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