Eric V Mazumdar — accepted AI-conference papers

2023

Synthesizing Reactive Test Environments for Autonomous Systems: Testing Reach-Avoid Specifications with Multi-Commodity Flows

Apurva Badithela, Josefine B. Graebener, Wyatt Ubellacker, Eric V. Mazumdar, Aaron D. Ames, Richard M. Murray

ICRA 2023poster

We study automated test generation for testing discrete decision-making modules in autonomous systems. Linear temporal logic is used to encode the system specification - requirements of the system under test - and the test specification, which is unknown to the system and describes the desired test…

Cited by 3 Source ↗Scholar ↗

2022

Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization

Yaodong Yu, Tianyi Lin, Eric V. Mazumdar, Michael Jordan

AISTATS 2022poster

Distributionally robust supervised learning (DRSL) is emerging as a key paradigm for building reliable machine learning systems for real-world applications–reflecting the need for classifiers and predictive models that are robust to the distribution shifts that arise from phenomena such as selection…

Cited by 37 Source ↗PDF ↗Scholar ↗

2022

Langevin Monte Carlo for Contextual Bandits

Pan Xu, Hongkai Zheng, Eric V Mazumdar, Kamyar Azizzadenesheli, Animashree Anandkumar

ICML 2022spotlight

We study the efficiency of Thompson sampling for contextual bandits. Existing Thompson sampling-based algorithms need to construct a Laplace approximation (i.e., a Gaussian distribution) of the posterior distribution, which is inefficient to sample in high dimensional applications for general covari…

Cited by 43 Source ↗PDF ↗code ↗Scholar ↗