26 papers · 1 filter
Fairness in Reinforcement Learning with Bisimulation Metrics
Sahand Rezaei-Shoshtari, Hanna Yurchyk, Scott Fujimoto +2
Ensuring long-term fairness is crucial when developing automated decision making systems, specifically in dynamic and sequential environments. By maximizing their reward without co…
Mitigating Downstream Model Risks via Model Provenance
Keyu Wang, Abdullah Norozi Iranzad, Scott Schaffter +3
Research and industry are rapidly advancing the innovation and adoption of foundation model-based systems, yet the tools for managing these models have not kept pace. Understanding…
MaestroMotif: Skill Design from Artificial Intelligence Feedback
Martin Klissarov, Mikael Henaff, Roberta Raileanu +7
Describing skills in natural language has the potential to provide an accessible way to inject human knowledge about decision-making into an AI system. We present MaestroMotif, a m…
Parseval Regularization for Continual Reinforcement Learning
Wesley Chung, Lynn Cherif, David Meger +1
Loss of plasticity, trainability loss, and primacy bias have been identified as issues arising when training deep neural networks on sequences of tasks -- all referring to the incr…
Reaction-conditioned De Novo Enzyme Design with GENzyme
Chenqing Hua, Jiarui Lu, Yong Liu +7
The introduction of models like RFDiffusionAA, AlphaFold3, AlphaProteo, and Chai1 has revolutionized protein structure modeling and interaction prediction, primarily from a binding…
QGFN: Controllable Greediness with Action Values
Elaine Lau, Stephen Zhewen Lu, Ling Pan +2
Generative Flow Networks (GFlowNets; GFNs) are a family of energy-based generative methods for combinatorial objects, capable of generating diverse and high-utility samples. Howeve…