3 papers
cs.LG2025
Efficient Personalization of Generative Models via Optimal Experimental Design
Guy Schacht, Ziyad Sheebaelhamd, Riccardo De Santi +2
Preference learning from human feedback has the ability to align generative models with the needs of end-users. Human feedback is costly and time-consuming to obtain, which creates…
cs.LG2024
Transition Constrained Bayesian Optimization via Markov Decision Processes
Jose Pablo Folch, Calvin Tsay, Robert M Lee +6
Bayesian optimization is a methodology to optimize black-box functions. Traditionally, it focuses on the setting where you can arbitrarily query the search space. However, many rea…
cs.LG2023
Likelihood Ratio Confidence Sets for Sequential Decision Making
Nicolas Emmenegger, Mojmír Mutný, Andreas Krause
Certifiable, adaptive uncertainty estimates for unknown quantities are an essential ingredient of sequential decision-making algorithms. Standard approaches rely on problem-depende…