A Sampling Based Approach to Facial Feature Extraction. - Karthik Sridharan; Venu Govindaraju (AutoID, 2005)

A Probabilistic Approach to Semantic Face Retrieval System. - Karthik Sridharan; Sankalp Nayak; Sharat Chikkerur; Venu Govindaraju (AVBPA, 2005)

A Dynamic Migration Model for Self-adaptive Genetic Algorithms. - K. G. Srinivasa 001; Karthik Sridharan; P. Deepa Shenoy; K. R. Venugopal 001; Lalit M. Patnaik (IDEAL, 2005)

Identifying Handwritten Text in Mixed Documents. - Faisal Farooq; Karthik Sridharan; Venu Govindaraju (ICPR (2), 2006)

Competitive Mixtures of Simple Neurons. - Karthik Sridharan; Matthew J. Beal; Venu Govindaraju (ICPR (2), 2006)

A neural network based CBIR system using STI features and relevance feedback. - K. G. Srinivasa 001; Karthik Sridharan; P. Deepa Shenoy; K. R. Venugopal 001; Lalit M. Patnaik (Intell. Data Anal., 2006)

An Information Theoretic Framework for Multi-view Learning. - Karthik Sridharan; Sham M. Kakade (COLT, 2008)

On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization. - Sham M. Kakade; Karthik Sridharan; Ambuj Tewari (NIPS, 2008)

Fast Rates for Regularized Objectives. - Karthik Sridharan; Shai Shalev-Shwartz; Nathan Srebro (NIPS, 2008)

The Complexity of Improperly Learning Large Margin Halfspaces. - Shai Shalev-Shwartz; Ohad Shamir; Karthik Sridharan (COLT, 2009)

Stochastic Convex Optimization. - Shai Shalev-Shwartz; Ohad Shamir; Nathan Srebro; Karthik Sridharan (COLT, 2009)

Learnability and Stability in the General Learning Setting. - Shai Shalev-Shwartz; Ohad Shamir; Nathan Srebro; Karthik Sridharan (COLT, 2009)

Multi-view clustering via canonical correlation analysis. - Kamalika Chaudhuri; Sham M. Kakade; Karen Livescu; Karthik Sridharan (ICML, 2009)

Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity - Sham M. Kakade; Ohad Shamir; Karthik Sridharan; Ambuj Tewari (CoRR, 2009)

Robust Selective Sampling from Single and Multiple Teachers. - Ofer Dekel; Claudio Gentile; Karthik Sridharan (COLT, 2010)

Learning Kernel-Based Halfspaces with the Zero-One Loss. - Shai Shalev-Shwartz; Ohad Shamir; Karthik Sridharan (COLT, 2010)

Convex Games in Banach Spaces. - Karthik Sridharan; Ambuj Tewari (COLT, 2010)

Online Learning: Random Averages, Combinatorial Parameters, and Learnability. - Alexander Rakhlin; Karthik Sridharan; Ambuj Tewari (NIPS, 2010)

Smoothness, Low Noise and Fast Rates. - Nathan Srebro; Karthik Sridharan; Ambuj Tewari (NIPS, 2010)

Learnability, Stability and Uniform Convergence. - Shai Shalev-Shwartz; Ohad Shamir; Nathan Srebro; Karthik Sridharan (J. Mach. Learn. Res., 2010)

Learning Linear and Kernel Predictors with the 0-1 Loss Function. - Shai Shalev-Shwartz; Ohad Shamir; Karthik Sridharan (IJCAI, 2011)

Better Mini-Batch Algorithms via Accelerated Gradient Methods. - Andrew Cotter; Ohad Shamir; Nati Srebro; Karthik Sridharan (NIPS, 2011)

Online Learning: Stochastic, Constrained, and Smoothed Adversaries. - Alexander Rakhlin; Karthik Sridharan; Ambuj Tewari (NIPS, 2011)

On the Universality of Online Mirror Descent. - Nati Srebro; Karthik Sridharan; Ambuj Tewari (NIPS, 2011)

Online Learning: Stochastic and Constrained Adversaries - Alexander Rakhlin; Karthik Sridharan; Ambuj Tewari (CoRR, 2011)

Complexity-Based Approach to Calibration with Checking Rules. - Dean P. Foster; Alexander Rakhlin; Karthik Sridharan; Ambuj Tewari (COLT, 2011)

Online Learning: Beyond Regret. - Alexander Rakhlin; Karthik Sridharan; Ambuj Tewari (COLT, 2011)

Learning Kernel-Based Halfspaces with the 0-1 Loss. - Shai Shalev-Shwartz; Ohad Shamir; Karthik Sridharan (SIAM J. Comput., 2011)

Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss. - Shai Ben-David; David Loker; Nathan Srebro; Karthik Sridharan (ICML, 2012)

Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization. - Alexander Rakhlin; Ohad Shamir; Karthik Sridharan (ICML, 2012)

Relax and Randomize : From Value to Algorithms. - Alexander Rakhlin; Ohad Shamir; Karthik Sridharan (NIPS, 2012)

Relax and Localize: From Value to Algorithms - Alexander Rakhlin; Ohad Shamir; Karthik Sridharan (CoRR, 2012)

Learning From An Optimization Viewpoint - Karthik Sridharan (CoRR, 2012)

Selective sampling and active learning from single and multiple teachers. - Ofer Dekel; Claudio Gentile; Karthik Sridharan (J. Mach. Learn. Res., 2012)

Localization and Adaptation in Online Learning. - Alexander Rakhlin; Ohad Shamir; Karthik Sridharan (AISTATS, 2013)

Competing With Strategies. - Wei Han; Alexander Rakhlin; Karthik Sridharan (COLT, 2013)

Online Learning with Predictable Sequences. - Alexander Rakhlin; Karthik Sridharan (COLT, 2013)

On Semi-Probabilistic universal prediction. - Alexander Rakhlin; Karthik Sridharan (ITW, 2013)

Optimization, Learning, and Games with Predictable Sequences. - Alexander Rakhlin; Karthik Sridharan (NIPS, 2013)

Empirical Entropy, Minimax Regret and Minimax Risk. - Alexander Rakhlin; Karthik Sridharan; Alexandre B. Tsybakov (CoRR, 2013)

Online Non-Parametric Regression. - Alexander Rakhlin; Karthik Sridharan (COLT, 2014)

Online Nonparametric Regression. - Alexander Rakhlin; Karthik Sridharan (CoRR, 2014)

Online Optimization : Competing with Dynamic Comparators. - Ali Jadbabaie; Alexander Rakhlin; Shahin Shahrampour; Karthik Sridharan (AISTATS, 2015)

Learning with Square Loss: Localization through Offset Rademacher Complexity. - Tengyuan Liang; Alexander Rakhlin; Karthik Sridharan (COLT, 2015)

Hierarchies of Relaxations for Online Prediction Problems with Evolving Constraints. - Alexander Rakhlin; Karthik Sridharan (COLT, 2015)

Adaptive Online Learning. - Dylan J. Foster; Alexander Rakhlin; Karthik Sridharan (NIPS, 2015)

Sequential Probability Assignment with Binary Alphabets and Large Classes of Experts. - Alexander Rakhlin; Karthik Sridharan (CoRR, 2015)

Online Nonparametric Regression with General Loss Functions. - Alexander Rakhlin; Karthik Sridharan (CoRR, 2015)

Online learning via sequential complexities. - Alexander Rakhlin; Karthik Sridharan; Ambuj Tewari (J. Mach. Learn. Res., 2015)

Private Causal Inference. - Matt J. Kusner; Yu Sun 020; Karthik Sridharan; Kilian Q. Weinberger (AISTATS, 2016)

BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits. - Alexander Rakhlin; Karthik Sridharan (ICML, 2016)

Learning in Games: Robustness of Fast Convergence. - Dylan J. Foster; Zhiyuan Li 005; Thodoris Lykouris; Karthik Sridharan; Éva Tardos (NIPS, 2016)

Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters. - Zeyuan Allen Zhu; Yang Yuan; Karthik Sridharan (NIPS, 2016)

Fast Convergence of Common Learning Algorithms in Games. - Dylan J. Foster; Zhiyuan Li 005; Thodoris Lykouris; Karthik Sridharan; Éva Tardos (CoRR, 2016)

A Tutorial on Online Supervised Learning with Applications to Node Classification in Social Networks. - Alexander Rakhlin; Karthik Sridharan (CoRR, 2016)

Efficient Online Multiclass Prediction on Graphs via Surrogate Losses. - Alexander Rakhlin; Karthik Sridharan (AISTATS, 2017)

ZigZag: A New Approach to Adaptive Online Learning. - Dylan J. Foster; Alexander Rakhlin; Karthik Sridharan (COLT, 2017)

On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities. - Alexander Rakhlin; Karthik Sridharan (COLT, 2017)

Parameter-Free Online Learning via Model Selection. - Dylan J. Foster; Satyen Kale; Mehryar Mohri; Karthik Sridharan (NIPS, 2017)

Inference in Sparse Graphs with Pairwise Measurements and Side Information. - Dylan J. Foster; Karthik Sridharan; Daniel Reichman 001 (AISTATS, 2018)

Logistic Regression: The Importance of Being Improper. - Dylan J. Foster; Satyen Kale; Haipeng Luo; Mehryar Mohri; Karthik Sridharan (COLT, 2018)

Online Learning: Sufficient Statistics and the Burkholder Method. - Dylan J. Foster; Alexander Rakhlin; Karthik Sridharan (COLT, 2018)

Uniform Convergence of Gradients for Non-Convex Learning and Optimization. - Dylan J. Foster; Ayush Sekhari; Karthik Sridharan (NeurIPS, 2018)

Two-Player Games for Efficient Non-Convex Constrained Optimization. - Andrew Cotter; Heinrich Jiang; Karthik Sridharan (ALT, 2019)

The Complexity of Making the Gradient Small in Stochastic Convex Optimization. - Dylan J. Foster; Ayush Sekhari; Ohad Shamir; Nathan Srebro; Karthik Sridharan; Blake E. Woodworth (COLT, 2019)

Distributed Learning with Sublinear Communication. - Jayadev Acharya; Chris De Sa; Dylan J. Foster; Karthik Sridharan (ICML, 2019)

Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. - Andrew Cotter; Maya R. Gupta; Heinrich Jiang; Nathan Srebro; Karthik Sridharan; Serena Lutong Wang; Blake E. Woodworth; Seungil You (ICML, 2019)

Hypothesis Set Stability and Generalization. - Dylan J. Foster; Spencer Greenberg; Satyen Kale; Haipeng Luo; Mehryar Mohri; Karthik Sridharan (NeurIPS, 2019)

Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals. - Andrew Cotter; Heinrich Jiang; Maya R. Gupta; Serena Lutong Wang; Taman Narayan; Seungil You; Karthik Sridharan (J. Mach. Learn. Res., 2019)

Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations. - Yossi Arjevani; Yair Carmon; John C. Duchi; Dylan J. Foster; Ayush Sekhari; Karthik Sridharan (COLT, 2020)

Online learning with dynamics: A minimax perspective. - Kush Bhatia; Karthik Sridharan (NeurIPS, 2020)

Reinforcement Learning with Feedback Graphs. - Christoph Dann; Yishay Mansour; Mehryar Mohri; Ayush Sekhari; Karthik Sridharan (NeurIPS, 2020)

Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations. - Ayush Sekhari; Christoph Dann; Mehryar Mohri; Yishay Mansour; Karthik Sridharan (NeurIPS, 2021)

SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs. - Ayush Sekhari; Karthik Sridharan; Satyen Kale (NeurIPS, 2021)

Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation. - Christoph Dann; Yishay Mansour; Mehryar Mohri; Ayush Sekhari; Karthik Sridharan (ICML, 2022)

On the Complexity of Adversarial Decision Making. - Dylan J. Foster; Alexander Rakhlin; Ayush Sekhari; Karthik Sridharan (NeurIPS, 2022)

From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent. - Christopher De Sa; Satyen Kale; Jason D. Lee; Ayush Sekhari; Karthik Sridharan (NeurIPS, 2022)

Small-Loss Bounds for Online Learning with Partial Information. - Thodoris Lykouris; Karthik Sridharan; Éva Tardos (Math. Oper. Res., 2022)

Contextual Bandits and Imitation Learning with Preference-Based Active Queries. - Ayush Sekhari; Karthik Sridharan; Wen Sun 002; Runzhe Wu (NeurIPS, 2023)

Selective Sampling and Imitation Learning via Online Regression. - Ayush Sekhari; Karthik Sridharan; Wen Sun 002; Runzhe Wu (NeurIPS, 2023)

Contextual Bandits and Imitation Learning via Preference-Based Active Queries. - Ayush Sekhari; Karthik Sridharan; Wen Sun 002; Runzhe Wu (CoRR, 2023)

Online Learning with Unknown Constraints. - Karthik Sridharan; Seung Won Wilson Yoo (CoRR, 2024)

Langevin Dynamics: A Unified Perspective on Optimization via Lyapunov Potentials. - August Y. Chen; Ayush Sekhari; Karthik Sridharan (CoRR, 2024)

From Optimization to Sampling via Lyapunov Potentials. - August Y. Chen; Karthik Sridharan (CoRR, 2024)

Optimization, Isoperimetric Inequalities, and Sampling via Lyapunov Potentials. - August Y. Chen; Karthik Sridharan (COLT, 2025)

Efficiently Escaping Saddle Points under Generalized Smoothness via Self-Bounding Regularity. - Daniel Yiming Cao; August Y. Chen; Karthik Sridharan; Benjamin Tang (CoRR, 2025)

Active Learning via Regression Beyond Realizability. - Atul Ganju; Shashaank Aiyer; Ved Sriraman; Karthik Sridharan (CoRR, 2025)

System-Aware Unlearning Algorithms: Use Lesser, Forget Faster. - Linda Lu; Ayush Sekhari; Karthik Sridharan (CoRR, 2025)