ML Anthology

Rakhlin, Alexander

87 publications

COLT 2025 Decision Making in Changing Environments: Robustness, Query-Based Learning, and Differential Privacy Fan Chen, Alexander Rakhlin
ICML 2025 Do We Need to Verify Step by Step? Rethinking Process Supervision from a Theoretical Perspective Zeyu Jia, Alexander Rakhlin, Tengyang Xie
ICLR 2025 Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF Tengyang Xie, Dylan J Foster, Akshay Krishnamurthy, Corby Rosset, Ahmed Hassan Awadallah, Alexander Rakhlin
ICML 2025 GaussMark: A Practical Approach for Structural Watermarking of Language Models Adam Block, Alexander Rakhlin, Ayush Sekhari
COLT 2025 On the Minimax Regret of Sequential Probability Assignment via Square-Root Entropy Zeyu Jia, Alexander Rakhlin, Yury Polyanskiy
NeurIPS 2025 Outcome-Based Online Reinforcement Learning: Algorithms and Fundamental Limits Fan Chen, Zeyu Jia, Alexander Rakhlin, Tengyang Xie
NeurIPS 2025 Trajectory Bellman Residual Minimization: A Simple Value-Based Method for LLM Reasoning Yurun Yuan, Fan Chen, Zeyu Jia, Alexander Rakhlin, Tengyang Xie
NeurIPS 2024 Assouad, Fano, and Le Cam with Interaction: A Unifying Lower Bound Framework and Characterization for Bandit Learnability Fan Chen, Dylan J. Foster, Yanjun Han, Jian Qian, Alexander Rakhlin, Yunbei Xu
NeurIPS 2024 How Does Variance Shape the Regret in Contextual Bandits? Zeyu Jia, Jian Qian, Alexander Rakhlin, Chen-Yu Wei
COLT 2024 Near-Optimal Learning and Planning in Separated Latent MDPs Fan Chen, Constantinos Daskalakis, Noah Golowich, Alexander Rakhlin
COLT 2024 Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data Zeyu Jia, Alexander Rakhlin, Ayush Sekhari, Chen-Yu Wei
COLT 2024 On the Performance of Empirical Risk Minimization with Smoothed Data Adam Block, Alexander Rakhlin, Abhishek Shetty
NeurIPS 2024 Online Estimation via Offline Estimation: An Information-Theoretic Framework Dylan J. Foster, Yanjun Han, Jian Qian, Alexander Rakhlin
ICML 2024 Random Latent Exploration for Deep Reinforcement Learning Srinath V. Mahankali, Zhang-Wei Hong, Ayush Sekhari, Alexander Rakhlin, Pulkit Agrawal
ICML 2024 The Non-Linear $f$-Design and Applications to Interactive Learning Alekh Agarwal, Jian Qian, Alexander Rakhlin, Tong Zhang
NeurIPS 2024 The Power of Resets in Online Reinforcement Learning Zakaria Mhammedi, Dylan J. Foster, Alexander Rakhlin
NeurIPS 2023 Convergence of Adam Under Relaxed Assumptions Haochuan Li, Alexander Rakhlin, Ali Jadbabaie
NeurIPS 2023 Convex and Non-Convex Optimization Under Generalized Smoothness Haochuan Li, Jian Qian, Yi Tian, Alexander Rakhlin, Ali Jadbabaie
NeurIPS 2023 Efficient Model-Free Exploration in Low-Rank MDPs Zak Mhammedi, Adam Block, Dylan J Foster, Alexander Rakhlin
NeurIPS 2023 Model-Free Reinforcement Learning with the Decision-Estimation Coefficient Dylan J Foster, Noah Golowich, Jian Qian, Alexander Rakhlin, Ayush Sekhari
COLT 2023 On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring Dean Foster, Dylan J. Foster, Noah Golowich, Alexander Rakhlin
NeurIPS 2023 On the Variance, Admissibility, and Stability of Empirical Risk Minimization Gil Kur, Eli Putterman, Alexander Rakhlin
COLT 2023 Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making Adam Block, Max Simchowitz, Alexander Rakhlin
ICML 2023 Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL Zakaria Mhammedi, Dylan J Foster, Alexander Rakhlin
NeurIPS 2023 When Is Agnostic Reinforcement Learning Statistically Tractable? Zeyu Jia, Gene Li, Alexander Rakhlin, Ayush Sekhari, Nati Srebro
ICMLW 2023 When Is Agnostic Reinforcement Learning Statistically Tractable? Gene Li, Zeyu Jia, Alexander Rakhlin, Ayush Sekhari, Nathan Srebro
COLT 2022 Damped Online Newton Step for Portfolio Selection Zakaria Mhammedi, Alexander Rakhlin
JMLR 2022 Intrinsic Dimension Estimation Using Wasserstein Distance Adam Block, Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin
NeurIPS 2022 On the Complexity of Adversarial Decision Making Dylan J Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan
COLT 2022 Smoothed Online Learning Is as Easy as Statistical Learning Adam Block, Yuval Dagan, Noah Golowich, Alexander Rakhlin
JMLR 2021 Finite Time LTI System Identification Tuhin Sarkar, Alexander Rakhlin, Munther A. Dahleh
COLT 2021 Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective Dylan Foster, Alexander Rakhlin, David Simchi-Levi, Yunzong Xu
COLT 2021 Majorizing Measures, Sequential Complexities, and Online Learning Adam Block, Yuval Dagan, Alexander Rakhlin
COLT 2021 On the Minimal Error of Empirical Risk Minimization Gil Kur, Alexander Rakhlin
ICML 2021 Top-K eXtreme Contextual Bandits with Arm Hierarchy Rajat Sen, Alexander Rakhlin, Lexing Ying, Rahul Kidambi, Dean Foster, Daniel N Hill, Inderjit S. Dhillon
ICML 2020 Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles Dylan Foster, Alexander Rakhlin
L4DC 2020 Learning Nonlinear Dynamical Systems from a Single Trajectory Dylan Foster, Tuhin Sarkar, Alexander Rakhlin
NeurIPS 2020 Learning the Linear Quadratic Regulator from Nonlinear Observations Zakaria Mhammedi, Dylan J Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford
COLT 2020 On Suboptimality of Least Squares with Application to Estimation of Convex Bodies Gil Kur, Alexander Rakhlin, Adityanand Guntuboyina
COLT 2020 On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels Tengyuan Liang, Alexander Rakhlin, Xiyu Zhai
JMLR 2020 Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information T. Tony Cai, Tengyuan Liang, Alexander Rakhlin
ICCVW 2019 Breast Tumor Cellularity Assessment Using Deep Neural Networks Alexander Rakhlin, Aleksei Tiulpin, Alexey A. Shvets, Alexandr A. Kalinin, Vladimir I. Iglovikov, Sergey I. Nikolenko
COLT 2019 Consistency of Interpolation with Laplace Kernels Is a High-Dimensional Phenomenon Alexander Rakhlin, Xiyu Zhai
AISTATS 2019 Does Data Interpolation Contradict Statistical Optimality? Mikhail Belkin, Alexander Rakhlin, Alexandre B. Tsybakov
AISTATS 2019 Fisher-Rao Metric, Geometry, and Complexity of Neural Networks Tengyuan Liang, Tomaso Poggio, Alexander Rakhlin, James Stokes
ICML 2019 Near Optimal Finite Time Identification of Arbitrary Linear Dynamical Systems Tuhin Sarkar, Alexander Rakhlin
ICMLW 2019 Using Effective Dimension to Analyze Feature Transformations in Deep Neural Networks Kavya Ravichandran, Ajay Jain, Alexander Rakhlin
CVPRW 2018 Land Cover Classification from Satellite Imagery with U-Net and Lovasz-SoftMax Loss Alexander Rakhlin, Alex Davydow, Sergey I. Nikolenko
COLT 2018 Online Learning: Sufficient Statistics and the Burkholder Method Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan
COLT 2018 Size-Independent Sample Complexity of Neural Networks Noah Golowich, Alexander Rakhlin, Ohad Shamir
AISTATS 2017 Efficient Online Multiclass Prediction on Graphs via Surrogate Losses Alexander Rakhlin, Karthik Sridharan
COLT 2017 Non-Convex Learning via Stochastic Gradient Langevin Dynamics: A Nonasymptotic Analysis Maxim Raginsky, Alexander Rakhlin, Matus Telgarsky
COLT 2017 On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities Alexander Rakhlin, Karthik Sridharan
COLT 2017 ZigZag: A New Approach to Adaptive Online Learning Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan
ICML 2016 BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits Alexander Rakhlin, Karthik Sridharan
COLT 2016 Conference on Learning Theory 2016: Preface Vitaly Feldman, Alexander Rakhlin
COLT 2016 Proceedings of the 29th Conference on Learning Theory, COLT 2016, New York, USA, June 23-26, 2016 Vitaly Feldman, Alexander Rakhlin, Ohad Shamir
NeurIPS 2015 Adaptive Online Learning Dylan J Foster, Alexander Rakhlin, Karthik Sridharan
COLT 2015 Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions Alexandre Belloni, Tengyuan Liang, Hariharan Narayanan, Alexander Rakhlin
COLT 2015 Hierarchies of Relaxations for Online Prediction Problems with Evolving Constraints Alexander Rakhlin, Karthik Sridharan
COLT 2015 Learning with Square Loss: Localization Through Offset Rademacher Complexity Tengyuan Liang, Alexander Rakhlin, Karthik Sridharan
JMLR 2015 Online Learning via Sequential Complexities Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
AISTATS 2015 Online Optimization : Competing with Dynamic Comparators Ali Jadbabaie, Alexander Rakhlin, Shahin Shahrampour, Karthik Sridharan
COLT 2014 Online Non-Parametric Regression Alexander Rakhlin, Karthik Sridharan
COLT 2013 Competing with Strategies Wei Han, Alexander Rakhlin, Karthik Sridharan
AISTATS 2013 Localization and Adaptation in Online Learning Alexander Rakhlin, Ohad Shamir, Karthik Sridharan
COLT 2013 Online Learning with Predictable Sequences Alexander Rakhlin, Karthik Sridharan
ICML 2012 Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization Alexander Rakhlin, Ohad Shamir, Karthik Sridharan
AISTATS 2012 No Internal Regret via Neighborhood Watch Dean Foster, Alexander Rakhlin
COLT 2011 Complexity-Based Approach to Calibration with Checking Rules Dean P. Foster, Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
NeurIPS 2011 Lower Bounds for Passive and Active Learning Maxim Raginsky, Alexander Rakhlin
COLT 2011 Online Learning: Beyond Regret Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
NeurIPS 2011 Online Learning: Stochastic, Constrained, and Smoothed Adversaries Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
NeurIPS 2011 Stochastic Convex Optimization with Bandit Feedback Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin
NeurIPS 2010 Online Learning: Random Averages, Combinatorial Parameters, and Learnability Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
NeurIPS 2010 Random Walk Approach to Regret Minimization Hariharan Narayanan, Alexander Rakhlin
COLT 2009 A Stochastic View of Optimal Regret Through Minimax Duality Jacob D. Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin
COLT 2009 An Efficient Bandit Algorithm for sqrt(T) Regret in Online Multiclass Prediction? Jacob D. Abernethy, Alexander Rakhlin
COLT 2009 Beating the Adaptive Bandit with High Probability Jacob D. Abernethy, Alexander Rakhlin
COLT 2008 Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization Jacob D. Abernethy, Elad Hazan, Alexander Rakhlin
COLT 2008 High-Probability Regret Bounds for Bandit Online Linear Optimization Peter L. Bartlett, Varsha Dani, Thomas P. Hayes, Sham M. Kakade, Alexander Rakhlin, Ambuj Tewari
COLT 2008 Optimal Stragies and Minimax Lower Bounds for Online Convex Games Jacob D. Abernethy, Peter L. Bartlett, Alexander Rakhlin, Ambuj Tewari
NeurIPS 2007 Adaptive Online Gradient Descent Peter L. Bartlett, Elad Hazan, Alexander Rakhlin
COLT 2007 Multitask Learning with Expert Advice Jacob D. Abernethy, Peter L. Bartlett, Alexander Rakhlin
ICML 2007 Online Discovery of Similarity Mappings Alexander Rakhlin, Jacob D. Abernethy, Peter L. Bartlett
JMLR 2006 Stability Properties of Empirical Risk Minimization over Donsker Classes Andrea Caponnetto, Alexander Rakhlin
NeurIPS 2006 Stability of $k$-Means Clustering Alexander Rakhlin, Andrea Caponnetto