← Authors

Tom Rainforth

42 papers · 2016–2025 · 6 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (6) πŸŒ‰ Interdisciplinary Bridge 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (8) πŸ—ΊοΈ Taxonomy Completionist (12) 🧭 Keyword Pioneer πŸƒ Academic Marathon (9) πŸ† Keyword Champion (2) πŸ”¬ Deep Specialist (13) πŸ‘‘ Triple Crown 🀝 Dynamic Duo (10) πŸ—ƒοΈ Keyword Collector (110) ❓ The Questioner (2) ⚑ Prolific Year (5) πŸš€ Conference Pioneer πŸ’Ž Century Club (42) πŸ”₯ Unstoppable (8) πŸ“ˆ Trend Setter

Conferences

ICML (16) ICLR (11) AISTATS (10) NIPS (2) UAI (2) JMLR (1)

Top co-authors

Frank Wood (10) Yee Whye Teh (10) Adam Foster (8) Yarin Gal (5) Hongseok Yang (4) Philip Torr (4) Siddharth N (4) Adam Golinski (4) Sebastian Farquhar (4) Alexander Camuto (3)

Research topics

Statistics (1)

Keywords

variational inference (11) bayesian inference (6) probabilistic programming (6) amortized inference (5) importance sampling (4) markov chain monte carlo (4) acquisition function (3) variational autoencoder (3) posterior distribution (3) bayesian experimental design (3) posterior approximation (3) predictive performance (2) target function (2) probabilistic program (2) gradient estimator (2) bayesian active learning (2) adversarial robustness (2) generative model (2) data augmentation (1) self-supervised learning (1)

Papers

Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design ICML 2025
Shh, don't say that! Domain Certification in LLMs ICLR 2025
Do Bayesian Neural Networks Actually Behave Like Bayesian Models? ICML 2025
Rethinking Aleatoric and Epistemic Uncertainty ICML 2025
SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning ICLR 2024
In-Context Learning Learns Label Relationships but Is Not Conventional Learning ICLR 2024
Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design ICML 2024
Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support AISTATS 2024
Making Better Use of Unlabelled Data in Bayesian Active Learning AISTATS 2024
On the Expected Size of Conformal Prediction Sets AISTATS 2024
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design ICML 2023
Do Bayesian Neural Networks Need To Be Fully Stochastic? AISTATS 2023
Prediction-Oriented Bayesian Active Learning AISTATS 2023
Learning Instance-Specific Augmentations by Capturing Local Invariances ICML 2023
Certifiably Robust Variational Autoencoders AISTATS 2022
Amortized Rejection Sampling in Universal Probabilistic Programming AISTATS 2022
On Incorporating Inductive Biases into VAEs ICLR 2022
Learning Multimodal VAEs through Mutual Supervision ICLR 2022
Expectation programming: Adapting probabilistic programming systems to estimate expectations efficiently UAI 2022
Statistically robust neural network classification UAI 2021
Improving VAEs' Robustness to Adversarial Attack ICLR 2021
Improving Transformation Invariance in Contrastive Representation Learning ICLR 2021
Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design ICML 2021
Active Testing: Sample-Efficient Model Evaluation ICML 2021
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes ICML 2021
Probabilistic Programs with Stochastic Conditioning ICML 2021
Towards a Theoretical Understanding of the Robustness of Variational Autoencoders AISTATS 2021
Capturing Label Characteristics in VAEs ICLR 2021
On Statistical Bias In Active Learning: How and When to Fix It ICLR 2021
Target–Aware Bayesian Inference: How to Beat Optimal Conventional Estimators JMLR 2020
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments AISTATS 2020
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support ICML 2020
Disentangling Disentanglement in Variational Autoencoders ICML 2019
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models AISTATS 2019
A Statistical Approach to Assessing Neural Network Robustness ICLR 2019
Amortized Monte Carlo Integration ICML 2019
On Nesting Monte Carlo Estimators ICML 2018
Auto-Encoding Sequential Monte Carlo ICLR 2018
Faithful Inversion of Generative Models for Effective Amortized Inference NIPS 2018
Tighter Variational Bounds are Not Necessarily Better ICML 2018
Interacting Particle Markov Chain Monte Carlo ICML 2016
Bayesian Optimization for Probabilistic Programs NIPS 2016