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ORCID36th COLT 2023: Bangalore, India
- Gergely Neu, Lorenzo Rosasco:
The Thirty Sixth Annual Conference on Learning Theory, COLT 2023, 12-15 July 2023, Bangalore, India. Proceedings of Machine Learning Research 195, PMLR 2023 - Preface. i
- Alireza Mousavi Hosseini, Tyler K. Farghly, Ye He, Krishna Balasubramanian, Murat A. Erdogdu:
Towards a Complete Analysis of Langevin Monte Carlo: Beyond Poincaré Inequality. 1-35 - Matthew Shunshi Zhang, Sinho Chewi, Mufan (Bill) Li, Krishna Balasubramanian, Murat A. Erdogdu:
Improved Discretization Analysis for Underdamped Langevin Monte Carlo. 36-71 - Ishaq Aden-Ali, Yeshwanth Cherapanamjeri, Abhishek Shetty, Nikita Zhivotovskiy:
The One-Inclusion Graph Algorithm is not Always Optimal. 72-88 - Matthew Faw, Litu Rout, Constantine Caramanis, Sanjay Shakkottai:
Beyond Uniform Smoothness: A Stopped Analysis of Adaptive SGD. 89-160 - Bohan Wang, Huishuai Zhang, Zhiming Ma, Wei Chen:
Convergence of AdaGrad for Non-convex Objectives: Simple Proofs and Relaxed Assumptions. 161-190 - Yunwen Lei:
Stability and Generalization of Stochastic Optimization with Nonconvex and Nonsmooth Problems. 191-227 - Adam Block, Yury Polyanskiy:
The Sample Complexity of Approximate Rejection Sampling With Applications to Smoothed Online Learning. 228-273 - Angelos Assos, Idan Attias, Yuval Dagan, Constantinos Daskalakis, Maxwell K. Fishelson:
Online Learning and Solving Infinite Games with an ERM Oracle. 274-324 - Changlong Wu, Ananth Grama, Wojciech Szpankowski:
Online Learning in Dynamically Changing Environments. 325-358 - David Martínez-Rubio
, Sebastian Pokutta:
Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties. 359-393 - Sayak Ray Chowdhury, Patrick Saux, Odalric Maillard, Aditya Gopalan:
Bregman Deviations of Generic Exponential Families. 394-449 - Kasper Green Larsen:
Bagging is an Optimal PAC Learner. 450-468 - Julia Gaudio, Nirmit Joshi:
Community Detection in the Hypergraph SBM: Optimal Recovery Given the Similarity Matrix. 469-510 - Valentino Delle Rose, Alexander Kozachinskiy, Cristóbal Rojas, Tomasz Steifer:
Find a witness or shatter: the landscape of computable PAC learning. 511-524 - Han Bao:
Proper Losses, Moduli of Convexity, and Surrogate Regret Bounds. 525-547 - Rares-Darius Buhai, David Steurer:
Beyond Parallel Pancakes: Quasi-Polynomial Time Guarantees for Non-Spherical Gaussian Mixtures. 548-611 - Mohamad Kazem Shirani Faradonbeh, Mohamad Sadegh Shirani Faradonbeh:
Online Reinforcement Learning in Stochastic Continuous-Time Systems. 612-656 - Fang Kong, Canzhe Zhao, Shuai Li:
Best-of-three-worlds Analysis for Linear Bandits with Follow-the-regularized-leader Algorithm. 657-673 - Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar:
Private Online Prediction from Experts: Separations and Faster Rates. 674-699 - Weiwei Liu:
Improved Bounds for Multi-task Learning with Trace Norm Regularization. 700-714 - Doron Cohen, Aryeh Kontorovich:
Local Glivenko-Cantelli. 715 - Giacomo Greco, Maxence Noble, Giovanni Conforti, Alain Durmus:
Non-asymptotic convergence bounds for Sinkhorn iterates and their gradients: a coupling approach. 716-746 - Konstantina Bairaktari, Guy Blanc, Li-Yang Tan, Jonathan R. Ullman, Lydia Zakynthinou:
Multitask Learning via Shared Features: Algorithms and Hardness. 747-772 - Yuval Filmus, Steve Hanneke, Idan Mehalel, Shay Moran:
Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension. 773-836 - Yuzhou Gu, Yury Polyanskiy:
Uniqueness of BP fixed point for the Potts model and applications to community detection. 837-884 - Yuzhou Gu, Yury Polyanskiy:
Weak Recovery Threshold for the Hypergraph Stochastic Block Model. 885-920 - Gabriel Arpino, Ramji Venkataramanan:
Statistical-Computational Tradeoffs in Mixed Sparse Linear Regression. 921-986 - Alekh Agarwal, Yujia Jin, Tong Zhang:
VOQL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation. 987-1063 - Zongbo Bao, Penghui Yao:
On Testing and Learning Quantum Junta Channels. 1064-1094 - Nicolò Cesa-Bianchi, Tommaso Renato Cesari, Roberto Colomboni, Federico Fusco, Stefano Leonardi:
Repeated Bilateral Trade Against a Smoothed Adversary. 1095-1130 - Rémy Degenne:
On the Existence of a Complexity in Fixed Budget Bandit Identification. 1131-1154 - Weihang Xu, Simon S. Du:
Over-Parameterization Exponentially Slows Down Gradient Descent for Learning a Single Neuron. 1155-1198 - Luca Arnaboldi, Ludovic Stephan, Florent Krzakala, Bruno Loureiro:
From high-dimensional & mean-field dynamics to dimensionless ODEs: A unifying approach to SGD in two-layers networks. 1199-1227 - Sholom Schechtman, Daniil Tiapkin, Michael Muehlebach, Éric Moulines:
Orthogonal Directions Constrained Gradient Method: from non-linear equality constraints to Stiefel manifold. 1228-1258 - Dan Garber, Ben Kretzu:
Projection-free Online Exp-concave Optimization. 1259-1284 - Dirk van der Hoeven, Lukas Zierahn, Tal Lancewicki, Aviv Rosenberg, Nicolò Cesa-Bianchi:
A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial Semi-Bandits, Linear Bandits, and MDPs. 1285-1321 - Andrew J. Wagenmaker, Dylan J. Foster:
Instance-Optimality in Interactive Decision Making: Toward a Non-Asymptotic Theory. 1322-1472 - Jiaojiao Fan, Bo Yuan, Yongxin Chen:
Improved dimension dependence of a proximal algorithm for sampling. 1473-1521 - Oren Mangoubi, Nisheeth K. Vishnoi:
Private Covariance Approximation and Eigenvalue-Gap Bounds for Complex Gaussian Perturbations. 1522-1587 - Sihan Liu, Gaurav Mahajan, Daniel Kane, Shachar Lovett, Gellért Weisz, Csaba Szepesvári:
Exponential Hardness of Reinforcement Learning with Linear Function Approximation. 1588-1617 - Adam Block, Max Simchowitz, Alexander Rakhlin:
Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making. 1618-1665 - Anthimos Vardis Kandiros, Constantinos Daskalakis, Yuval Dagan, Davin Choo:
Learning and Testing Latent-Tree Ising Models Efficiently. 1666-1729 - Arun Ganesh, Abhradeep Thakurta, Jalaj Upadhyay:
Universality of Langevin Diffusion for Private Optimization, with Applications to Sampling from Rashomon Sets. 1730-1773 - Antonio Blanca, Zongchen Chen, Daniel Stefankovic, Eric Vigoda:
Complexity of High-Dimensional Identity Testing with Coordinate Conditional Sampling. 1774-1790 - Osama A. Hanna, Lin Yang, Christina Fragouli:
Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms. 1791-1821 - Omar Fawzi, Nicolas Flammarion, Aurélien Garivier, Aadil Oufkir:
Quantum Channel Certification with Incoherent Measurements. 1822-1884 - Shay Moran, Ohad Sharon, Iska Tsubari, Sivan Yosebashvili:
List Online Classification. 1885-1913 - Advait Parulekar, Liam Collins, Karthikeyan Shanmugam, Aryan Mokhtari, Sanjay Shakkottai:
InfoNCE Loss Provably Learns Cluster-Preserving Representations. 1914-1961 - Ruichen Jiang, Qiujiang Jin, Aryan Mokhtari:
Online Learning Guided Curvature Approximation: A Quasi-Newton Method with Global Non-Asymptotic Superlinear Convergence. 1962-1992 - Nikita Puchkin, Nikita Zhivotovskiy:
Exploring Local Norms in Exp-concave Statistical Learning. 1993-2013 - Gaurav Mahajan, Sham M. Kakade, Akshay Krishnamurthy, Cyril Zhang:
Learning Hidden Markov Models Using Conditional Samples. 2014-2066 - Tor Lattimore, András György:
A Second-Order Method for Stochastic Bandit Convex Optimisation. 2067-2094 - Tor Lattimore:
A Lower Bound for Linear and Kernel Regression with Adaptive Covariates. 2095-2113 - Alekh Agarwal, Yuda Song, Wen Sun, Kaiwen Wang, Mengdi Wang, Xuezhou Zhang:
Provable Benefits of Representational Transfer in Reinforcement Learning. 2114-2187 - Victor-Emmanuel Brunel:
Geodesically convex M-estimation in metric spaces. 2188-2210 - Ilias Diakonikolas, Jelena Diakonikolas, Daniel M. Kane, Puqian Wang, Nikos Zarifis:
Information-Computation Tradeoffs for Learning Margin Halfspaces with Random Classification Noise. 2211-2239 - Kyoungseok Jang, Kwang-Sung Jun, Ilja Kuzborskij, Francesco Orabona:
Tighter PAC-Bayes Bounds Through Coin-Betting. 2240-2264 - Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara:
Inference on Strongly Identified Functionals of Weakly Identified Functions. 2265 - Zijian Liu, Jiawei Zhang, Zhengyuan Zhou:
Breaking the Lower Bound with (Little) Structure: Acceleration in Non-Convex Stochastic Optimization with Heavy-Tailed Noise. 2266-2290 - Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara:
Minimax Instrumental Variable Regression and L2 Convergence Guarantees without Identification or Closedness. 2291-2318 - Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis:
SQ Lower Bounds for Learning Mixtures of Separated and Bounded Covariance Gaussians. 2319-2349 - Wenhao Li, Ningyuan Chen:
Allocating Divisible Resources on Arms with Unknown and Random Rewards. 2350-2351 - Jonathan A. Kelner, Jerry Li, Allen Liu, Aaron Sidford, Kevin Tian:
Semi-Random Sparse Recovery in Nearly-Linear Time. 2352-2398 - Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian:
Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler. 2399-2439 - Cheng Mao, Alexander S. Wein, Shenduo Zhang:
Detection-Recovery Gap for Planted Dense Cycles. 2440-2481 - Raef Bassily, Cristóbal Guzmán, Michael Menart:
Differentially Private Algorithms for the Stochastic Saddle Point Problem with Optimal Rates for the Strong Gap. 2482-2508 - Jason M. Altschuler, Kunal Talwar:
Resolving the Mixing Time of the Langevin Algorithm to its Stationary Distribution for Log-Concave Sampling. 2509-2510 - Rohith Kuditipudi, John C. Duchi, Saminul Haque:
A Pretty Fast Algorithm for Adaptive Private Mean Estimation. 2511-2551 - Emmanuel Abbe, Enric Boix Adserà, Theodor Misiakiewicz:
SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics. 2552-2623 - Jason D. Hartline, Liren Shan, Yingkai Li, Yifan Wu:
Optimal Scoring Rules for Multi-dimensional Effort. 2624-2650 - Qiwen Cui, Kaiqing Zhang, Simon S. Du:
Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation. 2651-2652 - Shinji Ito, Kei Takemura:
Best-of-Three-Worlds Linear Bandit Algorithm with Variance-Adaptive Regret Bounds. 2653-2677 - Dean P. Foster, Dylan J. Foster, Noah Golowich, Alexander Rakhlin:
On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring. 2678-2792 - Yuanhao Wang, Qinghua Liu, Yu Bai, Chi Jin:
Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation. 2793-2848 - Wai Ming Tai, Bryon Aragam:
Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures. 2849 - Wei You, Chao Qin, Zihao Wang, Shuoguang Yang:
Information-Directed Selection for Top-Two Algorithms. 2850-2851 - David Martínez-Rubio, Elias Samuel Wirth, Sebastian Pokutta:
Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond. 2852-2876 - Kefan Dong, Tengyu Ma:
Toward L_∞Recovery of Nonlinear Functions: A Polynomial Sample Complexity Bound for Gaussian Random Fields. 2877-2918 - Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Self-Directed Linear Classification. 2919-2947 - Pengyun Yue, Cong Fang, Zhouchen Lin:
On the Lower Bound of Minimizing Polyak-Łojasiewicz functions. 2948-2968 - Christopher Criscitiello, Nicolas Boumal:
Curvature and complexity: Better lower bounds for geodesically convex optimization. 2969-3013 - Daniel Kane, Ilias Diakonikolas:
A Nearly Tight Bound for Fitting an Ellipsoid to Gaussian Random Points. 3014-3028 - Stefan Tiegel:
Hardness of Agnostically Learning Halfspaces from Worst-Case Lattice Problems. 3029-3064 - Sourav Chakraborty, Eldar Fischer, Arijit Ghosh, Gopinath Mishra, Sayantan Sen:
Testing of Index-Invariant Properties in the Huge Object Model. 3065-3136 - Michal Derezinski:
Algorithmic Gaussianization through Sketching: Converting Data into Sub-gaussian Random Designs. 3137-3172 - Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro:
Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization. 3173-3228 - Ankit Pensia, Amir-Reza Asadi, Varun S. Jog, Po-Ling Loh:
Simple Binary Hypothesis Testing under Local Differential Privacy and Communication Constraints. 3229-3230 - David Gamarnik, Eren C. Kizildag, Will Perkins, Changji Xu:
Geometric Barriers for Stable and Online Algorithms for Discrepancy Minimization. 3231-3263 - Navid Ardeshir, Daniel J. Hsu, Clayton Hendrick Sanford:
Intrinsic dimensionality and generalization properties of the R-norm inductive bias. 3264-3303 - Yuanyu Wan, Lijun Zhang, Mingli Song:
Improved Dynamic Regret for Online Frank-Wolfe. 3304-3327 - Ziwei Guan, Yi Zhou, Yingbin Liang:
Online Nonconvex Optimization with Limited Instantaneous Oracle Feedback. 3328-3355 - Max Simchowitz, Abhishek Gupta, Kaiqing Zhang:
Tackling Combinatorial Distribution Shift: A Matrix Completion Perspective. 3356-3468 - Mirabel E. Reid, Santosh S. Vempala:
The k-Cap Process on Geometric Random Graphs. 3469-3509 - Zhiyuan Fan, Jian Li:
Efficient Algorithms for Sparse Moment Problems without Separation. 3510-3565 - Cynthia Dwork, Daniel Lee, Huijia Lin, Pranay Tankala:
From Pseudorandomness to Multi-Group Fairness and Back. 3566-3614 - Doudou Zhou, Hao Chen:
A new ranking scheme for modern data and its application to two-sample hypothesis testing. 3615-3668 - Maria-Luiza Vladarean, Nikita Doikov, Martin Jaggi, Nicolas Flammarion:
Linearization Algorithms for Fully Composite Optimization. 3669-3695 - Aniket Das, Dheeraj M. Nagaraj, Praneeth Netrapalli, Dheeraj Baby:
Near Optimal Heteroscedastic Regression with Symbiotic Learning. 3696-3757 - Giannis Fikioris, Éva Tardos:
Approximately Stationary Bandits with Knapsacks. 3758-3782 - Yuchen Wu, Kangjie Zhou:
Lower Bounds for the Convergence of Tensor Power Iteration on Random Overcomplete Models. 3783-3820 - Anish Agarwal, Munther A. Dahleh, Devavrat Shah, Dennis Shen:
Causal Matrix Completion. 3821-3826 - Kevin Han Huang, Xing Liu, Andrew B. Duncan, Axel Gandy:
A High-dimensional Convergence Theorem for U-statistics with Applications to Kernel-based Testing. 3827-3918 - Shuyu Liu, Florentina Bunea, Jonathan Niles-Weed:
Asymptotic confidence sets for random linear programs. 3919-3940 - Yiyun He, Roman Vershynin, Yizhe Zhu:
Algorithmically Effective Differentially Private Synthetic Data. 3941-3968 - Dylan J. Foster, Noah Golowich, Yanjun Han:
Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient. 3969-4043 - Yiding Hua, Jingqiu Ding, Tommaso d'Orsi, David Steurer:
Reaching Kesten-Stigum Threshold in the Stochastic Block Model under Node Corruptions. 4044-4071 - Aniket Das, Dheeraj M. Nagaraj, Anant Raj:
Utilising the CLT Structure in Stochastic Gradient based Sampling : Improved Analysis and Faster Algorithms. 4072-4129 - Angeliki Giannou, Shashank Rajput, Dimitris Papailiopoulos:
The Expressive Power of Tuning Only the Normalization Layers. 4130-4131 - David Bosch, Ashkan Panahi, Babak Hassibi:
Precise Asymptotic Analysis of Deep Random Feature Models. 4132-4179 - Constantinos Daskalakis, Noah Golowich, Kaiqing Zhang:
The Complexity of Markov Equilibrium in Stochastic Games. 4180-4234 - Aaron Potechin, Paxton M. Turner, Prayaag Venkat, Alexander S. Wein:
Near-optimal fitting of ellipsoids to random points. 4235-4295 - Zeyu Jia, Yury Polyanskiy, Yihong Wu:
Entropic characterization of optimal rates for learning Gaussian mixtures. 4296-4335 - Zihao Hu, Guanghui Wang, Jacob D. Abernethy:
Minimizing Dynamic Regret on Geodesic Metric Spaces. 4336-4383 - Abhishek Dhawan, Cheng Mao, Ashwin Pananjady:
Sharp analysis of EM for learning mixtures of pairwise differences. 4384-4428 - Pengyun Yue, Long Yang, Cong Fang, Zhouchen Lin:
Zeroth-order Optimization with Weak Dimension Dependency. 4429-4472 - Zakaria Mhammedi, Khashayar Gatmiry:
Quasi-Newton Steps for Efficient Online Exp-Concave Optimization. 4473-4503 - Yunbum Kook, Yin Tat Lee, Ruoqi Shen, Santosh S. Vempala:
Condition-number-independent Convergence Rate of Riemannian Hamiltonian Monte Carlo with Numerical Integrators. 4504-4569 - Michael I. Jordan, Guy Kornowski, Tianyi Lin, Ohad Shamir, Manolis Zampetakis:
Deterministic Nonsmooth Nonconvex Optimization. 4570-4597 - Zeyuan Allen-Zhu, Yuanzhi Li:
Backward Feature Correction: How Deep Learning Performs Deep (Hierarchical) Learning. 4598 - Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Thakurta:
Differentially Private and Lazy Online Convex Optimization. 4599-4632 - Aleksandrs Slivkins, Karthik Abinav Sankararaman, Dylan J. Foster:
Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression. 4633-4656 - Arnaud Descours, Tom Huix, Arnaud Guillin, Manon Michel, Éric Moulines, Boris Nectoux:
Law of Large Numbers for Bayesian two-layer Neural Network trained with Variational Inference. 4657-4695 - Moïse Blanchard, Junhui Zhang, Patrick Jaillet:
Quadratic Memory is Necessary for Optimal Query Complexity in Convex Optimization: Center-of-Mass is Pareto-Optimal. 4696-4736 - Gleb Novikov:
Sparse PCA Beyond Covariance Thresholding. 4737-4776 - Shivam Gupta, Jasper C. H. Lee, Eric Price:
Finite-Sample Symmetric Mean Estimation with Fisher Information Rate. 4777-4830 - Moses Charikar, Beidi Chen, Christopher Ré, Erik Waingarten:
Fast Algorithms for a New Relaxation of Optimal Transport. 4831-4862 - Sarah Sachs, Tim van Erven, Liam Hodgkinson, Rajiv Khanna, Umut Simsekli:
Generalization Guarantees via Algorithm-dependent Rademacher Complexity. 4863-4880 - Naren Sarayu Manoj, Nathan Srebro:
Shortest Program Interpolation Learning. 4881-4901 - Yi Li, Honghao Lin, David P. Woodruff:
ℓp-Regression in the Arbitrary Partition Model of Communication. 4902-4928 - Yutong Wang, Clayton Scott:
On Classification-Calibration of Gamma-Phi Losses. 4929-4951 - Ibrahim Issa, Amedeo Roberto Esposito, Michael Gastpar:
Asymptotically Optimal Generalization Error Bounds for Noisy, Iterative Algorithms. 4952-4976 - Heyang Zhao, Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu:
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency. 4977-5020 - Saachi Mutreja, Jonathan Shafer:
PAC Verification of Statistical Algorithms. 5021-5043 - Aymen Al Marjani, Andrea Tirinzoni, Emilie Kaufmann:
Active Coverage for PAC Reinforcement Learning. 5044-5109 - Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang:
Ticketed Learning-Unlearning Schemes. 5110-5139 - Mahdi Soltanolkotabi, Dominik Stöger, Changzhi Xie:
Implicit Balancing and Regularization: Generalization and Convergence Guarantees for Overparameterized Asymmetric Matrix Sensing. 5140-5142 - Bobby Kleinberg, Renato Paes Leme, Jon Schneider, Yifeng Teng:
U-Calibration: Forecasting for an Unknown Agent. 5143-5145 - Constantinos Daskalakis, Noah Golowich, Stratis Skoulakis, Emmanouil Zampetakis:
STay-ON-the-Ridge: Guaranteed Convergence to Local Minimax Equilibrium in Nonconvex-Nonconcave Games. 5146-5198 - Soham Jana, Yury Polyanskiy, Anzo Z. Teh, Yihong Wu:
Empirical Bayes via ERM and Rademacher complexities: the Poisson model. 5199-5235 - Loucas Pillaud-Vivien, Francis R. Bach:
Kernelized Diffusion Maps. 5236-5259 - Ilias Diakonikolas, Daniel M. Kane, Yuetian Luo, Anru Zhang:
Statistical and Computational Limits for Tensor-on-Tensor Association Detection. 5260-5310 - Ramchandran Muthukumar, Jeremias Sulam:
Sparsity-aware generalization theory for deep neural networks. 5311-5342 - Pravesh Kothari, Santosh S. Vempala, Alexander S. Wein, Jeff Xu:
Is Planted Coloring Easier than Planted Clique? 5343-5372 - Yujia Jin, Christopher Musco, Aaron Sidford, Apoorv Vikram Singh:
Moments, Random Walks, and Limits for Spectrum Approximation. 5373-5394 - Patrik R. Gerber, Yanjun Han, Yury Polyanskiy:
Minimax optimal testing by classification. 5395-5432 - Nataly Brukhim, Steve Hanneke, Shay Moran:
Improper Multiclass Boosting. 5433-5452 - Ilias Diakonikolas, Sushrut Karmalkar, Jongho Park, Christos Tzamos:
Distribution-Independent Regression for Generalized Linear Models with Oblivious Corruptions. 5453-5475 - Zihan Zhang, Qiaomin Xie:
Sharper Model-free Reinforcement Learning for Average-reward Markov Decision Processes. 5476-5477 - Xuyang Zhao, Huiyuan Wang, Wei Lin:
The Aggregation-Heterogeneity Trade-off in Federated Learning. 5478-5502 - Christoph Dann, Chen-Yu Wei, Julian Zimmert:
A Blackbox Approach to Best of Both Worlds in Bandits and Beyond. 5503-5570 - Alexandros Hollender, Emmanouil Zampetakis:
The Computational Complexity of Finding Stationary Points in Non-Convex Optimization. 5571-5572 - Elchanan Mossel, Jonathan Niles-Weed, Youngtak Sohn, Nike Sun, Ilias Zadik:
Sharp thresholds in inference of planted subgraphs. 5573-5577 - Gavin Brown, Samuel B. Hopkins, Adam Smith:
Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions. 5578-5579 - Sitan Chen, Zehao Dou, Surbhi Goel, Adam R. Klivans, Raghu Meka:
Learning Narrow One-Hidden-Layer ReLU Networks. 5580-5614 - Steve Hanneke, Shay Moran, Qian Zhang:
Universal Rates for Multiclass Learning. 5615-5681 - Steve Hanneke, Shay Moran, Vinod Raman, Unique Subedi, Ambuj Tewari:
Multiclass Online Learning and Uniform Convergence. 5682-5696 - Jaouad Mourtada, Tomas Vaskevicius, Nikita Zhivotovskiy:
Local Risk Bounds for Statistical Aggregation. 5697-5698 - Yuan Cao, Difan Zou, Yuanzhi Li, Quanquan Gu:
The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks. 5699-5753 - Bo Yuan, Jiaojiao Fan, Jiaming Liang, Andre Wibisono, Yongxin Chen:
On a Class of Gibbs Sampling over Networks. 5754-5780 - Steve Hanneke, Samory Kpotufe, Yasaman Mahdaviyeh:
Limits of Model Selection under Transfer Learning. 5781-5812 - Steve Hanneke, Liu Yang:
Bandit Learnability can be Undecidable. 5813-5849 - Guy Bresler, Tianze Jiang:
Detection-Recovery and Detection-Refutation Gaps via Reductions from Planted Clique. 5850-5889 - Olivier Bousquet, Steve Hanneke, Shay Moran, Jonathan Shafer, Ilya O. Tolstikhin:
Fine-Grained Distribution-Dependent Learning Curves. 5890-5924 - Stanislav Minsker:
Efficient median of means estimator. 5925-5933 - Doron Cohen, Aryeh Kontorovich:
Open problem: log(n) factor in "Local Glivenko-Cantelli. 5934-5936 - Manfred K. Warmuth, Ehsan Amid:
Open Problem: Learning sparse linear concepts by priming the features. 5937-5942 - Pranjal Awasthi, Nika Haghtalab, Eric Zhao:
Open Problem: The Sample Complexity of Multi-Distribution Learning for VC Classes. 5943-5949 - Christopher Criscitiello, David Martínez-Rubio, Nicolas Boumal:
Open Problem: Polynomial linearly-convergent method for g-convex optimization? 5950-5956 - Jiseok Chae, Kyuwon Kim, Donghwan Kim:
Open Problem: Is There a First-Order Method that Only Converges to Local Minimax Optima? 5957-5964
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