Alexander Rakhlin

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 113

Top Venues

CoRR

COLT

NeurIPS

J. Mach. Learn. Res.

Publications

Subhradeep Kayal, Alexander Rakhlin, Ali Dashti, Serguei Stepaniants
How Far Is Too Far? Studying the Effects of Domain Discrepancy on Masked Language Models. LREC/COLING (2024)
Tengyang Xie, Dylan J. Foster, Akshay Krishnamurthy, Corby Rosset, Ahmed Awadallah, Alexander Rakhlin
Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF. CoRR (2024)
Adam Block, Alexander Rakhlin, Abhishek Shetty
On the Performance of Empirical Risk Minimization with Smoothed Data. COLT (2024)
Zeyu Jia, Alexander Rakhlin, Ayush Sekhari, Chen-Yu Wei
Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data. COLT (2024)
Dylan J. Foster, Yanjun Han, Jian Qian, Alexander Rakhlin
Online Estimation via Offline Estimation: An Information-Theoretic Framework. CoRR (2024)
Fan Chen, Constantinos Daskalakis, Noah Golowich, Alexander Rakhlin
Near-Optimal Learning and Planning in Separated Latent MDPs. CoRR (2024)
Adam Block, Alexander Rakhlin, Abhishek Shetty
On the Performance of Empirical Risk Minimization with Smoothed Data. CoRR (2024)
Srinath Mahankali, Zhang-Wei Hong, Ayush Sekhari, Alexander Rakhlin, Pulkit Agrawal
Random Latent Exploration for Deep Reinforcement Learning. CoRR (2024)
Zakaria Mhammedi, Dylan J. Foster, Alexander Rakhlin
The Power of Resets in Online Reinforcement Learning. CoRR (2024)
Zeyu Jia, Alexander Rakhlin, Ayush Sekhari, Chen-Yu Wei
Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data. CoRR (2024)
Fan Chen, Constantinos Daskalakis, Noah Golowich, Alexander Rakhlin
Near-Optimal Learning and Planning in Separated Latent MDPs. COLT (2024)
Zakaria Mhammedi, Dylan J. Foster, Alexander Rakhlin
Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL. ICML (2023)
Gil Kur, Eli Putterman, Alexander Rakhlin
On the Variance, Admissibility, and Stability of Empirical Risk Minimization. CoRR (2023)
Haochuan Li, Ali Jadbabaie, Alexander Rakhlin
Convergence of Adam Under Relaxed Assumptions. CoRR (2023)
Haochuan Li, Jian Qian, Yi Tian, Alexander Rakhlin, Ali Jadbabaie
Convex and Non-convex Optimization Under Generalized Smoothness. NeurIPS (2023)
Haochuan Li, Alexander Rakhlin, Ali Jadbabaie
Convergence of Adam Under Relaxed Assumptions. NeurIPS (2023)
Zeyu Jia, Gene Li, Alexander Rakhlin, Ayush Sekhari, Nati Srebro
When is Agnostic Reinforcement Learning Statistically Tractable? NeurIPS (2023)
Adam Block, Max Simchowitz, Alexander Rakhlin
Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making. COLT (2023)
Zakaria Mhammedi, Dylan J. Foster, Alexander Rakhlin
Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL. CoRR (2023)
Dylan J. Foster, Dean P. Foster, Noah Golowich, Alexander Rakhlin
On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring. CoRR (2023)
Margalit Glasgow, Alexander Rakhlin
Lower Bounds for γ-Regret via the Decision-Estimation Coefficient. CoRR (2023)
Adam Block, Alexander Rakhlin, Max Simchowitz
Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making. CoRR (2023)
Zakaria Mhammedi, Adam Block, Dylan J. Foster, Alexander Rakhlin
Efficient Model-Free Exploration in Low-Rank MDPs. NeurIPS (2023)
Dylan J. Foster, Alexander Rakhlin
Foundations of Reinforcement Learning and Interactive Decision Making. CoRR (2023)
Zakaria Mhammedi, Adam Block, Dylan J. Foster, Alexander Rakhlin
Efficient Model-Free Exploration in Low-Rank MDPs. CoRR (2023)
Haochuan Li, Jian Qian, Yi Tian, Alexander Rakhlin, Ali Jadbabaie
Convex and Non-Convex Optimization under Generalized Smoothness. CoRR (2023)
Gil Kur, Eli Putterman, Alexander Rakhlin
On the Variance, Admissibility, and Stability of Empirical Risk Minimization. NeurIPS (2023)
Dylan J. Foster, Noah Golowich, Jian Qian, Alexander Rakhlin, Ayush Sekhari
Model-Free Reinforcement Learning with the Decision-Estimation Coefficient. NeurIPS (2023)
Dean Foster, Dylan J. Foster, Noah Golowich, Alexander Rakhlin
On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring. COLT (2023)
Zeyu Jia, Gene Li, Alexander Rakhlin, Ayush Sekhari, Nathan Srebro
When is Agnostic Reinforcement Learning Statistically Tractable? CoRR (2023)
Dylan J. Foster, Noah Golowich, Jian Qian, Alexander Rakhlin, Ayush Sekhari
A Note on Model-Free Reinforcement Learning with the Decision-Estimation Coefficient. CoRR (2022)
Dylan J. Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan
On the Complexity of Adversarial Decision Making. CoRR (2022)
Adam Block, Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin
Rate of convergence of the smoothed empirical Wasserstein distance. CoRR (2022)
Adam Block, Yuval Dagan, Noah Golowich, Alexander Rakhlin
Smoothed Online Learning is as Easy as Statistical Learning. COLT (2022)
Dylan J. Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan
On the Complexity of Adversarial Decision Making. NeurIPS (2022)
Zakaria Mhammedi, Alexander Rakhlin
Damped Online Newton Step for Portfolio Selection. COLT (2022)
Zakaria Mhammedi, Alexander Rakhlin
Damped Online Newton Step for Portfolio Selection. CoRR (2022)
Adam Block, Yuval Dagan, Noah Golowich, Alexander Rakhlin
Smoothed Online Learning is as Easy as Statistical Learning. CoRR (2022)
Adam Block, Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin
Intrinsic Dimension Estimation Using Wasserstein Distance. J. Mach. Learn. Res. 23 (2022)
Dylan J. Foster, Sham M. Kakade, Jian Qian, Alexander Rakhlin
The Statistical Complexity of Interactive Decision Making. CoRR (2021)
Tuhin Sarkar, Alexander Rakhlin, Munther A. Dahleh
Finite Time LTI System Identification. J. Mach. Learn. Res. 22 (2021)
Adam Block, Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin
Intrinsic Dimension Estimation. CoRR (2021)
Peter L. Bartlett, Andrea Montanari, Alexander Rakhlin
Deep learning: a statistical viewpoint. CoRR (2021)
Dean P. Foster, Alexander Rakhlin
On Submodular Contextual Bandits. CoRR (2021)
Dylan J. Foster, Alexander Rakhlin, David Simchi-Levi, Yunzong Xu
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective. COLT (2021)
Rajat Sen, Alexander Rakhlin, Lexing Ying, Rahul Kidambi, Dean P. Foster, Daniel N. Hill, Inderjit S. Dhillon
Top-k eXtreme Contextual Bandits with Arm Hierarchy. CoRR (2021)
Adam Block, Yuval Dagan, Alexander Rakhlin
Majorizing Measures, Sequential Complexities, and Online Learning. COLT (2021)
Peter L. Bartlett, Andrea Montanari, Alexander Rakhlin
Deep learning: a statistical viewpoint. Acta Numer. 30 (2021)
Gil Kur, Alexander Rakhlin
On the Minimal Error of Empirical Risk Minimization. CoRR (2021)
Rajat Sen, Alexander Rakhlin, Lexing Ying, Rahul Kidambi, Dean Foster, Daniel N. Hill, Inderjit S. Dhillon
Top-k eXtreme Contextual Bandits with Arm Hierarchy. ICML (2021)
Gil Kur, Alexander Rakhlin
On the Minimal Error of Empirical Risk Minimization. COLT (2021)
Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford
Learning the Linear Quadratic Regulator from Nonlinear Observations. CoRR (2020)
Dylan J. Foster, Alexander Rakhlin, David Simchi-Levi, Yunzong Xu
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective. CoRR (2020)
Gil Kur, Alexander Rakhlin, Adityanand Guntuboyina
On Suboptimality of Least Squares with Application to Estimation of Convex Bodies. COLT (2020)
Katja Ovchinnikova, Lachlan Stuart, Alexander Rakhlin, Sergey I. Nikolenko, Theodore Alexandrov
ColocML: machine learning quantifies co-localization between mass spectrometry images. Bioinform. 36 (10) (2020)
Gil Kur, Alexander Rakhlin, Adityanand Guntuboyina
On Suboptimality of Least Squares with Application to Estimation of Convex Bodies. CoRR (2020)
Dylan J. Foster, Alexander Rakhlin
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles. ICML (2020)
T. Tony Cai, Tengyuan Liang, Alexander Rakhlin
Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information. J. Mach. Learn. Res. 21 (2020)
Dylan J. Foster, Alexander Rakhlin, Tuhin Sarkar
Learning nonlinear dynamical systems from a single trajectory. CoRR (2020)
Dylan J. Foster, Tuhin Sarkar, Alexander Rakhlin
Learning nonlinear dynamical systems from a single trajectory. L4DC (2020)
Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford
Learning the Linear Quadratic Regulator from Nonlinear Observations. NeurIPS (2020)
Tengyuan Liang, Alexander Rakhlin, Xiyu Zhai
On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels. COLT (2020)
Dylan J. Foster, Alexander Rakhlin
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles. CoRR (2020)
Adam Block, Youssef Mroueh, Alexander Rakhlin
Generative Modeling with Denoising Auto-Encoders and Langevin Sampling. CoRR (2020)
Adam Block, Youssef Mroueh, Alexander Rakhlin, Jerret Ross
Fast Mixing of Multi-Scale Langevin Dynamics under the Manifold Hypothesis. CoRR (2020)
Tuhin Sarkar, Alexander Rakhlin, Munther A. Dahleh
Nonparametric System identification of Stochastic Switched Linear Systems. CoRR (2019)
Tuhin Sarkar, Alexander Rakhlin, Munther A. Dahleh
Nonparametric System identification of Stochastic Switched Linear Systems. CDC (2019)
Mikhail Belkin, Alexander Rakhlin, Alexandre B. Tsybakov
Does data interpolation contradict statistical optimality? AISTATS (2019)
Tengyuan Liang, Tomaso A. Poggio, Alexander Rakhlin, James Stokes
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. AISTATS (2019)
Alexander Rakhlin, Xiyu Zhai
Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon. COLT (2019)
Alexander Rakhlin, Alexey A. Shvets, Alexandr A. Kalinin, Aleksei Tiulpin, Vladimir I. Iglovikov, Sergey I. Nikolenko
Breast Tumor Cellularity Assessment using Deep Neural Networks. CoRR (2019)
Tuhin Sarkar, Alexander Rakhlin
Near optimal finite time identification of arbitrary linear dynamical systems. ICML (2019)
Dylan J. Foster, Alexander Rakhlin
Vector Contraction for Rademacher Complexity. CoRR (2019)
Tuhin Sarkar, Alexander Rakhlin, Munther A. Dahleh
Finite-Time System Identification for Partially Observed LTI Systems of Unknown Order. CoRR (2019)
Tengyuan Liang, Alexander Rakhlin, Xiyu Zhai
On the Risk of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels. CoRR (2019)
Alexander Rakhlin, Aleksei Tiulpin, Alexey A. Shvets, Alexandr A. Kalinin, Vladimir I. Iglovikov, Sergey I. Nikolenko
Breast Tumor Cellularity Assessment Using Deep Neural Networks. ICCV Workshops (2019)
Chiyuan Zhang, Qianli Liao, Alexander Rakhlin, Brando Miranda, Noah Golowich, Tomaso A. Poggio
Theory of Deep Learning IIb: Optimization Properties of SGD. CoRR (2018)
Mikhail Belkin, Alexander Rakhlin, Alexandre B. Tsybakov
Does data interpolation contradict statistical optimality? CoRR (2018)
Alexey Shvets, Vladimir Iglovikov, Alexander Rakhlin, Alexandr A. Kalinin
Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks. CoRR (2018)
Noah Golowich, Alexander Rakhlin, Ohad Shamir
Size-Independent Sample Complexity of Neural Networks. COLT (2018)
Alexey A. Shvets, Alexander Rakhlin, Alexandr A. Kalinin, Vladimir I. Iglovikov
Automatic Instrument Segmentation in Robot-Assisted Surgery using Deep Learning. ICMLA (2018)
Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan
Online Learning: Sufficient Statistics and the Burkholder Method. COLT (2018)
Tuhin Sarkar, Alexander Rakhlin
How fast can linear dynamical systems be learned? CoRR (2018)
Vladimir I. Iglovikov, Alexander Rakhlin, Alexandr A. Kalinin, Alexey A. Shvets
Paediatric Bone Age Assessment Using Deep Convolutional Neural Networks. DLMIA/ML-CDS@MICCAI (2018)
Alexander Rakhlin, Xiyu Zhai
Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon. CoRR (2018)
Alexander Rakhlin, Alexey Shvets, Vladimir Iglovikov, Alexandr A. Kalinin
Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis. ICIAR (2018)
Alexey Shvets, Alexander Rakhlin, Alexandr A. Kalinin, Vladimir Iglovikov
Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning. CoRR (2018)
Alexander Rakhlin, Alex Davydow, Sergey I. Nikolenko
Land Cover Classification From Satellite Imagery With U-Net and Lovasz-Softmax Loss. CVPR Workshops (2018)
Tengyuan Liang, Alexander Rakhlin
Just Interpolate: Kernel "Ridgeless" Regression Can Generalize. CoRR (2018)
Alexander Rakhlin, Alexey Shvets, Vladimir Iglovikov, Alexandr A. Kalinin
Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis. CoRR (2018)
Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan
Online Learning: Sufficient Statistics and the Burkholder Method. CoRR (2018)
Alexey A. Shvets, Vladimir I. Iglovikov, Alexander Rakhlin, Alexandr A. Kalinin
Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks. ICMLA (2018)
Alexander Rakhlin, Karthik Sridharan
Efficient Online Multiclass Prediction on Graphs via Surrogate Losses. AISTATS (2017)
Maxim Raginsky, Alexander Rakhlin, Matus Telgarsky
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis. COLT (2017)
Maxim Raginsky, Alexander Rakhlin, Matus Telgarsky
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis. CoRR (2017)
T. Tony Cai, Tengyuan Liang, Alexander Rakhlin
On Detection and Structural Reconstruction of Small-World Random Networks. IEEE Trans. Netw. Sci. Eng. 4 (3) (2017)
Noah Golowich, Alexander Rakhlin, Ohad Shamir
Size-Independent Sample Complexity of Neural Networks. CoRR (2017)
Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan
ZigZag: A New Approach to Adaptive Online Learning. COLT (2017)
Vladimir Iglovikov, Alexander Rakhlin, Alexandr A. Kalinin, Alexey Shvets
Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks. CoRR (2017)
Tengyuan Liang, Tomaso A. Poggio, Alexander Rakhlin, James Stokes
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. CoRR (2017)

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Publication Activity (10 Years)

Top Topics

Top Venues

Alexander Rakhlin