2 citations · 5 across the 4 of their papers we have counts for
4 papers
Characterizing Out-of-Distribution Error via Optimal Transport
Yuzhe Lu, Yilong Qin, Runtian Zhai +7
Out-of-distribution (OOD) data poses serious challenges in deployed machine learning models, so methods of predicting a model's performance on OOD data without labels are important…
Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy Matching
Yecheng Jason Ma, Andrew Shen, Dinesh Jayaraman +1
We propose State Matching Offline DIstribution Correction Estimation (SMODICE), a novel and versatile regression-based offline imitation learning (IL) algorithm derived via state-o…
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning
Yecheng Jason Ma, Andrew Shen, Osbert Bastani +1
Reinforcement Learning (RL) agents in the real world must satisfy safety constraints in addition to maximizing a reward objective. Model-based RL algorithms hold promise for reduci…
A Practical Analysis of Rust's Concurrency Story
Aditya Saligrama, Andrew Shen, Jon Gjengset
Correct concurrent programs are difficult to write; when multiple threads mutate shared data, they may lose writes, corrupt data, or produce erratic program behavior. While many of…