Alexander Rakhlin

Affiliations:
  • MIT, Department of Brain & Cognitive Sciences, USA
  • University of Pennsylvania, Department of Computer and Information Science (former)


According to our database1, Alexander Rakhlin authored at least 132 papers between 2006 and 2024.

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Bibliography

2024
Refined Risk Bounds for Unbounded Losses via Transductive Priors.
CoRR, 2024

How Does Variance Shape the Regret in Contextual Bandits?
CoRR, 2024

Assouad, Fano, and Le Cam with Interaction: A Unifying Lower Bound Framework and Characterization for Bandit Learnability.
CoRR, 2024

Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF.
CoRR, 2024

The Power of Resets in Online Reinforcement Learning.
CoRR, 2024

Online Estimation via Offline Estimation: An Information-Theoretic Framework.
CoRR, 2024

Random Latent Exploration for Deep Reinforcement Learning.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

The Non-linear F-Design and Applications to Interactive Learning.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data.
Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

Near-Optimal Learning and Planning in Separated Latent MDPs.
Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

On the Performance of Empirical Risk Minimization with Smoothed Data.
Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

How Far Is Too Far? Studying the Effects of Domain Discrepancy on Masked Language Models.
Proceedings of the 2024 Joint International Conference on Computational Linguistics, 2024

2023
Foundations of Reinforcement Learning and Interactive Decision Making.
CoRR, 2023

Lower Bounds for γ-Regret via the Decision-Estimation Coefficient.
CoRR, 2023

Efficient Model-Free Exploration in Low-Rank MDPs.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Convergence of Adam Under Relaxed Assumptions.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Convex and Non-convex Optimization Under Generalized Smoothness.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On the Variance, Admissibility, and Stability of Empirical Risk Minimization.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

When is Agnostic Reinforcement Learning Statistically Tractable?
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Model-Free Reinforcement Learning with the Decision-Estimation Coefficient.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL.
Proceedings of the International Conference on Machine Learning, 2023

On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022
Intrinsic Dimension Estimation Using Wasserstein Distance.
J. Mach. Learn. Res., 2022

A Note on Model-Free Reinforcement Learning with the Decision-Estimation Coefficient.
CoRR, 2022

Rate of convergence of the smoothed empirical Wasserstein distance.
CoRR, 2022

On the Complexity of Adversarial Decision Making.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Damped Online Newton Step for Portfolio Selection.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Smoothed Online Learning is as Easy as Statistical Learning.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021
Deep learning: a statistical viewpoint.
Acta Numer., May, 2021

Finite Time LTI System Identification.
J. Mach. Learn. Res., 2021

The Statistical Complexity of Interactive Decision Making.
CoRR, 2021

On Submodular Contextual Bandits.
CoRR, 2021

Intrinsic Dimension Estimation.
CoRR, 2021

Top-k eXtreme Contextual Bandits with Arm Hierarchy.
Proceedings of the 38th International Conference on Machine Learning, 2021

On the Minimal Error of Empirical Risk Minimization.
Proceedings of the Conference on Learning Theory, 2021

Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective.
Proceedings of the Conference on Learning Theory, 2021

Majorizing Measures, Sequential Complexities, and Online Learning.
Proceedings of the Conference on Learning Theory, 2021


2020
Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information.
J. Mach. Learn. Res., 2020

Fast Mixing of Multi-Scale Langevin Dynamics under the Manifold Hypothesis.
CoRR, 2020

Generative Modeling with Denoising Auto-Encoders and Langevin Sampling.
CoRR, 2020

ColocML: machine learning quantifies co-localization between mass spectrometry images.
Bioinform., 2020

Learning the Linear Quadratic Regulator from Nonlinear Observations.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning nonlinear dynamical systems from a single trajectory.
Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020

Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles.
Proceedings of the 37th International Conference on Machine Learning, 2020

On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels.
Proceedings of the Conference on Learning Theory, 2020

On Suboptimality of Least Squares with Application to Estimation of Convex Bodies.
Proceedings of the Conference on Learning Theory, 2020

2019
𝓁<sub>∞</sub> Vector Contraction for Rademacher Complexity.
CoRR, 2019

On the Risk of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels.
CoRR, 2019

Finite-Time System Identification for Partially Observed LTI Systems of Unknown Order.
CoRR, 2019

Near optimal finite time identification of arbitrary linear dynamical systems.
Proceedings of the 36th International Conference on Machine Learning, 2019

Breast Tumor Cellularity Assessment Using Deep Neural Networks.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision Workshops, 2019

Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon.
Proceedings of the Conference on Learning Theory, 2019

Nonparametric System identification of Stochastic Switched Linear Systems.
Proceedings of the 58th IEEE Conference on Decision and Control, 2019

Fisher-Rao Metric, Geometry, and Complexity of Neural Networks.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Does data interpolation contradict statistical optimality?
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
How fast can linear dynamical systems be learned?
CoRR, 2018

Just Interpolate: Kernel "Ridgeless" Regression Can Generalize.
CoRR, 2018

Theory of Deep Learning IIb: Optimization Properties of SGD.
CoRR, 2018

Paediatric Bone Age Assessment Using Deep Convolutional Neural Networks.
Proceedings of the Deep Learning in Medical Image Analysis - and - Multimodal Learning for Clinical Decision Support, 2018

Automatic Instrument Segmentation in Robot-Assisted Surgery using Deep Learning.
Proceedings of the 17th IEEE International Conference on Machine Learning and Applications, 2018

Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks.
Proceedings of the 17th IEEE International Conference on Machine Learning and Applications, 2018

Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis.
Proceedings of the Image Analysis and Recognition - 15th International Conference, 2018

Land Cover Classification From Satellite Imagery With U-Net and Lovasz-Softmax Loss.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018

Size-Independent Sample Complexity of Neural Networks.
Proceedings of the Conference On Learning Theory, 2018

Online Learning: Sufficient Statistics and the Burkholder Method.
Proceedings of the Conference On Learning Theory, 2018

2017
On Detection and Structural Reconstruction of Small-World Random Networks.
IEEE Trans. Netw. Sci. Eng., 2017

Efficient Sampling from Time-Varying Log-Concave Distributions.
J. Mach. Learn. Res., 2017

Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks.
CoRR, 2017

Multi-armed bandits in multi-agent networks.
Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities.
Proceedings of the 30th Conference on Learning Theory, 2017

Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis.
Proceedings of the 30th Conference on Learning Theory, 2017

ZigZag: A New Approach to Adaptive Online Learning.
Proceedings of the 30th Conference on Learning Theory, 2017

Efficient Online Multiclass Prediction on Graphs via Surrogate Losses.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016
Distributed Detection: Finite-Time Analysis and Impact of Network Topology.
IEEE Trans. Autom. Control., 2016

A Tutorial on Online Supervised Learning with Applications to Node Classification in Social Networks.
CoRR, 2016

Information-theoretic analysis of stability and bias of learning algorithms.
Proceedings of the 2016 IEEE Information Theory Workshop, 2016

BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Conference on Learning Theory 2016: Preface.
Proceedings of the 29th Conference on Learning Theory, 2016

Distributed estimation of dynamic parameters: Regret analysis.
Proceedings of the 2016 American Control Conference, 2016

2015
Online learning via sequential complexities.
J. Mach. Learn. Res., 2015

Online Nonparametric Regression with General Loss Functions.
CoRR, 2015

Sequential Probability Assignment with Binary Alphabets and Large Classes of Experts.
CoRR, 2015

Adaptive Online Learning.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Hierarchies of Relaxations for Online Prediction Problems with Evolving Constraints.
Proceedings of The 28th Conference on Learning Theory, 2015

Learning with Square Loss: Localization through Offset Rademacher Complexity.
Proceedings of The 28th Conference on Learning Theory, 2015

Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions.
Proceedings of The 28th Conference on Learning Theory, 2015

Finite-time analysis of the distributed detection problem.
Proceedings of the 53rd Annual Allerton Conference on Communication, 2015

Online Optimization : Competing with Dynamic Comparators.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014
Partial Monitoring - Classification, Regret Bounds, and Algorithms.
Math. Oper. Res., 2014

Online Nonparametric Regression.
CoRR, 2014

On Zeroth-Order Stochastic Convex Optimization via Random Walks.
CoRR, 2014

Online Non-Parametric Regression.
Proceedings of The 27th Conference on Learning Theory, 2014

2013
Stochastic Convex Optimization with Bandit Feedback.
SIAM J. Optim., 2013

Empirical Entropy, Minimax Regret and Minimax Risk.
CoRR, 2013

Online Learning of Dynamic Parameters in Social Networks.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Optimization, Learning, and Games with Predictable Sequences.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

On Semi-Probabilistic universal prediction.
Proceedings of the 2013 IEEE Information Theory Workshop, 2013

Online Learning with Predictable Sequences.
Proceedings of the COLT 2013, 2013

Competing With Strategies.
Proceedings of the COLT 2013, 2013

Localization and Adaptation in Online Learning.
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012
Interior-Point Methods for Full-Information and Bandit Online Learning.
IEEE Trans. Inf. Theory, 2012

Quantitative Analysis of Systems Using Game-Theoretic Learning.
ACM Trans. Embed. Comput. Syst., 2012

No Internal Regret via Neighborhood Watch.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Foreword.
J. Comput. Syst. Sci., 2012

Relax and Localize: From Value to Algorithms
CoRR, 2012

Relax and Randomize : From Value to Algorithms.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization.
Proceedings of the 29th International Conference on Machine Learning, 2012

2011
Information-Based Complexity, Feedback and Dynamics in Convex Programming.
IEEE Trans. Inf. Theory, 2011

Online Learning: Beyond Regret.
Proceedings of the COLT 2011, 2011

Complexity-Based Approach to Calibration with Checking Rules.
Proceedings of the COLT 2011, 2011

Online Learning: Stochastic and Constrained Adversaries
CoRR, 2011

Online Learning: Stochastic, Constrained, and Smoothed Adversaries.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Lower Bounds for Passive and Active Learning.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010
Information-based complexity, feedback and dynamics in sequential convex programming
CoRR, 2010

Online Learning: Random Averages, Combinatorial Parameters, and Learnability.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Random Walk Approach to Regret Minimization.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Online Convex Programming and regularization in adaptive control.
Proceedings of the 49th IEEE Conference on Decision and Control, 2010

2009
An Efficient Bandit Algorithm for sqrt(T) Regret in Online Multiclass Prediction?.
Proceedings of the COLT 2009, 2009

Beating the Adaptive Bandit with High Probability.
Proceedings of the COLT 2009, 2009

A Stochastic View of Optimal Regret through Minimax Duality.
Proceedings of the COLT 2009, 2009

Information complexity of black-box convex optimization: A new look via feedback information theory.
Proceedings of the 47th Annual Allerton Conference on Communication, 2009

2008
Game-theoretic timing analysis.
Proceedings of the 2008 International Conference on Computer-Aided Design, 2008

High-Probability Regret Bounds for Bandit Online Linear Optimization.
Proceedings of the 21st Annual Conference on Learning Theory, 2008

Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization.
Proceedings of the 21st Annual Conference on Learning Theory, 2008

Optimal Stragies and Minimax Lower Bounds for Online Convex Games.
Proceedings of the 21st Annual Conference on Learning Theory, 2008

2007
Adaptive Online Gradient Descent.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Online discovery of similarity mappings.
Proceedings of the Machine Learning, 2007

Multitask Learning with Expert Advice.
Proceedings of the Learning Theory, 20th Annual Conference on Learning Theory, 2007

2006
Stability Properties of Empirical Risk Minimization over Donsker Classes.
J. Mach. Learn. Res., 2006

Stability of $K$-Means Clustering.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006


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