262 citations · 652 across the 74 of their papers we have counts for
6 papers · 1 filter
BOReFT: Manifold Steering of Language Models for Black-box Optimization
Dhruv Agarwal, Rico Angell, Kavitha Srinivas +4
Language models are increasingly used as proposal models for black-box search, from program optimization to molecular design. Existing approaches typically improve proposals throug…
Trustworthy Protein-Ligand Binding Affinity Prediction via Reliability-Aware Multi-Engine Fusion
Yongchan Hong, Defu Cao, Wenjin Liu +6
Accurate protein-ligand binding affinity prediction is central to computational drug discovery, yet modern docking engines frequently disagree without indicating which prediction t…
An Exam for Active Observers
Jiarui Zhang, Muzi Tao, Shangshang Wang +3
Human vision is a closed loop: gaze is continuously redirected by intermediate hypotheses rather than a single snapshot. Decades of psychophysics and cognitive science have argued…
Rethinking RL for LLM Reasoning: It's Sparse Policy Selection, Not Capability Learning
Ömer Faruk Akgül, Rajgopal Kannan, Willie Neiswanger +1
Reinforcement learning has become the standard for improving reasoning in large language models, yet evidence increasingly suggests that RL does not teach new strategies; it redist…
Supercharging Simulation-Based Inference for Bayesian Optimal Experimental Design
Samuel Klein, Willie Neiswanger, Daniel Ratner +2
Bayesian optimal experimental design (BOED) seeks to maximize the expected information gain (EIG) of experiments. This requires a likelihood estimate, which in many settings is int…
Neural Nonmyopic Bayesian Optimization in Dynamic Cost Settings
Sang T. Truong, Duc Q. Nguyen, Willie Neiswanger +4
Bayesian optimization (BO) is a common framework for optimizing black-box functions, yet most existing methods assume static query costs and rely on myopic acquisition strategies.…