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04/17/2023
Prediction-Oriented Bayesian Active Learning
Information-theoretic approaches to active learning have traditionally f...
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02/28/2023
Modern Bayesian Experimental Design
Bayesian experimental design (BED) provides a powerful and general frame...
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02/21/2023
Differentiable Multi-Target Causal Bayesian Experimental Design
We introduce a gradient-based approach for the problem of Bayesian optim...
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11/03/2021
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods
We introduce implicit Deep Adaptive Design (iDAD), a new method for perf...
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06/18/2021
On Contrastive Representations of Stochastic Processes
Learning representations of stochastic processes is an emerging problem ...
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06/15/2021
Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness
Learning meaningful representations of data that can address challenges ...
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03/03/2021
Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design
We introduce Deep Adaptive Design (DAD), a method for amortizing the cos...
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10/19/2020
Improving Transformation Invariance in Contrastive Representation Learning
We propose methods to strengthen the invariance properties of representa...
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11/01/2019
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments
We introduce a fully stochastic gradient based approach to Bayesian opti...
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03/13/2019
Variational Estimators for Bayesian Optimal Experimental Design
Bayesian optimal experimental design (BOED) is a principled framework fo...
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03/13/2019
Variational Bayesian Optimal Experimental Design
Bayesian optimal experimental design (BOED) is a principled framework fo...
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07/09/2018