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06/26/2023
BatchGFN: Generative Flow Networks for Batch Active Learning
We introduce BatchGFN – a novel approach for pool-based active learning ...
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06/02/2023
ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages
In this paper, we introduce a novel method for enhancing the effectivene...
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04/20/2023
B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding
Estimating heterogeneous treatment effects from observational data is a ...
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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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04/21/2022
Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions
Estimating the effects of continuous-valued interventions from observati...
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03/03/2022
Interventions, Where and How? Experimental Design for Causal Models at Scale
Causal discovery from observational and interventional data is challengi...
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11/03/2021
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
Estimating personalized treatment effects from high-dimensional observat...
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10/28/2021
Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific
Aerosol-cloud interactions include a myriad of effects that all begin wh...
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10/22/2021
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
In vitro cellular experimentation with genetic interventions, using for ...
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03/08/2021
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding
We study the problem of learning conditional average treatment effects (...
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02/22/2021
Improving Deterministic Uncertainty Estimation in Deep Learning for Classification and Regression
We propose a new model that estimates uncertainty in a single forward pa...
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07/01/2020
Identifying Causal Effect Inference Failure with Uncertainty-Aware Models
Recommending the best course of action for an individual is a major appl...
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07/27/2018
CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance
We introduce CASED, a novel curriculum sampling algorithm that facilitat...
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07/14/2018
Adversarially Learned Mixture Model
The Adversarially Learned Mixture Model (AMM) is a generative model for ...
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06/03/2018