5 papers
Interactive Multi-Objective Probabilistic Preference Learning with Soft and Hard Bounds
Edward Chen, Sang T. Truong, Natalie Dullerud +2
High-stakes decision-making involves navigating multiple competing objectives with expensive evaluations. For instance, in brachytherapy, clinicians must balance maximizing tumor c…
FlowRL: A Taxonomy and Modular Framework for Reinforcement Learning with Diffusion Policies
Chenxiao Gao, Edward Chen, Tianyi Chen +1
Thanks to their remarkable flexibility, diffusion models and flow models have emerged as promising candidates for policy representation. However, efficient reinforcement learning (…
Discovering Implicit Large Language Model Alignment Objectives
Edward Chen, Sanmi Koyejo, Carlos Guestrin
Large language model (LLM) alignment relies on complex reward signals that often obscure the specific behaviors being incentivized, creating critical risks of misalignment and rewa…
Modeling Multi-Objective Tradeoffs with Monotonic Utility Functions
Edward Chen, Natalie Dullerud, Thomas Niedermayr +5
Countless science and engineering applications in multi-objective optimization (MOO) necessitate that decision-makers (DMs) select a Pareto-optimal (PO) solution which aligns with…
ALMo: Interactive Aim-Limit-Defined, Multi-Objective System for Personalized High-Dose-Rate Brachytherapy Treatment Planning and Visualization for Cervical Cancer
Edward Chen, Natalie Dullerud, Pang Wei Koh +4
In complex clinical decision-making, clinicians must often track a variety of competing metrics defined by aim (ideal) and limit (strict) thresholds. Sifting through these high-dim…