Stability of $K$-Means Clustering. - Alexander Rakhlin; Andrea Caponnetto (NIPS, 2006)

Stability Properties of Empirical Risk Minimization over Donsker Classes. - Andrea Caponnetto; Alexander Rakhlin (J. Mach. Learn. Res., 2006)

Multitask Learning with Expert Advice. - Jacob D. Abernethy; Peter L. Bartlett; Alexander Rakhlin (COLT, 2007)

Online discovery of similarity mappings. - Alexander Rakhlin; Jacob D. Abernethy; Peter L. Bartlett (ICML, 2007)

Adaptive Online Gradient Descent. - Peter L. Bartlett; Elad Hazan; Alexander Rakhlin (NIPS, 2007)

Optimal Stragies and Minimax Lower Bounds for Online Convex Games. - Jacob D. Abernethy; Peter L. Bartlett; Alexander Rakhlin; Ambuj Tewari (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)

Game-theoretic timing analysis. - Sanjit A. Seshia; Alexander Rakhlin (ICCAD, 2008)

Information complexity of black-box convex optimization: A new look via feedback information theory. - Maxim Raginsky; Alexander Rakhlin (Allerton, 2009)

A Stochastic View of Optimal Regret through Minimax Duality. - Jacob D. Abernethy; Alekh Agarwal; Peter L. Bartlett; Alexander Rakhlin (COLT, 2009)

Beating the Adaptive Bandit with High Probability. - Jacob D. Abernethy; Alexander Rakhlin (COLT, 2009)

An Efficient Bandit Algorithm for sqrt(T) Regret in Online Multiclass Prediction?. - Jacob D. Abernethy; Alexander Rakhlin (COLT, 2009)

Online Convex Programming and regularization in adaptive control. - Maxim Raginsky; Alexander Rakhlin; Serdar Yüksel (CDC, 2010)

Random Walk Approach to Regret Minimization. - Hariharan Narayanan; Alexander Rakhlin (NIPS, 2010)

Online Learning: Random Averages, Combinatorial Parameters, and Learnability. - Alexander Rakhlin; Karthik Sridharan; Ambuj Tewari (NIPS, 2010)

Information-based complexity, feedback and dynamics in sequential convex programming - Maxim Raginsky; Alexander Rakhlin (CoRR, 2010)

Lower Bounds for Passive and Active Learning. - Maxim Raginsky; Alexander Rakhlin (NIPS, 2011)

Online Learning: Stochastic, Constrained, and Smoothed Adversaries. - Alexander Rakhlin; Karthik Sridharan; Ambuj Tewari (NIPS, 2011)

Online Learning: Stochastic and Constrained Adversaries - Alexander Rakhlin; Karthik Sridharan; Ambuj Tewari (CoRR, 2011)

Complexity-Based Approach to Calibration with Checking Rules. - Dean P. Foster; Alexander Rakhlin; Karthik Sridharan; Ambuj Tewari (COLT, 2011)

Online Learning: Beyond Regret. - Alexander Rakhlin; Karthik Sridharan; Ambuj Tewari (COLT, 2011)

Information-Based Complexity, Feedback and Dynamics in Convex Programming. - Maxim Raginsky; Alexander Rakhlin (IEEE Trans. Inf. Theory, 2011)

Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization. - Alexander Rakhlin; Ohad Shamir; Karthik Sridharan (ICML, 2012)

Relax and Randomize : From Value to Algorithms. - Alexander Rakhlin; Ohad Shamir; Karthik Sridharan (NIPS, 2012)

Relax and Localize: From Value to Algorithms - Alexander Rakhlin; Ohad Shamir; Karthik Sridharan (CoRR, 2012)

Foreword. - Alexander Rakhlin (J. Comput. Syst. Sci., 2012)

No Internal Regret via Neighborhood Watch. - Dean P. Foster; Alexander Rakhlin (AISTATS, 2012)

Quantitative Analysis of Systems Using Game-Theoretic Learning. - Sanjit A. Seshia; Alexander Rakhlin (ACM Trans. Embed. Comput. Syst., 2012)

Interior-Point Methods for Full-Information and Bandit Online Learning. - Jacob D. Abernethy; Elad Hazan; Alexander Rakhlin (IEEE Trans. Inf. Theory, 2012)

Localization and Adaptation in Online Learning. - Alexander Rakhlin; Ohad Shamir; Karthik Sridharan (AISTATS, 2013)

Competing With Strategies. - Wei Han; Alexander Rakhlin; Karthik Sridharan (COLT, 2013)

Online Learning with Predictable Sequences. - Alexander Rakhlin; Karthik Sridharan (COLT, 2013)

On Semi-Probabilistic universal prediction. - Alexander Rakhlin; Karthik Sridharan (ITW, 2013)

Optimization, Learning, and Games with Predictable Sequences. - Alexander Rakhlin; Karthik Sridharan (NIPS, 2013)

Online Learning of Dynamic Parameters in Social Networks. - Shahin Shahrampour; Alexander Rakhlin; Ali Jadbabaie (NIPS, 2013)

Empirical Entropy, Minimax Regret and Minimax Risk. - Alexander Rakhlin; Karthik Sridharan; Alexandre B. Tsybakov (CoRR, 2013)

Stochastic Convex Optimization with Bandit Feedback. - Alekh Agarwal; Dean P. Foster; Daniel J. Hsu; Sham M. Kakade; Alexander Rakhlin (SIAM J. Optim., 2013)

Online Non-Parametric Regression. - Alexander Rakhlin; Karthik Sridharan (COLT, 2014)

On Zeroth-Order Stochastic Convex Optimization via Random Walks. - Tengyuan Liang; Hariharan Narayanan; Alexander Rakhlin (CoRR, 2014)

Online Nonparametric Regression. - Alexander Rakhlin; Karthik Sridharan (CoRR, 2014)

Partial Monitoring - Classification, Regret Bounds, and Algorithms. - Gábor Bartók; Dean P. Foster; Dávid Pál; Alexander Rakhlin; Csaba Szepesvári (Math. Oper. Res., 2014)

Online Optimization : Competing with Dynamic Comparators. - Ali Jadbabaie; Alexander Rakhlin; Shahin Shahrampour; Karthik Sridharan (AISTATS, 2015)

Finite-time analysis of the distributed detection problem. - Shahin Shahrampour; Alexander Rakhlin; Ali Jadbabaie (Allerton, 2015)

Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions. - Alexandre Belloni; Tengyuan Liang; Hariharan Narayanan; Alexander Rakhlin (COLT, 2015)

Learning with Square Loss: Localization through Offset Rademacher Complexity. - Tengyuan Liang; Alexander Rakhlin; Karthik Sridharan (COLT, 2015)

Hierarchies of Relaxations for Online Prediction Problems with Evolving Constraints. - Alexander Rakhlin; Karthik Sridharan (COLT, 2015)

Adaptive Online Learning. - Dylan J. Foster; Alexander Rakhlin; Karthik Sridharan (NIPS, 2015)

Sequential Probability Assignment with Binary Alphabets and Large Classes of Experts. - Alexander Rakhlin; Karthik Sridharan (CoRR, 2015)

Online Nonparametric Regression with General Loss Functions. - Alexander Rakhlin; Karthik Sridharan (CoRR, 2015)

Online learning via sequential complexities. - Alexander Rakhlin; Karthik Sridharan; Ambuj Tewari (J. Mach. Learn. Res., 2015)

Distributed estimation of dynamic parameters: Regret analysis. - Shahin Shahrampour; Alexander Rakhlin; Ali Jadbabaie (ACC, 2016)

Conference on Learning Theory 2016: Preface. - Vitaly Feldman; Alexander Rakhlin (COLT, 2016)

BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits. - Alexander Rakhlin; Karthik Sridharan (ICML, 2016)

Information-theoretic analysis of stability and bias of learning algorithms. - Maxim Raginsky; Alexander Rakhlin; Matthew Tsao; Yihong Wu; Aolin Xu 001 (ITW, 2016)

A Tutorial on Online Supervised Learning with Applications to Node Classification in Social Networks. - Alexander Rakhlin; Karthik Sridharan (CoRR, 2016)

Distributed Detection: Finite-Time Analysis and Impact of Network Topology. - Shahin Shahrampour; Alexander Rakhlin; Ali Jadbabaie (IEEE Trans. Autom. Control., 2016)

Efficient Online Multiclass Prediction on Graphs via Surrogate Losses. - Alexander Rakhlin; Karthik Sridharan (AISTATS, 2017)

ZigZag: A New Approach to Adaptive Online Learning. - Dylan J. Foster; 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)

Multi-armed bandits in multi-agent networks. - Shahin Shahrampour; Alexander Rakhlin; Ali Jadbabaie (ICASSP, 2017)

Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks. - Vladimir Iglovikov; Alexander Rakhlin; Alexandr A. Kalinin; Alexey Shvets (CoRR, 2017)

Efficient Sampling from Time-Varying Log-Concave Distributions. - Hariharan Narayanan; Alexander Rakhlin (J. Mach. Learn. Res., 2017)

On Detection and Structural Reconstruction of Small-World Random Networks. - T. Tony Cai; Tengyuan Liang; Alexander Rakhlin (IEEE Trans. Netw. Sci. Eng., 2017)

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 (COLT, 2018)

Land Cover Classification From Satellite Imagery With U-Net and Lovasz-Softmax Loss. - Alexander Rakhlin; Alex Davydow; Sergey I. Nikolenko (CVPR Workshops, 2018)

Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis. - Alexander Rakhlin; Alexey Shvets; Vladimir Iglovikov; Alexandr A. Kalinin (ICIAR, 2018)

Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks. - Alexey A. Shvets; Vladimir I. Iglovikov; Alexander Rakhlin; Alexandr A. Kalinin (ICMLA, 2018)

Automatic Instrument Segmentation in Robot-Assisted Surgery using Deep Learning. - Alexey A. Shvets; Alexander Rakhlin; Alexandr A. Kalinin; Vladimir I. Iglovikov (ICMLA, 2018)

Paediatric Bone Age Assessment Using Deep Convolutional Neural Networks. - Vladimir I. Iglovikov; Alexander Rakhlin; Alexandr A. Kalinin; Alexey A. Shvets (DLMIA/ML-CDS@MICCAI, 2018)

Theory of Deep Learning IIb: Optimization Properties of SGD. - Chiyuan Zhang; Qianli Liao; Alexander Rakhlin; Brando Miranda; Noah Golowich; Tomaso A. Poggio (CoRR, 2018)

Just Interpolate: Kernel "Ridgeless" Regression Can Generalize. - Tengyuan Liang; Alexander Rakhlin (CoRR, 2018)

How fast can linear dynamical systems be learned? - Tuhin Sarkar; Alexander Rakhlin (CoRR, 2018)

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 A. Poggio; Alexander Rakhlin; James Stokes (AISTATS, 2019)

Nonparametric System identification of Stochastic Switched Linear Systems. - Tuhin Sarkar; Alexander Rakhlin; Munther A. Dahleh (CDC, 2019)

Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon. - Alexander Rakhlin; Xiyu Zhai (COLT, 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 (ICCV Workshops, 2019)

Near optimal finite time identification of arbitrary linear dynamical systems. - Tuhin Sarkar; Alexander Rakhlin (ICML, 2019)

Finite-Time System Identification for Partially Observed LTI Systems of Unknown Order. - Tuhin Sarkar; Alexander Rakhlin; Munther A. Dahleh (CoRR, 2019)

On the Risk of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels. - Tengyuan Liang; Alexander Rakhlin; Xiyu Zhai (CoRR, 2019)

𝓁∞ Vector Contraction for Rademacher Complexity. - Dylan J. Foster; Alexander Rakhlin (CoRR, 2019)

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 (COLT, 2020)

Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles. - Dylan J. Foster; Alexander Rakhlin (ICML, 2020)

Learning nonlinear dynamical systems from a single trajectory. - Dylan J. Foster; Tuhin Sarkar; Alexander Rakhlin (L4DC, 2020)

Learning the Linear Quadratic Regulator from Nonlinear Observations. - Zakaria Mhammedi; Dylan J. Foster; Max Simchowitz; Dipendra Misra; Wen Sun 002; Akshay Krishnamurthy; Alexander Rakhlin; John Langford 001 (NeurIPS, 2020)

ColocML: machine learning quantifies co-localization between mass spectrometry images. - Katja Ovchinnikova; Lachlan Stuart; Alexander Rakhlin; Sergey I. Nikolenko; Theodore Alexandrov (Bioinform., 2020)

Generative Modeling with Denoising Auto-Encoders and Langevin Sampling. - Adam Block; Youssef Mroueh; Alexander Rakhlin (CoRR, 2020)

Fast Mixing of Multi-Scale Langevin Dynamics under the Manifold Hypothesis. - Adam Block; Youssef Mroueh; Alexander Rakhlin; Jerret Ross (CoRR, 2020)

Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information. - T. Tony Cai; Tengyuan Liang; Alexander Rakhlin (J. Mach. Learn. Res., 2020)

ResNet. - Isabel Amaya-Rodriguez; Javier Civit-Masot; Francisco Luna-Perejon; Lourdes Duran-Lopez; Alexander Rakhlin; Sergey I. Nikolenko; Satoshi Kondo; Pablo Laiz; Jordi Vitrià; Santi Seguí; Patrick Brandao (Computer-Aided Analysis of Gastrointestinal Videos, 2021)

Majorizing Measures, Sequential Complexities, and Online Learning. - Adam Block; Yuval Dagan; Alexander Rakhlin (COLT, 2021)

Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective. - Dylan J. Foster; Alexander Rakhlin; David Simchi-Levi; Yunzong Xu (COLT, 2021)

On the Minimal Error of Empirical Risk Minimization. - Gil Kur; Alexander Rakhlin (COLT, 2021)

Top-k eXtreme Contextual Bandits with Arm Hierarchy. - Rajat Sen; Alexander Rakhlin; Lexing Ying; Rahul Kidambi; Dean P. Foster; Daniel N. Hill; Inderjit S. Dhillon (ICML, 2021)

Intrinsic Dimension Estimation. - Adam Block; Zeyu Jia; Yury Polyanskiy; Alexander Rakhlin (CoRR, 2021)

On Submodular Contextual Bandits. - Dean P. Foster; Alexander Rakhlin (CoRR, 2021)

The Statistical Complexity of Interactive Decision Making. - Dylan J. Foster; Sham M. Kakade; Jian Qian; Alexander Rakhlin (CoRR, 2021)

Finite Time LTI System Identification. - Tuhin Sarkar; Alexander Rakhlin; Munther A. Dahleh (J. Mach. Learn. Res., 2021)

Deep learning: a statistical viewpoint. - Peter L. Bartlett; Andrea Montanari; Alexander Rakhlin (Acta Numer., 2021)

Smoothed Online Learning is as Easy as Statistical Learning. - Adam Block; Yuval Dagan; Noah Golowich; Alexander Rakhlin (COLT, 2022)

Damped Online Newton Step for Portfolio Selection. - Zakaria Mhammedi; Alexander Rakhlin (COLT, 2022)

On the Complexity of Adversarial Decision Making. - Dylan J. Foster; Alexander Rakhlin; Ayush Sekhari; Karthik Sridharan (NeurIPS, 2022)

Rate of convergence of the smoothed empirical Wasserstein distance. - Adam Block; Zeyu Jia; Yury Polyanskiy; Alexander Rakhlin (CoRR, 2022)

A Note on Model-Free Reinforcement Learning with the Decision-Estimation Coefficient. - Dylan J. Foster; Noah Golowich; Jian Qian; Alexander Rakhlin; Ayush Sekhari (CoRR, 2022)

Intrinsic Dimension Estimation Using Wasserstein Distance. - Adam Block; Zeyu Jia; Yury Polyanskiy; Alexander Rakhlin (J. Mach. Learn. Res., 2022)

Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making. - Adam Block; Max Simchowitz; Alexander Rakhlin (COLT, 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 (COLT, 2023)

Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL. - Zakaria Mhammedi; Dylan J. Foster; Alexander Rakhlin (ICML, 2023)

Model-Free Reinforcement Learning with the Decision-Estimation Coefficient. - Dylan J. Foster; Noah Golowich; Jian Qian; Alexander Rakhlin; Ayush Sekhari (NeurIPS, 2023)

When is Agnostic Reinforcement Learning Statistically Tractable? - Zeyu Jia; Gene Li; Alexander Rakhlin; Ayush Sekhari; Nati Srebro (NeurIPS, 2023)

On the Variance, Admissibility, and Stability of Empirical Risk Minimization. - Gil Kur; Eli Putterman; Alexander Rakhlin (NeurIPS, 2023)

Convex and Non-convex Optimization Under Generalized Smoothness. - Haochuan Li; Jian Qian; Yi Tian; Alexander Rakhlin; Ali Jadbabaie (NeurIPS, 2023)

Convergence of Adam Under Relaxed Assumptions. - Haochuan Li; Alexander Rakhlin; Ali Jadbabaie (NeurIPS, 2023)

Efficient Model-Free Exploration in Low-Rank MDPs. - Zakaria Mhammedi; Adam Block; Dylan J. Foster; Alexander Rakhlin (NeurIPS, 2023)

Lower Bounds for γ-Regret via the Decision-Estimation Coefficient. - Margalit Glasgow; Alexander Rakhlin (CoRR, 2023)

Foundations of Reinforcement Learning and Interactive Decision Making. - Dylan J. Foster; Alexander Rakhlin (CoRR, 2023)

How Far Is Too Far? Studying the Effects of Domain Discrepancy on Masked Language Models. - Subhradeep Kayal; Alexander Rakhlin; Ali Dashti; Serguei Stepaniants (LREC/COLING, 2024)

On the Performance of Empirical Risk Minimization with Smoothed Data. - Adam Block; Alexander Rakhlin; Abhishek Shetty (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)

The Non-linear F-Design and Applications to Interactive Learning. - Alekh Agarwal; Jian Qian; Alexander Rakhlin; Tong Zhang 001 (ICML, 2024)

Random Latent Exploration for Deep Reinforcement Learning. - Srinath Mahankali; Zhang-Wei Hong; Ayush Sekhari; Alexander Rakhlin; Pulkit Agrawal 001 (ICML, 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)

Online Estimation via Offline Estimation: An Information-Theoretic Framework. - Dylan J. Foster; Yanjun Han; Jian Qian; Alexander Rakhlin (NeurIPS, 2024)

How Does Variance Shape the Regret in Contextual Bandits? - Zeyu Jia; Jian Qian; Alexander Rakhlin; Chen-Yu Wei (NeurIPS, 2024)

The Power of Resets in Online Reinforcement Learning. - Zakaria Mhammedi; Dylan J. Foster; Alexander Rakhlin (NeurIPS, 2024)

Refined Risk Bounds for Unbounded Losses via Transductive Priors. - Jian Qian; Alexander Rakhlin; Nikita Zhivotovskiy (CoRR, 2024)

Decision Making in Changing Environments: Robustness, Query-Based Learning, and Differential Privacy. - Fan Chen; Alexander Rakhlin (COLT, 2025)

On the Minimax Regret of Sequential Probability Assignment via Square-Root Entropy. - Zeyu Jia; Alexander Rakhlin; Yury Polyanskiy (COLT, 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 (ICLR, 2025)

GaussMark: A Practical Approach for Structural Watermarking of Language Models. - Adam Block; Ayush Sekhari; Alexander Rakhlin (CoRR, 2025)

Do We Need to Verify Step by Step? Rethinking Process Supervision from a Theoretical Perspective. - Zeyu Jia; Alexander Rakhlin; Tengyang Xie (CoRR, 2025)

Near-Optimal Private Learning in Linear Contextual Bandits. - Fan Chen; Jiachun Li; Alexander Rakhlin; David Simchi-Levi (CoRR, 2025)

Trajectory Bellman Residual Minimization: A Simple Value-Based Method for LLM Reasoning. - Yurun Yuan; Fan Chen; Zeyu Jia; Alexander Rakhlin; Tengyang Xie (CoRR, 2025)

Outcome-Based Online Reinforcement Learning: Algorithms and Fundamental Limits. - Fan Chen; Zeyu Jia; Alexander Rakhlin; Tengyang Xie (CoRR, 2025)