← Authors

Alexander Rakhlin

72 papers · 2006–2025 · 8 conferences · across top CS/AI conferences

Achievements

πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (12) πŸƒ Academic Marathon (19) 🌍 Conference Polyglot (8) 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (5) 🧭 Keyword Pioneer πŸ—ΊοΈ Taxonomy Completionist (21) 🌟 Keyword Trendsetter Combo (8) 🏠 Conference Loyalist (20) πŸ† Keyword Champion (2) πŸ”¬ Deep Specialist (12) 🌱 Topic Pioneer 🀝 Dynamic Duo (19) πŸ’Ž Century Club (72) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer ⚑ Prolific Year (10) πŸ”₯ Unstoppable (16) πŸ—ƒοΈ Keyword Collector (82) ❓ The Questioner (5)

Conferences

COLT (29) NIPS (20) ICML (9) AISTATS (6) JMLR (5) COLING (1) ICLR (1) L4DC (1)

Top co-authors

Karthik Sridharan (19) Dylan J Foster (10) Adam Block (7) Zeyu Jia (6) Jian Qian (6) Ayush Sekhari (6) Dylan J. Foster (6) Noah Golowich (5) Tengyuan Liang (5) Ambuj Tewari (5)

Research topics

Learning Paradigms (1) Optimization (1) Learning Paradigms (1)

Keywords

online learning (19) regret bound (15) sample complexity (11) empirical risk minimization (8) rademacher complexity (6) contextual bandit (6) reinforcement learning (6) function approximation (6) learning theory (6) regret minimization (5) minimax rate (5) adaptive algorithm (4) stochastic optimization (4) multi-armed bandit (4) convex optimization (4) non-convex optimization (3) system identification (3) smoothed online learning (3) statistical learning (3) information theory (3)

Papers

Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF ICLR 2025
Decision Making in Changing Environments: Robustness, Query-Based Learning, and Differential Privacy COLT 2025
GaussMark: A Practical Approach for Structural Watermarking of Language Models ICML 2025
Do We Need to Verify Step by Step? Rethinking Process Supervision from a Theoretical Perspective ICML 2025
On the Minimax Regret of Sequential Probability Assignment via Square-Root Entropy COLT 2025
Near-Optimal Learning and Planning in Separated Latent MDPs COLT 2024
On the Performance of Empirical Risk Minimization with Smoothed Data COLT 2024
Random Latent Exploration for Deep Reinforcement Learning ICML 2024
How Far Is Too Far? Studying the Effects of Domain Discrepancy on Masked Language Models COLING 2024
Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data COLT 2024
The Non-linear $F$-Design and Applications to Interactive Learning ICML 2024
The Power of Resets in Online Reinforcement Learning NIPS 2024
Online Estimation via Offline Estimation: An Information-Theoretic Framework NIPS 2024
Assouad, Fano, and Le Cam with Interaction: A Unifying Lower Bound Framework and Characterization for Bandit Learnability NIPS 2024
How Does Variance Shape the Regret in Contextual Bandits? NIPS 2024
Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making COLT 2023
Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL ICML 2023
Model-Free Reinforcement Learning with the Decision-Estimation Coefficient NIPS 2023
When is Agnostic Reinforcement Learning Statistically Tractable? NIPS 2023
On the Variance, Admissibility, and Stability of Empirical Risk Minimization NIPS 2023
Convex and Non-convex Optimization Under Generalized Smoothness NIPS 2023
Convergence of Adam Under Relaxed Assumptions NIPS 2023
Efficient Model-Free Exploration in Low-Rank MDPs NIPS 2023
On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring COLT 2023
On the Complexity of Adversarial Decision Making NIPS 2022
Smoothed Online Learning is as Easy as Statistical Learning COLT 2022
Damped Online Newton Step for Portfolio Selection COLT 2022
Intrinsic Dimension Estimation Using Wasserstein Distance JMLR 2022
Top-k eXtreme Contextual Bandits with Arm Hierarchy ICML 2021
Majorizing Measures, Sequential Complexities, and Online Learning COLT 2021
On the Minimal Error of Empirical Risk Minimization COLT 2021
Finite Time LTI System Identification JMLR 2021
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective COLT 2021
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles ICML 2020
Learning the Linear Quadratic Regulator from Nonlinear Observations NIPS 2020
On Suboptimality of Least Squares with Application to Estimation of Convex Bodies COLT 2020
On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels COLT 2020
Learning nonlinear dynamical systems from a single trajectory L4DC 2020
Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information JMLR 2020
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks AISTATS 2019
Does data interpolation contradict statistical optimality? AISTATS 2019
Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon COLT 2019
Near optimal finite time identification of arbitrary linear dynamical systems ICML 2019
Size-Independent Sample Complexity of Neural Networks COLT 2018
Online Learning: Sufficient Statistics and the Burkholder Method COLT 2018
Efficient Online Multiclass Prediction on Graphs via Surrogate Losses AISTATS 2017
ZigZag: A New Approach to Adaptive Online Learning COLT 2017
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis COLT 2017
On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities COLT 2017
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits ICML 2016
Conference on Learning Theory 2016: Preface COLT 2016
Learning with Square Loss: Localization through Offset Rademacher Complexity COLT 2015
Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions COLT 2015
Hierarchies of Relaxations for Online Prediction Problems with Evolving Constraints COLT 2015
Online Optimization : Competing with Dynamic Comparators AISTATS 2015
Adaptive Online Learning NIPS 2015
Online Learning via Sequential Complexities JMLR 2015
Online Non-Parametric Regression COLT 2014
Localization and Adaptation in Online Learning AISTATS 2013
Competing With Strategies COLT 2013
Online Learning with Predictable Sequences COLT 2013
No Internal Regret via Neighborhood Watch AISTATS 2012
Lower Bounds for Passive and Active Learning NIPS 2011
Online Learning: Beyond Regret COLT 2011
Stochastic convex optimization with bandit feedback NIPS 2011
Complexity-Based Approach to Calibration with Checking Rules COLT 2011
Online Learning: Stochastic, Constrained, and Smoothed Adversaries NIPS 2011
Random Walk Approach to Regret Minimization NIPS 2010
Online Learning: Random Averages, Combinatorial Parameters, and Learnability NIPS 2010
Adaptive Online Gradient Descent NIPS 2007
Stability Properties of Empirical Risk Minimization over Donsker Classes JMLR 2006
Stability of $K$-Means Clustering NIPS 2006