3 citations · 5 across the 7 of their papers we have counts for
4 papers · 1 filter
Search-Augmented Masked Diffusion Models for Constrained Generation
Huu Binh Ta, Michael Cardei, Alvaro Velasquez +1
Discrete diffusion models generate sequences by iteratively denoising samples corrupted by categorical noise, offering an appealing alternative to autoregressive decoding for struc…
A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models
Longchao Da, Justin Turnau, Thirulogasankar Pranav Kutralingam +3
Deep Reinforcement Learning (RL) has been explored and verified to be effective in solving decision-making tasks in various domains, such as robotics, transportation, recommender s…
Criticality and Safety Margins for Reinforcement Learning
Alexander Grushin, Walt Woods, Alvaro Velasquez +1
State of the art reinforcement learning methods sometimes encounter unsafe situations. Identifying when these situations occur is of interest both for post-hoc analysis and during…
Combining AI Control Systems and Human Decision Support via Robustness and Criticality
Walt Woods, Alexander Grushin, Simon Khan +1
AI-enabled capabilities are reaching the requisite level of maturity to be deployed in the real world, yet do not always make correct or safe decisions. One way of addressing these…