← Authors

Karthik Sridharan

55 papers · 2008–2025 · 6 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🌈 Renaissance Researcher (6) 🐝 Cross-Pollinator (15) πŸƒ Academic Marathon (17) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (6) πŸ—ΊοΈ Taxonomy Completionist (17) 🐣 Hot Topic Early Bird 🌟 Keyword Trendsetter Combo (6) 🏠 Conference Loyalist (23) 🌱 Topic Pioneer 🀝 Dynamic Duo (19) πŸ”¬ Deep Specialist (30) πŸ† Keyword Champion (3) πŸš€ Conference Pioneer ⚑ Prolific Year (5) πŸ—ƒοΈ Keyword Collector (63) πŸ’Ž Century Club (55) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (14)

Conferences

NIPS (23) COLT (15) ICML (6) AISTATS (5) JMLR (4) ALT (2)

Top co-authors

Alexander Rakhlin (19) Ayush Sekhari (12) Ambuj Tewari (8) Mehryar Mohri (7) Dylan J Foster (6) Satyen Kale (5) Dylan J. Foster (5) Ohad Shamir (5) Nathan Srebro (5) Andrew Cotter (4)

Research topics

Learning Paradigms (1) Optimization (1) Learning Paradigms (1) Differential Privacy (1)

Keywords

online learning (22) regret bound (19) rademacher complexity (9) empirical risk minimization (7) stochastic convex optimization (6) convex optimization (6) non-convex optimization (5) sample complexity (4) stochastic optimization (4) constrained optimization (3) stochastic gradient descent (3) two-player game (3) adaptive algorithm (3) imitation learning (2) model selection (2) active learning (2) adversarial learning (2) statistical learning (2) supervised learning (2) game theory (2)

Papers

System-Aware Unlearning Algorithms: Use Lesser, Forget Faster ICML 2025
Optimization, Isoperimetric Inequalities, and Sampling via Lyapunov Potentials COLT 2025
Online Learning with Unknown Constraints ICML 2025
Selective Sampling and Imitation Learning via Online Regression NIPS 2023
Contextual Bandits and Imitation Learning with Preference-Based Active Queries NIPS 2023
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent NIPS 2022
On the Complexity of Adversarial Decision Making NIPS 2022
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation ICML 2022
SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs NIPS 2021
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations NIPS 2021
Reinforcement Learning with Feedback Graphs NIPS 2020
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations COLT 2020
Online learning with dynamics: A minimax perspective NIPS 2020
Hypothesis Set Stability and Generalization NIPS 2019
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints ICML 2019
Two-Player Games for Efficient Non-Convex Constrained Optimization ALT 2019
The Complexity of Making the Gradient Small in Stochastic Convex Optimization COLT 2019
Distributed Learning with Sublinear Communication ICML 2019
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals JMLR 2019
Inference in Sparse Graphs with Pairwise Measurements and Side Information AISTATS 2018
Uniform Convergence of Gradients for Non-Convex Learning and Optimization NIPS 2018
Algorithmic Learning Theory ALT 2018: Preface ALT 2018
Logistic Regression: The Importance of Being Improper COLT 2018
Small-loss bounds for online learning with partial information COLT 2018
Online Learning: Sufficient Statistics and the Burkholder Method COLT 2018
ZigZag: A New Approach to Adaptive Online Learning COLT 2017
Efficient Online Multiclass Prediction on Graphs via Surrogate Losses AISTATS 2017
Parameter-Free Online Learning via Model Selection NIPS 2017
On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities COLT 2017
Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters NIPS 2016
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits ICML 2016
Private Causal Inference AISTATS 2016
Learning in Games: Robustness of Fast Convergence NIPS 2016
Online Optimization : Competing with Dynamic Comparators AISTATS 2015
Learning with Square Loss: Localization through Offset Rademacher Complexity COLT 2015
Hierarchies of Relaxations for Online Prediction Problems with Evolving Constraints COLT 2015
Online Learning via Sequential Complexities JMLR 2015
Adaptive Online Learning NIPS 2015
Online Non-Parametric Regression COLT 2014
Localization and Adaptation in Online Learning AISTATS 2013
Competing With Strategies COLT 2013
Optimization, Learning, and Games with Predictable Sequences NIPS 2013
Online Learning with Predictable Sequences COLT 2013
Selective Sampling and Active Learning from Single and Multiple Teachers JMLR 2012
Relax and Randomize : From Value to Algorithms NIPS 2012
Online Learning: Beyond Regret COLT 2011
On the Universality of Online Mirror Descent NIPS 2011
Online Learning: Stochastic, Constrained, and Smoothed Adversaries NIPS 2011
Better Mini-Batch Algorithms via Accelerated Gradient Methods NIPS 2011
Complexity-Based Approach to Calibration with Checking Rules COLT 2011
Learnability, Stability and Uniform Convergence JMLR 2010
Online Learning: Random Averages, Combinatorial Parameters, and Learnability NIPS 2010
Smoothness, Low Noise and Fast Rates NIPS 2010
Fast Rates for Regularized Objectives NIPS 2008
On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization NIPS 2008