraborty, Kuldeep S. Meel, Uddalok Sarkar, Sayantan Sen:
Testing of Horn Samplers. 1301-1330 - Hsin-En Su, Yen-Ju Chen, Ping-Chun Hsieh, Xi Liu:
Coordinate Ascent for Off-Policy RL with Global Convergence Guarantees. 1331-1378 - Konstantin Klemmer, Nathan S. Safir, Daniel B. Neill:
Positional Encoder Graph Neural Networks for Geographic Data. 1379-1389 - Omer Gottesman, Kavosh Asadi, Cameron S. Allen, Samuel Lobel, George Konidaris, Michael Littman:
Coarse-Grained Smoothness for Reinforcement Learning in Metric Spaces. 1390-1410 - Alexander Hägele, Jonas Rothfuss, Lars Lorch, Vignesh Ram Somnath, Bernhard Schölkopf, Andreas Krause:
BaCaDI: Bayesian Causal Discovery with Unknown Interventions. 1411-1436 - Hengchao Chen, Xiang Li, Qiang Sun:
Statistical Analysis of Karcher Means for Random Restricted PSD Matrices. 1437-1456 - Saeyoung Rho, Rachel Cummings, Vishal Misra:
Differentially Private Synthetic Control. 1457-1491 - Felix Jimenez, Matthias Katzfuss:
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes. 1492-1512 - Hongru Yang, Zhangyang Wang:
On the Neural Tangent Kernel Analysis of Randomly Pruned Neural Networks. 1513-1553 - Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao:
Riemannian Accelerated Gradient Methods via Extrapolation. 1554-1585 - Matthew J. Holland:
Flexible risk design using bi-directional dispersion. 1586-1623 - Jung-Hun Kim, Se-Young Yun, Minchan Jeong, Junhyun Nam, Jinwoo Shin, Richard Combes:
Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles. 1624-1645 - Russell Tsuchida, Cheng Soon Ong:
Deep equilibrium models as estimators for continuous latent variables. 1646-1671 - Batiste Le Bars, Aurélien Bellet, Marc Tommasi, Erick Lavoie, Anne-Marie Kermarrec:
Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Data. 1672-1702 - Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G. Bellemare:
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces. 1703-1718 - Joachim Spoerhase, Kamyar Khodamoradi, Benedikt Riegel, Bruno Ordozgoiti, Aristides Gionis:
A Constant-Factor Approximation Algorithm for Reconciliation k-Median. 1719-1746 - Samuel Holt, Alihan Hüyük, Zhaozhi Qian, Hao Sun, Mihaela van der Schaar:
Neural Laplace Control for Continuous-time Delayed Systems. 1747-1778 - Natalie Maus, Kaiwen Wu, David Eriksson, Jacob R. Gardner:
Discovering Many Diverse Solutions with Bayesian Optimization. 1779-1798 - Vitalii Bulygin, Dmytro Mykheievskyi, Kyrylo Kuchynskyi:
BlitzMask: Real-Time Instance Segmentation Approach for Mobile Devices. 1799-1811 - Jane H. Lee, Saeid Haghighatshoar, Amin Karbasi:
Exact Gradient Computation for Spiking Neural Networks via Forward Propagation. 1812-1831 - Mingshan Sun, Ye Zheng, Tianpeng Bao, Jianqiu Chen, Guoqiang Jin, Liwei Wu, Rui Zhao, Xiaoke Jiang:
Uni6Dv2: Noise Elimination for 6D Pose Estimation. 1832-1844 - Kaiyu Li, Daniel Giles, Toni Karvonen, Serge Guillas, François-Xavier Briol:
Multilevel Bayesian Quadrature. 1845-1868 - Shiv Shankar, Ritwik Sinha, Saayan Mitra, Moumita Sinha, Madalina Fiterau:
Direct Inference of Effect of Treatment (DIET) for a Cookieless World. 1869-1887 - Niklas Stoehr, Benjamin J. Radford, Ryan Cotterell, Aaron Schein:
The Ordered Matrix Dirichlet for State-Space Models. 1888-1903 - Jen Ning Lim, Sebastian J. Vollmer, Lorenz Wolf, Andrew Duncan:
Energy-Based Models for Functional Data using Path Measure Tilting. 1904-1923 - Emilio Dorigatti, Benjamin Schubert, Bernd Bischl, David Rügamer:
Frequentist Uncertainty Quantification in Semi-Structured Neural Networks. 1924-1941 - Xiangyu Sun, Oliver Schulte, Guiliang Liu, Pascal Poupart:
NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledge. 1942-1964 - Pedro Cisneros-Velarde, Boxiang Lyu, Sanmi Koyejo, Mladen Kolar:
One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning. 1965-2001 - Chengkuan Hong, Christian R. Shelton:
Variational Inference for Neyman-Scott Processes. 2002-2018 - Giannis Nikolentzos, Michalis Vazirgiannis:
Graph Alignment Kernels using Weisfeiler and Leman Hierarchies. 2019-2034 - Giannis Nikolentzos, Michalis Vazirgiannis:
Geometric Random Walk Graph Neural Networks via Implicit Layers. 2035-2053 - Shalev Shaer, Gal Maman, Yaniv Romano:
Model-X Sequential Testing for Conditional Independence via Testing by Betting. 2054-2086 - Imad Aouali, Branislav Kveton, Sumeet Katariya:
Mixed-Effect Thompson Sampling. 2087-2115 - Nelvin Tan, Ramji Venkataramanan:
Mixed Linear Regression via Approximate Message Passing. 2116-2131 - Yirui Liu, Xinghao Qiao, Liying Wang, Jessica Lam:
EEGNN: Edge Enhanced Graph Neural Network with a Bayesian Nonparametric Graph Model. 2132-2146 - Ke Bai, Pengyu Cheng, Weituo Hao, Ricardo Henao, Larry Carin:
Estimating Total Correlation with Mutual Information Estimators. 2147-2164 - Çagin Ararat, Cem Tekin:
Vector Optimization with Stochastic Bandit Feedback. 2165-2190 - Ervine Zheng, Qi Yu, Rui Li, Pengcheng Shi, Anne R. Haake:
Knowledge Acquisition for Human-In-The-Loop Image Captioning. 2191-2206 - Xiang Li, Wenhao Yang, Jiadong Liang, Zhihua Zhang, Michael I. Jordan:
A Statistical Analysis of Polyak-Ruppert Averaged Q-Learning. 2207-2261 - Ziqing Xu, Hancheng Min, Salma Tarmoun, Enrique Mallada, René Vidal:
Linear Convergence of Gradient Descent For Finite Width Over-parametrized Linear Networks With General Initialization. 2262-2284 - Jerry Chee, Hwanwoo Kim, Panos Toulis:
"Plus/minus the learning rate": Easy and Scalable Statistical Inference with SGD. 2285-2309 - Rick Presman, Jason Xu:
Distance-to-Set Priors and Constrained Bayesian Inference. 2310-2326 - Achal Awasthi, Jason Xu:
Fast Computation of Branching Process Transition Probabilities via ADMM. 2327-2347 - Junwen Yao, N. Benjamin Erichson, Miles E. Lopes:
Error Estimation for Random Fourier Features. 2348-2364 - Feihu Huang, Xidong Wu, Zhengmian Hu:
AdaGDA: Faster Adaptive Gradient Descent Ascent Methods for Minimax Optimization. 2365-2389 - Jaromír Plhák, Ondrej Sotolár, Michaela Lebedíková, David Smahel:
Classification of Adolescents' Risky Behavior in Instant Messaging Conversations. 2390-2404 - Tom Norman, Nir Weinberger, Kfir Y. Levy:
Robust Linear Regression for General Feature Distribution. 2405-2435 - Solenne Gaucher, Nicolas Schreuder, Evgenii Chzhen:
Fair learning with Wasserstein barycenters for non-decomposable performance measures. 2436-2459 - Matthias Rath, Alexandru Paul Condurache:
Deep Neural Networks with Efficient Guaranteed Invariances. 2460-2480 - Yasutoshi Ida, Sekitoshi Kanai, Atsutoshi Kumagai:
Fast Block Coordinate Descent for Non-Convex Group Regularizations. 2481-2493 - Andrea Pugnana, Salvatore Ruggieri:
AUC-based Selective Classification. 2494-2514 - Shashank Singh:
Nonparametric Indirect Active Learning. 2515-2541 - Loay Mualem, Moran Feldman:
Resolving the Approximability of Offline and Online Non-monotone DR-Submodular Maximization over General Convex Sets. 2542-2564 - Jixiang Qing, Henry B. Moss, Tom Dhaene, Ivo Couckuyt:
PF2ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimization. 2565-2588 - Hedda Cohen Indelman, Tamir Hazan:
Learning Constrained Structured Spaces with Application to Multi-Graph Matching. 2589-2602 - El-Mahdi El-Mhamdi, Sadegh Farhadkhani, Rachid Guerraoui, Lê-Nguyên Hoang:
On the Strategyproofness of the Geometric Median. 2603-2640 - Young-Geun Kim, Ying Liu, Xuexin Wei:
Covariate-informed Representation Learning to Prevent Posterior Collapse of iVAE. 2641-2660 - Lev Telyatnikov, Simone Scardapane:
EGG-GAE: scalable graph neural networks for tabular data imputation. 2661-2676 - Mengdi Xu, Peide Huang, Yaru Niu, Visak Kumar, Jielin Qiu, Chao Fang, Kuan-Hui Lee, Xuewei Qi, Henry Lam, Bo Li, Ding Zhao:
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables. 2677-2703 - Xun Zhu, Yutong Xiong, Ming Wu, Gaozhen Nie, Bin Zhang, Ziheng Yang:
Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stations. 2704-2722 - Yiling Luo, Yiling Xie, Xiaoming Huo:
Improved Rate of First Order Algorithms for Entropic Optimal Transport. 2723-2750 - Yingying Zhang, Chengchun Shi, Shikai Luo:
Conformal Off-Policy Prediction. 2751-2768 - Jeremy Sellier, Petros Dellaportas:
Sparse Spectral Bayesian Permanental Process with Generalized Kernel. 2769-2791 - Huishuai Zhang, Da Yu, Yiping Lu, Di He:
Adversarial Noises Are Linearly Separable for (Nearly) Random Neural Networks. 2792-2804 - Yassir Jedra, Junghyun Lee, Alexandre Proutière, Se-Young Yun:
Nearly Optimal Latent State Decoding in Block MDPs. 2805-2904 - Quentin Bertrand, Wojciech Marian Czarnecki, Gauthier Gidel:
On the Limitations of the Elo, Real-World Games are Transitive, not Additive. 2905-2921 - Mohsen Heidari, Wojciech Szpankowski:
Agnostic PAC Learning of k-juntas Using L2-Polynomial Regression. 2922-2938 - Zhenbang Wang, Emanuel Ben-David, Martin Slawski:
Regularization for Shuffled Data Problems via Exponential Family Priors on the Permutation Group. 2939-2959 - Luca Masserano, Tommaso Dorigo, Rafael Izbicki, Mikael Kuusela, Ann B. Lee:
Simulator-Based Inference with WALDO: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problems. 2960-2974 - Daniel Goldfarb, Paul Hand:
Analysis of Catastrophic Forgetting for Random Orthogonal Transformation Tasks in the Overparameterized Regime. 2975-2993 - Ehsan Amid, Richard Nock, Manfred K. Warmuth:
Clustering above Exponential Families with Tempered Exponential Measures. 2994-3017 - Hussein Hazimeh, Natalia Ponomareva:
Mind the (optimality) Gap: A Gap-Aware Learning Rate Scheduler for Adversarial Nets. 3018-3033 - Di He, Shanda Li, Wenlei Shi, Xiaotian Gao, Jia Zhang, Jiang Bian, Liwei Wang, Tie-Yan Liu:
Learning Physics-Informed Neural Networks without Stacked Back-propagation. 3034-3047 - Kihyuk Hong, Yuhang Li, Ambuj Tewari:
An Optimization-based Algorithm for Non-stationary Kernel Bandits without Prior Knowledge. 3048-3085 - David Simchi-Levi, Chonghuan Wang:
Multi-armed Bandit Experimental Design: Online Decision-making and Adaptive Inference. 3086-3097 - Wonyoung Kim, Myunghee Cho Paik, Min-hwan Oh:
Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits. 3098-3124 - Ziye Ma, Somayeh Sojoudi:
Noisy Low-rank Matrix Optimization: Geometry of Local Minima and Convergence Rate. 3125-3150 - Banghua Zhu, Lun Wang, Qi Pang, Shuai Wang, Jiantao Jiao, Dawn Song, Michael I. Jordan:
Byzantine-Robust Federated Learning with Optimal Statistical Rates. 3151-3178 - Yinglong Guo, Dongmian Zou, Gilad Lerman:
An Unpooling Layer for Graph Generation. 3179-3209 - Devansh Jalota, Karthik Gopalakrishnan, Navid Azizan, Ramesh Johari, Marco Pavone:
Online Learning for Traffic Routing under Unknown Preferences. 3210-3229 - Yiding Chen, Xuezhou Zhang, Kaiqing Zhang, Mengdi Wang, Xiaojin Zhu:
Byzantine-Robust Online and Offline Distributed Reinforcement Learning. 3230-3269