no code implementations • 6 Oct 2023 • Marvin Schmitt, Desi R. Ivanova, Daniel Habermann, Ullrich Köthe, Paul-Christian Bürkner, Stefan T. Radev
We propose a method to improve the efficiency and accuracy of amortized Bayesian inference by leveraging universal symmetries in the joint probabilistic model of parameters and data.
1 code implementation • 27 Feb 2023 • Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster
We formalize the problem of contextual optimization through the lens of Bayesian experimental design and propose CO-BED -- a general, model-agnostic framework for designing contextual experiments using information-theoretic principles.
1 code implementation • 21 Feb 2023 • Yashas Annadani, Panagiotis Tigas, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer
We introduce a gradient-based approach for the problem of Bayesian optimal experimental design to learn causal models in a batch setting -- a critical component for causal discovery from finite data where interventions can be costly or risky.
no code implementations • 12 Jul 2022 • Desi R. Ivanova, Joel Jennings, Cheng Zhang, Adam Foster
In this paper we introduce a model-agnostic framework for gathering data to evaluate and improve contextual decision making through Bayesian Experimental Design.
1 code implementation • NeurIPS 2021 • Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Tom Rainforth
We introduce implicit Deep Adaptive Design (iDAD), a new method for performing adaptive experiments in real-time with implicit models.
1 code implementation • 3 Mar 2021 • Adam Foster, Desi R. Ivanova, Ilyas Malik, Tom Rainforth
We introduce Deep Adaptive Design (DAD), a method for amortizing the cost of adaptive Bayesian experimental design that allows experiments to be run in real-time.