5 papers
Multi-Objective Protein Optimization via Modular Diffusion Steering
Meir Roketlishvili, Felix Huber, Wenda Chu +2
Protein engineering often requires improving several properties simultaneously, motivating the search for high-performing sequences that represent useful fitness tradeoffs. Discove…
Actor-Curator: Co-adaptive Curriculum Learning via Policy-Improvement Bandits for RL Post-Training
Zhengyao Gu, Jonathan Light, Raul Astudillo +7
Post-training large foundation models with reinforcement learning typically relies on massive and heterogeneous datasets, making effective curriculum learning both critical and cha…
Steering Generative Models with Experimental Data for Protein Fitness Optimization
Jason Yang, Wenda Chu, Daniel Khalil +4
Protein fitness optimization involves finding a protein sequence that maximizes desired quantitative properties in a combinatorially large design space of possible sequences. Recen…
Practical Bayesian Algorithm Execution via Posterior Sampling
Chu Xin Cheng, Raul Astudillo, Thomas Desautels +1
We consider Bayesian algorithm execution (BAX), a framework for efficiently selecting evaluation points of an expensive function to infer a property of interest encoded as the outp…
Preferential Multi-Objective Bayesian Optimization
Raul Astudillo, Kejun Li, Maegan Tucker +3
Preferential Bayesian optimization (PBO) is a framework for optimizing a decision-maker's latent preferences over available design choices. While preferences often involve multiple…