5 papers
Loss-Driven Bayesian Active Learning
Zhuoyue Huang, Freddie Bickford Smith, Tom Rainforth
The central goal of active learning is to gather data that maximises downstream predictive performance, but popular approaches have limited flexibility in customising this data acq…
BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Design
Deepro Choudhury, Sinead Williamson, Adam GoliÅski +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…
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…
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…
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…