Publications
The list below provides direct links to each paper and its BibTeX entry for easy access and citation.
You can also find my articles on my Google Scholar profile.
You can also find my articles on my Google Scholar profile.
Preprints
Privileged Solutions or Context-Induced Teacher Behavior? Dissecting On-Policy Self-Distillation
Conference Papers
Fixed-Confidence Best-Arm Identification for Causal Mediation Analysis
To appear in Conference on Uncertainty in Artificial Intelligence (UAI), 2026
Rate-optimal Design for Anytime Best Arm Identification
To appear in International Conference on Artificial Intelligence and Statistics (AISTATS), 2026
Best-of-infinity: Asymptotic Performance of Test-Time LLM Ensembling
Published in International Conference on Learning Representations (ICLR), 2026
Optimal Estimation of the Best Mean in Multi-Armed Bandits
Published in Advances in Neural Information Processing Systems (NeurIPS), 2025
No-Regret Incentive-Compatible Online Learning under Exact Truthfulness with Non-Myopic Experts
Published in ACM Conference on Economics and Computation (EC), 2025
Data-dependent Bounds with T-Optimal Best-of-Both-Worlds Guarantees in Multi-Armed Bandits using Stability-Penalty Matching
Published in Conference on Learning Theory (COLT), 2025
Fixed Confidence Best Arm Identification in the Bayesian Setting
Published in Advances in Neural Information Processing Systems (NeurIPS), 2024
This paper addresses fixed confidence best arm identification in the Bayesian setting, providing theoretical analysis and practical algorithms for Bayesian bandit problems.
Learning Fair Division from Bandit Feedback
Published in International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
This paper develops algorithms for learning fair division mechanisms from bandit feedback, combining fairness considerations with sequential decision-making.
Posterior Tracking Algorithm for Classification Bandits
Published in International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
This paper develops posterior tracking algorithms for classification bandit problems, providing efficient methods for learning classifiers in sequential decision-making settings.
Thresholded Linear Bandits
Published in International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
This paper introduces thresholded linear bandits, extending linear bandit theory to scenarios where only thresholded feedback is available.
High-dimensional Contextual Bandit Problem without Sparsity
Published in Advances in Neural Information Processing Systems (NeurIPS), 2023
This paper addresses high-dimensional contextual bandit problems without sparsity assumptions, providing theoretical guarantees and practical algorithms.
Anytime Capacity Expansion in Medical Residency Match by Monte Carlo Tree Search
Published in International Joint Conference on Artificial Intelligence (IJCAI), 2022
This paper applies Monte Carlo tree search for flexible-capacity mechanism design in medical residency matching, addressing NP-Complete optimization problems.
Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification
Published in Advances in Neural Information Processing Systems (NeurIPS), 2022
This paper develops minimax optimal algorithms for fixed-budget best arm identification, providing theoretical guarantees and practical implementations.
Scaling Multi-Armed Bandit Algorithms
Published in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2019
This paper addresses scalability challenges in multi-armed bandit algorithms, developing methods for handling large-scale bandit problems efficiently.
Nonconvex Optimization for Regression with Fairness Constraints
Published in International Conference on Machine Learning (ICML), 2018
This paper develops nonconvex optimization methods for regression problems with fairness constraints, addressing algorithmic bias in machine learning.
Statistical Emerging Pattern Mining with Multiple Testing Correction
Published in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2017
This paper develops statistical methods for emerging pattern mining with multiple testing correction, providing rigorous statistical guarantees for pattern discovery.
Position-based Multiple-play Bandit Problem with Unknown Position Bias
Published in Advances in Neural Information Processing Systems (NIPS), 2017
This paper addresses position-based multiple-play bandit problems with unknown position bias, providing theoretical analysis and practical algorithms.
Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient Algorithm
Published in International Conference on Machine Learning (ICML), 2016
This paper provides comprehensive analysis of the Copeland dueling bandit problem, including regret lower bounds, optimal algorithms, and computationally efficient implementations.
Regret Lower Bound and Optimal Algorithm in Finite Stochastic Partial Monitoring
Published in Advances in Neural Information Processing Systems (NIPS), 2015
This paper establishes regret lower bounds and develops optimal algorithms for finite stochastic partial monitoring, extending bandit theory to partial feedback settings.
Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays
Published in International Conference on Machine Learning (ICML), 2015
This paper provides optimal regret analysis of Thompson sampling in stochastic multi-armed bandit problems with multiple plays, establishing theoretical guarantees for Bayesian bandit algorithms.
Regret Lower Bound and Optimal Algorithm in Dueling Bandit Problem
Published in Conference on Learning Theory (COLT), 2015
This paper establishes regret lower bounds and develops optimal algorithms for dueling bandit problems, providing fundamental theoretical contributions to preference-based learning.
Journal Articles
Replicability is Asymptotically Free in Multi-armed Bandits
Published in Transactions of Machine Learning Research, 2026
Journal to Conference Transfer: To be presented in NeurIPS 2026
Monotone Bandits: Power of Maximum Likelihood Estimation in Online Decision-Making
Published in Machine Learning, 2026
Miners’ Reward Elasticity and Stability of Competing Proof-of-Work Cryptocurrencies
Published in International Economic Review, 2026
On Statistical Discrimination as a Failure of Social Learning: A Multi-Armed Bandit Approach
Published in Management Science, 2026
This paper analyzes statistical discrimination through the lens of multi-armed bandit theory, showing how it emerges as a failure of social learning.
Strategic Choices of Migrants and Smugglers in the Central Mediterranean Sea
Published in PLoS ONE, 2024
This paper analyzes the strategic interactions between migrants and smugglers in the Central Mediterranean Sea using game-theoretic and machine learning approaches.
Rate-Optimal Bayesian Simple Regret in Best Arm Identification
Published in Mathematics of Operations Research, 2024
This paper establishes rate-optimal bounds for Bayesian simple regret in best arm identification problems, providing theoretical guarantees for Bayesian bandit algorithms.
Finite-time Analysis of Globally Nonstationary Multi-Armed Bandits
Published in Journal of Machine Learning Research, 2024
This paper provides finite-time analysis for globally nonstationary multi-armed bandit problems, extending theoretical guarantees to dynamic environments.
Bridging Offline and Online Experimentation: Constraint Active Search for Deployed Performance Optimization
Published in Transactions of Machine Learning Research, 2022
This paper develops methods for bridging offline and online experimentation through constraint active search, enabling effective performance optimization in deployed systems.