4 papers
A Geometric Approach to Optimal Experimental Design
Gavin Kerrigan, Christian A. Naesseth, Tom Rainforth
We introduce a novel geometric framework for optimal experimental design (OED). Traditional OED approaches, such as those based on mutual information, rely explicitly on probabilit…
Active Learning with Task-Driven Representations for Messy Pools
Kianoosh Ashouritaklimi, Tom Rainforth
Active learning has the potential to be especially useful for messy, uncurated pools where datapoints vary in relevance to the target task. However, state-of-the-art approaches to…
BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Design
Deepro Choudhury, Sinead Williamson, Adam Goliński +5
We propose a general-purpose approach for improving the ability of large language models (LLMs) to intelligently and adaptively gather information from a user or other external sou…
Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design
Marcel Hedman, Desi R. Ivanova, Cong Guan +1
We develop a semi-amortized, policy-based, approach to Bayesian experimental design (BED) called Stepwise Deep Adaptive Design (Step-DAD). Like existing, fully amortized, policy-ba…