Karthik Sridharan

Publication Activity (10 Years)

Years Active: 2005-2024
Publications (10 Years): 59

Top Venues

CoRR

COLT

NeurIPS

AISTATS

Publications

August Y. Chen, Ayush Sekhari, Karthik Sridharan
Langevin Dynamics: A Unified Perspective on Optimization via Lyapunov Potentials. CoRR (2024)
Karthik Sridharan, Seung Won Wilson Yoo
Online Learning with Unknown Constraints. CoRR (2024)
Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu
Contextual Bandits and Imitation Learning via Preference-Based Active Queries. CoRR (2023)
Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu
Contextual Bandits and Imitation Learning with Preference-Based Active Queries. NeurIPS (2023)
Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu
Selective Sampling and Imitation Learning via Online Regression. CoRR (2023)
Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu
Selective Sampling and Imitation Learning via Online Regression. NeurIPS (2023)
Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation. CoRR (2022)
Dylan J. Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan
On the Complexity of Adversarial Decision Making. CoRR (2022)
Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation. ICML (2022)
Satyen Kale, Jason D. Lee, Chris De Sa, Ayush Sekhari, Karthik Sridharan
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent. CoRR (2022)
Dylan J. Foster, Alexander Rakhlin, Ayush Sekhari, Karthik Sridharan
On the Complexity of Adversarial Decision Making. NeurIPS (2022)
Thodoris Lykouris, Karthik Sridharan, Éva Tardos
Small-Loss Bounds for Online Learning with Partial Information. Math. Oper. Res. 47 (3) (2022)
Christopher De Sa, Satyen Kale, Jason D. Lee, Ayush Sekhari, Karthik Sridharan
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent. NeurIPS (2022)
Ayush Sekhari, Karthik Sridharan, Satyen Kale
SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs. NeurIPS (2021)
Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations. CoRR (2021)
Ayush Sekhari, Christoph Dann, Mehryar Mohri, Yishay Mansour, Karthik Sridharan
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations. NeurIPS (2021)
Satyen Kale, Ayush Sekhari, Karthik Sridharan
SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs. CoRR (2021)
Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
Reinforcement Learning with Feedback Graphs. CoRR (2020)
Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
Reinforcement Learning with Feedback Graphs. NeurIPS (2020)
Kush Bhatia, Karthik Sridharan
Online learning with dynamics: A minimax perspective. NeurIPS (2020)
Kush Bhatia, Karthik Sridharan
Online learning with dynamics: A minimax perspective. CoRR (2020)
Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations. COLT (2020)
Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations. CoRR (2020)
Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake E. Woodworth
The Complexity of Making the Gradient Small in Stochastic Convex Optimization. COLT (2019)
Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan
Hypothesis Set Stability and Generalization. NeurIPS (2019)
Andrew Cotter, Heinrich Jiang, Karthik Sridharan
Two-Player Games for Efficient Non-Convex Constrained Optimization. ALT (2019)
Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake E. Woodworth
The Complexity of Making the Gradient Small in Stochastic Convex Optimization. CoRR (2019)
Andrew Cotter, Heinrich Jiang, Maya R. Gupta, Serena Wang, Taman Narayan, Seungil You, Karthik Sridharan
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals. J. Mach. Learn. Res. 20 (2019)
Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake E. Woodworth, Seungil You
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. ICML (2019)
Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan
Hypothesis Set Stability and Generalization. CoRR (2019)
Jayadev Acharya, Christopher De Sa, Dylan J. Foster, Karthik Sridharan
Distributed Learning with Sublinear Communication. CoRR (2019)
Jayadev Acharya, Chris De Sa, Dylan J. Foster, Karthik Sridharan
Distributed Learning with Sublinear Communication. ICML (2019)
Dylan J. Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan
Logistic Regression: The Importance of Being Improper. COLT (2018)
Dylan J. Foster, Ayush Sekhari, Karthik Sridharan
Uniform Convergence of Gradients for Non-Convex Learning and Optimization. NeurIPS (2018)
Thodoris Lykouris, Karthik Sridharan, Éva Tardos
Small-loss bounds for online learning with partial information. COLT (2018)
Dylan J. Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan
Logistic Regression: The Importance of Being Improper. CoRR (2018)
Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake E. Woodworth, Seungil You
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. CoRR (2018)
Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan
Online Learning: Sufficient Statistics and the Burkholder Method. COLT (2018)
Andrew Cotter, Heinrich Jiang, Serena Wang, Taman Narayan, Maya R. Gupta, Seungil You, Karthik Sridharan
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals. CoRR (2018)
Dylan J. Foster, Satyen Kale, Mehryar Mohri, Karthik Sridharan
Parameter-free online learning via model selection. CoRR (2018)
Dylan J. Foster, Karthik Sridharan, Daniel Reichman
Inference in Sparse Graphs with Pairwise Measurements and Side Information. AISTATS (2018)
Dylan J. Foster, Ayush Sekhari, Karthik Sridharan
Uniform Convergence of Gradients for Non-Convex Learning and Optimization. CoRR (2018)
Andrew Cotter, Heinrich Jiang, Karthik Sridharan
Two-Player Games for Efficient Non-Convex Constrained Optimization. CoRR (2018)
Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan
Online Learning: Sufficient Statistics and the Burkholder Method. CoRR (2018)
Alexander Rakhlin, Karthik Sridharan
Efficient Online Multiclass Prediction on Graphs via Surrogate Losses. AISTATS (2017)
Dylan J. Foster, Satyen Kale, Mehryar Mohri, Karthik Sridharan
Parameter-Free Online Learning via Model Selection. NIPS (2017)
Thodoris Lykouris, Karthik Sridharan, Éva Tardos
Small-loss bounds for online learning with partial information. CoRR (2017)
Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan
ZigZag: A New Approach to Adaptive Online Learning. COLT (2017)
Dylan J. Foster, Daniel Reichman, Karthik Sridharan
Inference in Sparse Graphs with Pairwise Measurements and Side Information. CoRR (2017)
Alexander Rakhlin, Karthik Sridharan
On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities. COLT (2017)
Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan
ZigZag: A new approach to adaptive online learning. CoRR (2017)
Dylan J. Foster, Zhiyuan Li, Thodoris Lykouris, Karthik Sridharan, Éva Tardos
Learning in Games: Robustness of Fast Convergence. NIPS (2016)
Alexander Rakhlin, Karthik Sridharan
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits. CoRR (2016)
Alexander Rakhlin, Karthik Sridharan
A Tutorial on Online Supervised Learning with Applications to Node Classification in Social Networks. CoRR (2016)
Zeyuan Allen Zhu, Yang Yuan, Karthik Sridharan
Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters. CoRR (2016)
Zeyuan Allen Zhu, Yang Yuan, Karthik Sridharan
Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters. NIPS (2016)
Matt J. Kusner, Yu Sun, Karthik Sridharan, Kilian Q. Weinberger
Private Causal Inference. AISTATS (2016)
Dylan J. Foster, Zhiyuan Li, Thodoris Lykouris, Karthik Sridharan, Éva Tardos
Fast Convergence of Common Learning Algorithms in Games. CoRR (2016)
Alexander Rakhlin, Karthik Sridharan
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits. ICML (2016)
Tengyuan Liang, Alexander Rakhlin, Karthik Sridharan
Learning with Square Loss: Localization through Offset Rademacher Complexity. CoRR (2015)
Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan
Adaptive Online Learning. NIPS (2015)
Alexander Rakhlin, Karthik Sridharan
Hierarchies of Relaxations for Online Prediction Problems with Evolving Constraints. CoRR (2015)
Tengyuan Liang, Alexander Rakhlin, Karthik Sridharan
Learning with Square Loss: Localization through Offset Rademacher Complexity. COLT (2015)
Alexander Rakhlin, Karthik Sridharan
Sequential Probability Assignment with Binary Alphabets and Large Classes of Experts. CoRR (2015)
Alexander Rakhlin, Karthik Sridharan
Hierarchies of Relaxations for Online Prediction Problems with Evolving Constraints. COLT (2015)
Ali Jadbabaie, Alexander Rakhlin, Shahin Shahrampour, Karthik Sridharan
Online Optimization : Competing with Dynamic Comparators. AISTATS (2015)
Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
Online learning via sequential complexities. J. Mach. Learn. Res. 16 (2015)
Alexander Rakhlin, Karthik Sridharan
Online Nonparametric Regression with General Loss Functions. CoRR (2015)
Ali Jadbabaie, Alexander Rakhlin, Shahin Shahrampour, Karthik Sridharan
Online Optimization : Competing with Dynamic Comparators. CoRR (2015)
Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan
Adaptive Online Learning. CoRR (2015)
Alexander Rakhlin, Karthik Sridharan
On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities. CoRR (2015)
Alexander Rakhlin, Karthik Sridharan
Online Non-Parametric Regression. COLT (2014)
Alexander Rakhlin, Karthik Sridharan
Online Nonparametric Regression. CoRR (2014)
Alexander Rakhlin, Karthik Sridharan
Optimization, Learning, and Games with Predictable Sequences. NIPS (2013)
Wei Han, Alexander Rakhlin, Karthik Sridharan
Competing With Strategies CoRR (2013)
Alexander Rakhlin, Karthik Sridharan, Alexandre B. Tsybakov
Empirical Entropy, Minimax Regret and Minimax Risk. CoRR (2013)
Alexander Rakhlin, Karthik Sridharan
On Semi-Probabilistic universal prediction. ITW (2013)
Wei Han, Alexander Rakhlin, Karthik Sridharan
Competing With Strategies. COLT (2013)
Alexander Rakhlin, Karthik Sridharan
Optimization, Learning, and Games with Predictable Sequences. CoRR (2013)
Alexander Rakhlin, Ohad Shamir, Karthik Sridharan
Localization and Adaptation in Online Learning. AISTATS (2013)
Alexander Rakhlin, Karthik Sridharan
Online Learning with Predictable Sequences. COLT (2013)
Alexander Rakhlin, Ohad Shamir, Karthik Sridharan
Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization. ICML (2012)
Alexander Rakhlin, Ohad Shamir, Karthik Sridharan
Relax and Randomize : From Value to Algorithms. NIPS (2012)
Karthik Sridharan
Learning From An Optimization Viewpoint CoRR (2012)
Ofer Dekel, Claudio Gentile, Karthik Sridharan
Selective sampling and active learning from single and multiple teachers. J. Mach. Learn. Res. 13 (2012)
Alexander Rakhlin, Ohad Shamir, Karthik Sridharan
Relax and Localize: From Value to Algorithms CoRR (2012)
Alexander Rakhlin, Karthik Sridharan
Online Learning with Predictable Sequences CoRR (2012)
Shai Ben-David, David Loker, Nathan Srebro, Karthik Sridharan
Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss. ICML (2012)
Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
Online Learning: Stochastic and Constrained Adversaries CoRR (2011)
Andrew Cotter, Ohad Shamir, Nati Srebro, Karthik Sridharan
Better Mini-Batch Algorithms via Accelerated Gradient Methods. NIPS (2011)
Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
Online Learning: Beyond Regret. COLT (2011)
Andrew Cotter, Ohad Shamir, Nathan Srebro, Karthik Sridharan
Better Mini-Batch Algorithms via Accelerated Gradient Methods CoRR (2011)
Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan
Learning Linear and Kernel Predictors with the 0-1 Loss Function. IJCAI (2011)
Nathan Srebro, Karthik Sridharan, Ambuj Tewari
On the Universality of Online Mirror Descent CoRR (2011)
Nati Srebro, Karthik Sridharan, Ambuj Tewari
On the Universality of Online Mirror Descent. NIPS (2011)
Dean P. Foster, Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
Complexity-Based Approach to Calibration with Checking Rules. COLT (2011)
Shai Shalev-Shwartz, Ohad Shamir, Karthik Sridharan
Learning Kernel-Based Halfspaces with the 0-1 Loss. SIAM J. Comput. 40 (6) (2011)
Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
Online Learning: Stochastic, Constrained, and Smoothed Adversaries. NIPS (2011)
Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
Online Learning: Random Averages, Combinatorial Parameters, and Learnability. NIPS (2010)
Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
Online Learning: Beyond Regret CoRR (2010)

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Publication Activity (10 Years)

Top Topics

Top Venues

Karthik Sridharan