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ORCIDAlexander Rakhlin
Sasha Rakhlin
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- affiliation: MIT, Department of Brain & Cognitive Sciences, USA
- affiliation (former): University of Pennsylvania, Department of Computer and Information Science
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2020 – today
- 2024
[c80]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: 8192-8199- [i81]Adam Block, Alexander Rakhlin, Abhishek Shetty:
On the Performance of Empirical Risk Minimization with Smoothed Data. CoRR abs/2402.14987 (2024) - [i80]Zeyu Jia, Alexander Rakhlin, Ayush Sekhari, Chen-Yu Wei:
Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data. CoRR abs/2403.17091 (2024) - [i79]Dylan J. Foster, Yanjun Han, Jian Qian, Alexander Rakhlin:
Online Estimation via Offline Estimation: An Information-Theoretic Framework. CoRR abs/2404.10122 (2024) - [i78]Zakaria Mhammedi, Dylan J. Foster, Alexander Rakhlin:
The Power of Resets in Online Reinforcement Learning. CoRR abs/2404.15417 (2024) - 2023
- [c79]Adam Block, Max Simchowitz, Alexander Rakhlin:
Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making. COLT 2023: 1618-1665 - [c78]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: 2678-2792 - [c77]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: 24659-24700 - [c76]Dylan J. Foster, Noah Golowich, Jian Qian, Alexander Rakhlin, Ayush Sekhari:
Model-Free Reinforcement Learning with the Decision-Estimation Coefficient. NeurIPS 2023 - [c75]Zeyu Jia, Gene Li, Alexander Rakhlin, Ayush Sekhari, Nati Srebro:
When is Agnostic Reinforcement Learning Statistically Tractable? NeurIPS 2023 - [c74]Gil Kur, Eli Putterman, Alexander Rakhlin:
On the Variance, Admissibility, and Stability of Empirical Risk Minimization. NeurIPS 2023 - [c73]Haochuan Li, Jian Qian, Yi Tian, Alexander Rakhlin, Ali Jadbabaie:
Convex and Non-convex Optimization Under Generalized Smoothness. NeurIPS 2023 - [c72]Haochuan Li, Alexander Rakhlin, Ali Jadbabaie:
Convergence of Adam Under Relaxed Assumptions. NeurIPS 2023 - [c71]Zakaria Mhammedi, Adam Block, Dylan J. Foster, Alexander Rakhlin:
Efficient Model-Free Exploration in Low-Rank MDPs. NeurIPS 2023 - [i77]Adam Block, Alexander Rakhlin, Max Simchowitz:
Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making. CoRR abs/2302.05430 (2023) - [i76]Margalit Glasgow, Alexander Rakhlin:
Lower Bounds for γ-Regret via the Decision-Estimation Coefficient. CoRR abs/2303.03327 (2023) - [i75]Zakaria Mhammedi, Dylan J. Foster, Alexander Rakhlin:
Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL. CoRR abs/2304.05889 (2023) - [i74]Haochuan Li, Ali Jadbabaie, Alexander Rakhlin:
Convergence of Adam Under Relaxed Assumptions. CoRR abs/2304.13972 (2023) - [i73]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 abs/2305.00684 (2023) - [i72]Gil Kur, Eli Putterman, Alexander Rakhlin:
On the Variance, Admissibility, and Stability of Empirical Risk Minimization. CoRR abs/2305.18508 (2023) - [i71]Haochuan Li, Jian Qian, Yi Tian, Alexander Rakhlin, Ali Jadbabaie:
Convex and Non-Convex Optimization under Generalized Smoothness. CoRR abs/2306.01264 (2023) - [i70]Zakaria Mhammedi, Adam Block, Dylan J. Foster, Alexander Rakhlin:
Efficient Model-Free Exploration in Low-Rank MDPs. CoRR abs/2307.03997 (2023) - [i69]Zeyu Jia, Gene Li, Alexander Rakhlin, Ayush Sekhari, Nathan Srebro:
When is Agnostic Reinforcement Learning Statistically Tractable? CoRR abs/2310.06113 (2023) - [i68]Dylan J. Foster, Alexander Rakhlin:
Foundations of Reinforcement Learning and Interactive Decision Making. CoRR abs/2312.16730 (2023) - 2022
- [j16]Adam Block, Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin:
Intrinsic Dimension Estimation Using Wasserstein Distance. J. Mach. Learn. Res. 23: 313:1-313:37 (2022) - [c70]Adam Block, Yuval Dagan, Noah Golowich, Alexander Rakhlin:
Smoothed Online Learning is as Easy as Statistical Learning. COLT 2022: 1716-1786 - [c69]Zakaria Mhammedi, Alexander Rakhlin:
Damped Online Newton Step for Portfolio Selection. COLT 2022: 5561-5595 - [c68]Dylan J. Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan:
On the Complexity of Adversarial Decision Making. NeurIPS 2022 - [i67]Adam Block, Yuval Dagan, Noah Golowich, Alexander Rakhlin:
Smoothed Online Learning is as Easy as Statistical Learning. CoRR abs/2202.04690 (2022) - [i66]Zakaria Mhammedi, Alexander Rakhlin:
Damped Online Newton Step for Portfolio Selection. CoRR abs/2202.07574 (2022) - [i65]Adam Block, Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin:
Rate of convergence of the smoothed empirical Wasserstein distance. CoRR abs/2205.02128 (2022) - [i64]Dylan J. Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan:
On the Complexity of Adversarial Decision Making. CoRR abs/2206.13063 (2022) - [i63]Dylan J. Foster, Noah Golowich, Jian Qian, Alexander Rakhlin, Ayush Sekhari:
A Note on Model-Free Reinforcement Learning with the Decision-Estimation Coefficient. CoRR abs/2211.14250 (2022) - 2021
- [j15]Peter L. Bartlett, Andrea Montanari, Alexander Rakhlin:
Deep learning: a statistical viewpoint. Acta Numer. 30: 87-201 (2021) - [j14]Tuhin Sarkar, Alexander Rakhlin, Munther A. Dahleh:
Finite Time LTI System Identification. J. Mach. Learn. Res. 22: 26:1-26:61 (2021) - [c67]Adam Block, Yuval Dagan, Alexander Rakhlin:
Majorizing Measures, Sequential Complexities, and Online Learning. COLT 2021: 587-590 - [c66]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: 2059 - [c65]Gil Kur, Alexander Rakhlin:
On the Minimal Error of Empirical Risk Minimization. COLT 2021: 2849-2852 - [c64]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: 9422-9433 - [p1]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:
ResNet. Computer-Aided Analysis of Gastrointestinal Videos 2021: 99-114 - [i62]Adam Block, Yuval Dagan, Sasha Rakhlin:
Majorizing Measures, Sequential Complexities, and Online Learning. CoRR abs/2102.01729 (2021) - [i61]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 abs/2102.07800 (2021) - [i60]Gil Kur, Alexander Rakhlin:
On the Minimal Error of Empirical Risk Minimization. CoRR abs/2102.12066 (2021) - [i59]Peter L. Bartlett, Andrea Montanari, Alexander Rakhlin:
Deep learning: a statistical viewpoint. CoRR abs/2103.09177 (2021) - [i58]Adam Block, Zeyu Jia, Yury Polyanskiy, Alexander Rakhlin:
Intrinsic Dimension Estimation. CoRR abs/2106.04018 (2021) - [i57]Dean P. Foster, Alexander Rakhlin:
On Submodular Contextual Bandits. CoRR abs/2112.02165 (2021) - [i56]Dylan J. Foster, Sham M. Kakade, Jian Qian, Alexander Rakhlin:
The Statistical Complexity of Interactive Decision Making. CoRR abs/2112.13487 (2021) - 2020
- [j13]Katja Ovchinnikova, Lachlan Stuart, Alexander Rakhlin, Sergey I. Nikolenko
, Theodore Alexandrov:
ColocML: machine learning quantifies co-localization between mass spectrometry images. Bioinform. 36(10): 3215-3224 (2020) - [j12]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: 11:1-11:34 (2020) - [c63]Gil Kur, Alexander Rakhlin, Adityanand Guntuboyina:
On Suboptimality of Least Squares with Application to Estimation of Convex Bodies. COLT 2020: 2406-2424 - [c62]Tengyuan Liang, Alexander Rakhlin, Xiyu Zhai:
On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels. COLT 2020: 2683-2711 - [c61]Dylan J. Foster, Alexander Rakhlin:
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles. ICML 2020: 3199-3210 - [c60]Dylan J. Foster, Tuhin Sarkar, Alexander Rakhlin:
Learning nonlinear dynamical systems from a single trajectory. L4DC 2020: 851-861 - [c59]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 - [i55]Adam Block, Youssef Mroueh, Alexander Rakhlin:
Generative Modeling with Denoising Auto-Encoders and Langevin Sampling. CoRR abs/2002.00107 (2020) - [i54]Dylan J. Foster, Alexander Rakhlin:
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles. CoRR abs/2002.04926 (2020) - [i53]Dylan J. Foster, Alexander Rakhlin, Tuhin Sarkar:
Learning nonlinear dynamical systems from a single trajectory. CoRR abs/2004.14681 (2020) - [i52]Gil Kur, Alexander Rakhlin, Adityanand Guntuboyina:
On Suboptimality of Least Squares with Application to Estimation of Convex Bodies. CoRR abs/2006.04046 (2020) - [i51]Adam Block, Youssef Mroueh, Alexander Rakhlin, Jerret Ross:
Fast Mixing of Multi-Scale Langevin Dynamics under the Manifold Hypothesis. CoRR abs/2006.11166 (2020) - [i50]Dylan J. Foster, Alexander Rakhlin, David Simchi-Levi, Yunzong Xu:
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective. CoRR abs/2010.03104 (2020) - [i49]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 abs/2010.03799 (2020)
2010 – 2019
- 2019
- [c58]Tengyuan Liang, Tomaso A. Poggio, Alexander Rakhlin, James Stokes:
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. AISTATS 2019: 888-896 - [c57]Mikhail Belkin, Alexander Rakhlin, Alexandre B. Tsybakov:
Does data interpolation contradict statistical optimality? AISTATS 2019: 1611-1619 - [c56]Tuhin Sarkar, Alexander Rakhlin, Munther A. Dahleh:
Nonparametric System identification of Stochastic Switched Linear Systems. CDC 2019: 3623-3628 - [c55]Alexander Rakhlin, Xiyu Zhai:
Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon. COLT 2019: 2595-2623 - [c54]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: 371-380 - [c53]Tuhin Sarkar, Alexander Rakhlin:
Near optimal finite time identification of arbitrary linear dynamical systems. ICML 2019: 5610-5618 - [i48]Tuhin Sarkar, Alexander Rakhlin, Munther A. Dahleh:
Finite-Time System Identification for Partially Observed LTI Systems of Unknown Order. CoRR abs/1902.01848 (2019) - [i47]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 abs/1905.01743 (2019) - [i46]Tengyuan Liang, Alexander Rakhlin, Xiyu Zhai:
On the Risk of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels. CoRR abs/1908.10292 (2019) - [i45]Tuhin Sarkar, Alexander Rakhlin, Munther A. Dahleh:
Nonparametric System identification of Stochastic Switched Linear Systems. CoRR abs/1909.04617 (2019) - [i44]Dylan J. Foster, Alexander Rakhlin:
𝓁∞ Vector Contraction for Rademacher Complexity. CoRR abs/1911.06468 (2019) - 2018
- [c52]Noah Golowich, Alexander Rakhlin, Ohad Shamir:
Size-Independent Sample Complexity of Neural Networks. COLT 2018: 297-299 - [c51]Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan:
Online Learning: Sufficient Statistics and the Burkholder Method. COLT 2018: 3028-3064 - [c50]Alexander Rakhlin, Alex Davydow, Sergey I. Nikolenko:
Land Cover Classification From Satellite Imagery With U-Net and Lovasz-Softmax Loss. CVPR Workshops 2018: 262-266 - [c49]Alexander Rakhlin, Alexey Shvets, Vladimir Iglovikov, Alexandr A. Kalinin:
Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis. ICIAR 2018: 737-744 - [c48]Alexey A. Shvets, Vladimir I. Iglovikov, Alexander Rakhlin, Alexandr A. Kalinin:
Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks. ICMLA 2018: 612-617 - [c47]Alexey A. Shvets, Alexander Rakhlin, Alexandr A. Kalinin, Vladimir I. Iglovikov:
Automatic Instrument Segmentation in Robot-Assisted Surgery using Deep Learning. ICMLA 2018: 624-628 - [c46]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: 300-308 - [i43]Chiyuan Zhang, Qianli Liao, Alexander Rakhlin, Brando Miranda, Noah Golowich, Tomaso A. Poggio:
Theory of Deep Learning IIb: Optimization Properties of SGD. CoRR abs/1801.02254 (2018) - [i42]Alexander Rakhlin, Alexey Shvets, Vladimir Iglovikov, Alexandr A. Kalinin:
Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis. CoRR abs/1802.00752 (2018) - [i41]Alexey Shvets, Alexander Rakhlin, Alexandr A. Kalinin, Vladimir Iglovikov:
Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning. CoRR abs/1803.01207 (2018) - [i40]Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan:
Online Learning: Sufficient Statistics and the Burkholder Method. CoRR abs/1803.07617 (2018) - [i39]Alexey Shvets, Vladimir Iglovikov, Alexander Rakhlin, Alexandr A. Kalinin:
Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks. CoRR abs/1804.08024 (2018) - [i38]Mikhail Belkin, Alexander Rakhlin, Alexandre B. Tsybakov:
Does data interpolation contradict statistical optimality? CoRR abs/1806.09471 (2018) - [i37]Tengyuan Liang, Alexander Rakhlin:
Just Interpolate: Kernel "Ridgeless" Regression Can Generalize. CoRR abs/1808.00387 (2018) - [i36]Tuhin Sarkar, Alexander Rakhlin:
How fast can linear dynamical systems be learned? CoRR abs/1812.01251 (2018) - [i35]Alexander Rakhlin, Xiyu Zhai:
Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon. CoRR abs/1812.11167 (2018) - 2017
- [j11]Hariharan Narayanan, Alexander Rakhlin:
Efficient Sampling from Time-Varying Log-Concave Distributions. J. Mach. Learn. Res. 18: 112:1-112:29 (2017) - [j10]T. Tony Cai, Tengyuan Liang, Alexander Rakhlin:
On Detection and Structural Reconstruction of Small-World Random Networks. IEEE Trans. Netw. Sci. Eng. 4(3): 165-176 (2017) - [c45]Alexander Rakhlin, Karthik Sridharan:
Efficient Online Multiclass Prediction on Graphs via Surrogate Losses. AISTATS 2017: 1403-1411 - [c44]Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan:
ZigZag: A New Approach to Adaptive Online Learning. COLT 2017: 876-924 - [c43]Maxim Raginsky, Alexander Rakhlin, Matus Telgarsky:
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis. COLT 2017: 1674-1703 - [c42]Alexander Rakhlin, Karthik Sridharan:
On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities. COLT 2017: 1704-1722 - [c41]Shahin Shahrampour, Alexander Rakhlin, Ali Jadbabaie:
Multi-armed bandits in multi-agent networks. ICASSP 2017: 2786-2790 - [e2]Oren Anava, Azadeh Khaleghi, Marco Cuturi, Vitaly Kuznetsov, Alexander Rakhlin:
Proceedings of the NIPS 2016 Time Series Workshop, co-located with the 30th Annual Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain, December 9, 2016. JMLR Workshop and Conference Proceedings 55, JMLR.org 2017 [contents] - [i34]Maxim Raginsky, Alexander Rakhlin, Matus Telgarsky:
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis. CoRR abs/1702.03849 (2017) - [i33]Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan:
ZigZag: A new approach to adaptive online learning. CoRR abs/1704.04010 (2017) - [i32]Tengyuan Liang, Tomaso A. Poggio, Alexander Rakhlin, James Stokes:
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. CoRR abs/1711.01530 (2017) - [i31]Vladimir Iglovikov, Alexander Rakhlin, Alexandr A. Kalinin, Alexey Shvets:
Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks. CoRR abs/1712.05053 (2017) - [i30]Noah Golowich, Alexander Rakhlin, Ohad Shamir:
Size-Independent Sample Complexity of Neural Networks. CoRR abs/1712.06541 (2017) - 2016
- [j9]Shahin Shahrampour, Alexander Rakhlin, Ali Jadbabaie:
Distributed Detection: Finite-Time Analysis and Impact of Network Topology. IEEE Trans. Autom. Control. 61(11): 3256-3268 (2016) - [c40]Shahin Shahrampour, Alexander Rakhlin, Ali Jadbabaie:
Distributed estimation of dynamic parameters: Regret analysis. ACC 2016: 1066-1071 - [c39]Vitaly Feldman, Alexander Rakhlin:
Conference on Learning Theory 2016: Preface. COLT 2016: 1-3 - [c38]Alexander Rakhlin, Karthik Sridharan:
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits. ICML 2016: 1977-1985 - [c37]Maxim Raginsky, Alexander Rakhlin, Matthew Tsao, Yihong Wu, Aolin Xu:
Information-theoretic analysis of stability and bias of learning algorithms. ITW 2016: 26-30 - [e1]Vitaly Feldman, Alexander Rakhlin, Ohad Shamir:
Proceedings of the 29th Conference on Learning Theory, COLT 2016, New York, USA, June 23-26, 2016. JMLR Workshop and Conference Proceedings 49, JMLR.org 2016 [contents] - [i29]Alexander Rakhlin, Karthik Sridharan:
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits. CoRR abs/1602.02196 (2016) - [i28]Shahin Shahrampour, Alexander Rakhlin, Ali Jadbabaie:
Distributed Estimation of Dynamic Parameters : Regret Analysis. CoRR abs/1603.00576 (2016) - [i27]T. Tony Cai, Tengyuan Liang, Alexander Rakhlin:
On Detection and Structural Reconstruction of Small-World Random Networks. CoRR abs/1604.06474 (2016) - [i26]Alexander Rakhlin, Karthik Sridharan:
A Tutorial on Online Supervised Learning with Applications to Node Classification in Social Networks. CoRR abs/1608.09014 (2016) - 2015
- [j8]Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Online learning via sequential complexities. J. Mach. Learn. Res. 16: 155-186 (2015) - [c36]Ali Jadbabaie, Alexander Rakhlin, Shahin Shahrampour, Karthik Sridharan:
Online Optimization : Competing with Dynamic Comparators. AISTATS 2015 - [c35]Shahin Shahrampour, Alexander Rakhlin, Ali Jadbabaie:
Finite-time analysis of the distributed detection problem. Allerton 2015: 598-603 - [c34]Alexandre Belloni, Tengyuan Liang, Hariharan Narayanan, Alexander Rakhlin:
Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions. COLT 2015: 240-265 - [c33]Tengyuan Liang, Alexander Rakhlin, Karthik Sridharan:
Learning with Square Loss: Localization through Offset Rademacher Complexity. COLT 2015: 1260-1285 - [c32]Alexander Rakhlin, Karthik Sridharan:
Hierarchies of Relaxations for Online Prediction Problems with Evolving Constraints. COLT 2015: 1457-1479 - [c31]Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan:
Adaptive Online Learning. NIPS 2015: 3375-3383 - [i25]Ali Jadbabaie, Alexander Rakhlin, Shahin Shahrampour, Karthik Sridharan:
Online Optimization : Competing with Dynamic Comparators. CoRR abs/1501.06225 (2015) - [i24]Alexander Rakhlin, Karthik Sridharan:
Online Nonparametric Regression with General Loss Functions. CoRR abs/1501.06598 (2015) - [i23]Alexandre Belloni, Tengyuan Liang, Hariharan Narayanan, Alexander Rakhlin:
Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions. CoRR abs/1501.07242 (2015) - [i22]Alexander Rakhlin, Karthik Sridharan:
Sequential Probability Assignment with Binary Alphabets and Large Classes of Experts. CoRR abs/1501.07340 (2015) - [i21]Tengyuan Liang, Alexander Rakhlin, Karthik Sridharan:
Learning with Square Loss: Localization through Offset Rademacher Complexity. CoRR abs/1502.06134 (2015) - [i20]Alexander Rakhlin, Karthik Sridharan:
Hierarchies of Relaxations for Online Prediction Problems with Evolving Constraints. CoRR abs/1503.01212 (2015) - [i19]Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan:
Adaptive Online Learning. CoRR abs/1508.05170 (2015) - [i18]Alexander Rakhlin, Karthik Sridharan:
On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities. CoRR abs/1510.03925 (2015) - [i17]Shahin Shahrampour, Alexander Rakhlin, Ali Jadbabaie:
Finite-time Analysis of the Distributed Detection Problem. CoRR abs/1512.09311 (2015) - 2014
- [j7]Gábor Bartók, Dean P. Foster, Dávid Pál, Alexander Rakhlin, Csaba Szepesvári:
Partial Monitoring - Classification, Regret Bounds, and Algorithms. Math. Oper. Res. 39(4): 967-997 (2014) - [c30]Alexander Rakhlin, Karthik Sridharan:
Online Non-Parametric Regression. COLT 2014: 1232-1264 - [i16]Alexander Rakhlin, Karthik Sridharan:
Online Nonparametric Regression. CoRR abs/1402.2594 (2014) - [i15]Tengyuan Liang, Hariharan Narayanan, Alexander Rakhlin:
On Zeroth-Order Stochastic Convex Optimization via Random Walks. CoRR abs/1402.2667 (2014) - [i14]Shahin Shahrampour, Alexander Rakhlin, Ali Jadbabaie:
Distributed Detection : Finite-time Analysis and Impact of Network Topology. CoRR abs/1409.8606 (2014) - 2013
- [j6]Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin:
Stochastic Convex Optimization with Bandit Feedback. SIAM J. Optim. 23(1): 213-240 (2013) - [c29]Alexander Rakhlin, Ohad Shamir, Karthik Sridharan:
Localization and Adaptation in Online Learning. AISTATS 2013: 516-526 - [c28]Wei Han, Alexander Rakhlin, Karthik Sridharan:
Competing With Strategies. COLT 2013: 966-992 - [c27]Alexander Rakhlin, Karthik Sridharan:
Online Learning with Predictable Sequences. COLT 2013: 993-1019 - [c26]Alexander Rakhlin, Karthik Sridharan:
On Semi-Probabilistic universal prediction. ITW 2013: 1-5 - [c25]Shahin Shahrampour, Alexander Rakhlin, Ali Jadbabaie:
Online Learning of Dynamic Parameters in Social Networks. NIPS 2013: 2013-2021 - [c24]Alexander Rakhlin, Karthik Sridharan:
Optimization, Learning, and Games with Predictable Sequences. NIPS 2013: 3066-3074 - [i13]Wei Han, Alexander Rakhlin, Karthik Sridharan:
Competing With Strategies. CoRR abs/1302.2672 (2013) - [i12]Alexander Rakhlin, Karthik Sridharan, Alexandre B. Tsybakov:
Empirical Entropy, Minimax Regret and Minimax Risk. CoRR abs/1308.1147 (2013) - [i11]Shahin Shahrampour, Alexander Rakhlin, Ali Jadbabaie:
Online Learning of Dynamic Parameters in Social Networks. CoRR abs/1310.0432 (2013) - [i10]Alexander Rakhlin, Karthik Sridharan:
Optimization, Learning, and Games with Predictable Sequences. CoRR abs/1311.1869 (2013) - 2012
- [j5]Alexander Rakhlin:
Foreword. J. Comput. Syst. Sci. 78(5): 1403 (2012) - [j4]Sanjit A. Seshia, Alexander Rakhlin:
Quantitative Analysis of Systems Using Game-Theoretic Learning. ACM Trans. Embed. Comput. Syst. 11(S2): 55:1-55:27 (2012) - [j3]Jacob D. Abernethy, Elad Hazan, Alexander Rakhlin:
Interior-Point Methods for Full-Information and Bandit Online Learning. IEEE Trans. Inf. Theory 58(7): 4164-4175 (2012) - [c23]Alexander Rakhlin, Ohad Shamir, Karthik Sridharan:
Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization. ICML 2012 - [c22]Alexander Rakhlin, Ohad Shamir, Karthik Sridharan:
Relax and Randomize : From Value to Algorithms. NIPS 2012: 2150-2158 - [c21]Dean P. Foster, Alexander Rakhlin:
No Internal Regret via Neighborhood Watch. AISTATS 2012: 382-390 - [i9]Alexander Rakhlin, Ohad Shamir, Karthik Sridharan:
Relax and Localize: From Value to Algorithms. CoRR abs/1204.0870 (2012) - [i8]Alexander Rakhlin, Karthik Sridharan:
Online Learning with Predictable Sequences. CoRR abs/1208.3728 (2012) - 2011
- [j2]Maxim Raginsky, Alexander Rakhlin:
Information-Based Complexity, Feedback and Dynamics in Convex Programming. IEEE Trans. Inf. Theory 57(10): 7036-7056 (2011) - [c20]Maxim Raginsky, Alexander Rakhlin:
Lower Bounds for Passive and Active Learning. NIPS 2011: 1026-1034 - [c19]Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin:
Stochastic convex optimization with bandit feedback. NIPS 2011: 1035-1043 - [c18]Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Online Learning: Stochastic, Constrained, and Smoothed Adversaries. NIPS 2011: 1764-1772 - [c17]Dean P. Foster, Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Complexity-Based Approach to Calibration with Checking Rules. COLT 2011: 293-314 - [c16]Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Online Learning: Beyond Regret. COLT 2011: 559-594 - [i7]Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Online Learning: Stochastic and Constrained Adversaries. CoRR abs/1104.5070 (2011) - [i6]Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin:
Stochastic convex optimization with bandit feedback. CoRR abs/1107.1744 (2011) - [i5]Dean P. Foster, Alexander Rakhlin:
No Internal Regret via Neighborhood Watch. CoRR abs/1108.6088 (2011) - 2010
- [c15]Maxim Raginsky, Alexander Rakhlin, Serdar Yüksel:
Online Convex Programming and regularization in adaptive control. CDC 2010: 1957-1962 - [c14]Hariharan Narayanan, Alexander Rakhlin:
Random Walk Approach to Regret Minimization. NIPS 2010: 1777-1785 - [c13]Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Online Learning: Random Averages, Combinatorial Parameters, and Learnability. NIPS 2010: 1984-1992 - [i4]Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Online Learning: Random Averages, Combinatorial Parameters, and Learnability. CoRR abs/1006.1138 (2010) - [i3]Maxim Raginsky, Alexander Rakhlin:
Information-based complexity, feedback and dynamics in sequential convex programming. CoRR abs/1010.2285 (2010) - [i2]Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Online Learning: Beyond Regret. CoRR abs/1011.3168 (2010)
2000 – 2009
- 2009
- [c12]Maxim Raginsky, Alexander Rakhlin:
Information complexity of black-box convex optimization: A new look via feedback information theory. Allerton 2009: 803-510 - [c11]Jacob D. Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin:
A Stochastic View of Optimal Regret through Minimax Duality. COLT 2009 - [c10]Jacob D. Abernethy, Alexander Rakhlin:
Beating the Adaptive Bandit with High Probability. COLT 2009 - [c9]Jacob D. Abernethy, Alexander Rakhlin:
An Efficient Bandit Algorithm for sqrt(T) Regret in Online Multiclass Prediction?. COLT 2009 - [i1]Jacob D. Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin:
A Stochastic View of Optimal Regret through Minimax Duality. CoRR abs/0903.5328 (2009) - 2008
- [c8]Jacob D. Abernethy, Elad Hazan, Alexander Rakhlin:
Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization. COLT 2008: 263-274 - [c7]Peter L. Bartlett, Varsha Dani, Thomas P. Hayes, Sham M. Kakade, Alexander Rakhlin, Ambuj Tewari:
High-Probability Regret Bounds for Bandit Online Linear Optimization. COLT 2008: 335-342 - [c6]Jacob D. Abernethy, Peter L. Bartlett, Alexander Rakhlin, Ambuj Tewari:
Optimal Stragies and Minimax Lower Bounds for Online Convex Games. COLT 2008: 415-424 - [c5]Sanjit A. Seshia, Alexander Rakhlin:
Game-theoretic timing analysis. ICCAD 2008: 575-582 - 2007
- [c4]Jacob D. Abernethy, Peter L. Bartlett, Alexander Rakhlin:
Multitask Learning with Expert Advice. COLT 2007: 484-498 - [c3]Alexander Rakhlin, Jacob D. Abernethy, Peter L. Bartlett:
Online discovery of similarity mappings. ICML 2007: 767-774 - [c2]Peter L. Bartlett, Elad Hazan, Alexander Rakhlin:
Adaptive Online Gradient Descent. NIPS 2007: 65-72 - 2006
- [j1]Andrea Caponnetto, Alexander Rakhlin:
Stability Properties of Empirical Risk Minimization over Donsker Classes. J. Mach. Learn. Res. 7: 2565-2583 (2006) - [c1]Alexander Rakhlin, Andrea Caponnetto:
Stability of $K$-Means Clustering. NIPS 2006: 1121-1128
Coauthor Index
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