71 citations · 604 across the 67 of their papers we have counts for
37 papers · 1 filter
Designing Experiments for External Decision Makers
Sahel Iqbal, Tom Rainforth
Standard Bayesian experimental design (BED) methods assume the experimenter alone will act on the data, choosing the best action given their own beliefs and goals. In practice, dat…
Prediction-Powered Active Testing
Kianoosh Ashouritaklimi, Valentin Kilian, Daolang Huang +2
Active testing provides a label--efficient approach to risk estimation by adaptively selecting which test points should be labelled. However, existing estimators fail to exploit th…
Bayesian Experimental Design via Score Matching
Angus Phillips, Gavin Kerrigan, Tom Rainforth
Policy-based approaches to Bayesian experimental design (BED) allow the learning of deep policy networks that adaptively make intelligent design decisions based on previously colle…
Action-BED: Task-Driven Bayesian Experimental Design with Singly Intractable Objectives
Tom Rossa, Angus Phillips, Tom Rainforth
Bayesian experimental design (BED) has traditionally been based on maximising expected uncertainty reductions from prior to posterior. A major shortfall of this approach is that it…
FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data
Marcel Hedman, Emily Alger, Brieuc Lehmann +2
Frameworks for ensuring fairness in machine learning typically focus on learning fair models from existing data. But this endeavor is often undermined by biases already present in…
Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design
Andrew Campbell, Jason Yim, Regina Barzilay +2
Combining discrete and continuous data is an important capability for generative models. We present Discrete Flow Models (DFMs), a new flow-based model of discrete data that provid…