Iyengar, Garud

17 publications

AISTATS 2025 $β$-Th Order Acyclicity Derivatives for DAG Learning Madhumitha Shridharan, Garud Iyengar

AISTATS 2025 Learning the Pareto Front Using Bootstrapped Observation Samples Wonyoung Kim, Garud Iyengar, Assaf Zeevi

ICML 2025 Linear Bandits with Partially Observable Features Wonyoung Kim, Sungwoo Park, Garud Iyengar, Assaf Zeevi, Min-Hwan Oh

AISTATS 2024 A Doubly Robust Approach to Sparse Reinforcement Learning Wonyoung Kim, Garud Iyengar, Assaf Zeevi

NeurIPS 2024 Is Cross-Validation the Gold Standard to Estimate Out-of-Sample Model Performance? Garud Iyengar, Henry Lam, Tianyu Wang

IJCAI 2024 Layered Graph Security Games Jakub Cerný, Chun Kai Ling, Christian Kroer, Garud Iyengar

ICML 2023 Causal Bounds in Quasi-Markovian Graphs Madhumitha Shridharan, Garud Iyengar

AISTATS 2023 Hedging Against Complexity: Distributionally Robust Optimization with Parametric Approximation Garud Iyengar, Henry Lam, Tianyu Wang

ICML 2023 Improved Algorithms for Multi-Period Multi-Class Packing Problems with Bandit Feedback Wonyoung Kim, Garud Iyengar, Assaf Zeevi

JMLR 2023 Scalable Computation of Causal Bounds Madhumitha Shridharan, Garud Iyengar

ICML 2022 Scalable Computation of Causal Bounds Madhumitha Shridharan, Garud Iyengar

AAAI 2021 Multinomial Logit Contextual Bandits: Provable Optimality and Practicality Min-hwan Oh, Garud Iyengar

ICML 2021 Sparsity-Agnostic Lasso Bandit Min-Hwan Oh, Garud Iyengar, Assaf Zeevi

ICMLW 2019 Multinomial Logit Contextual Bandits Min-hwan Oh, Garud Iyengar

NeurIPS 2019 Thompson Sampling for Multinomial Logit Contextual Bandits Min-hwan Oh, Garud Iyengar

AISTATS 2017 Linear Convergence of Stochastic Frank Wolfe Variants Donald Goldfarb, Garud Iyengar, Chaoxu Zhou

ICML 2015 An Asynchronous Distributed Proximal Gradient Method for Composite Convex Optimization Necdet Aybat, Zi Wang, Garud Iyengar