Adam Foster
14 papers · 2019–2025 · 4 venues · across top CS/AI venues
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Achievements
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π£ Hot Topic Early Bird π Conference Polyglot Β· Bronze π Academic Marathon Β· Sprint π Interdisciplinary Bridge Β· Silver πΊοΈ Taxonomy Completionist (31)
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(60)
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Century Club Β· Bronze
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Venues
Top co-authors
Research topics
Machine Learning
(11)
Artificial Intelligence
(9)
Learning Types Β· Machine Learning
(7)
Optimization & Theory
(6)
Core AI
(5)
Bayesian & Probabilistic Β· Artificial Intelligence
(4)
Deep Learning
(3)
Bayesian & Probabilistic Β· Machine Learning
(3)
Knowledge & Reasoning
(2)
Mathematics & Optimization
(2)
Learning Paradigms
(2)
Learning Types Β· Deep Learning
(2)
Reasoning
(2)
Reinforcement Learning
(1)
Application Areas
(1)
Keywords
contrastive learning
(4)
amortized inference
(3)
variational inference
(3)
bayesian inference
(3)
representation learning
(3)
bayesian experimental design
(2)
acquisition function
(2)
predictive performance
(2)
experimental design
(2)
bayesian active learning
(2)
expected information gain
(2)
gradient-based optimization
(1)
causal discovery
(1)
self-supervised learning
(1)
structure learning
(1)
active learning
(1)
time series
(1)
stochastic gradient
(1)
deep learning
(1)
causal structure learning
(1)
Papers
Rethinking Aleatoric and Epistemic Uncertainty
ICML 2025
Amortized Active Causal Induction with Deep Reinforcement Learning
NeurIPS 2024
Making Better Use of Unlabelled Data in Bayesian Active Learning
AISTATS 2024
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design
ICML 2023
Differentiable Multi-Target Causal Bayesian Experimental Design
ICML 2023
Learning Instance-Specific Augmentations by Capturing Local Invariances
ICML 2023
Prediction-Oriented Bayesian Active Learning
AISTATS 2023
Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness
ICML 2022
Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design
ICML 2021
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods
NeurIPS 2021
Improving Transformation Invariance in Contrastive Representation Learning
ICLR 2021
On Contrastive Representations of Stochastic Processes
NeurIPS 2021
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments
AISTATS 2020
Variational Bayesian Optimal Experimental Design
NeurIPS 2019