Tom Rainforth

Bayesian Experimental Design via Score Matching UAI 2026 Do Bayesian Neural Networks Actually Behave Like Bayesian Models? ICML 2025 Rethinking Aleatoric and Epistemic Uncertainty ICML 2025 Shh, don't say that! Domain Certification in LLMs ICLR 2025 Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design ICML 2025 Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support AISTATS 2024 Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design ICML 2024 In-Context Learning Learns Label Relationships but Is Not Conventional Learning ICLR 2024 Making Better Use of Unlabelled Data in Bayesian Active Learning AISTATS 2024 On the Expected Size of Conformal Prediction Sets AISTATS 2024 SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning ICLR 2024 CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design ICML 2023 Do Bayesian Neural Networks Need To Be Fully Stochastic? AISTATS 2023 Learning Instance-Specific Augmentations by Capturing Local Invariances ICML 2023 Prediction-Oriented Bayesian Active Learning AISTATS 2023 Amortized Rejection Sampling in Universal Probabilistic Programming AISTATS 2022 Certifiably Robust Variational Autoencoders AISTATS 2022 Expectation programming: Adapting probabilistic programming systems to estimate expectations efficiently UAI 2022 Learning Multimodal VAEs through Mutual Supervision ICLR 2022 On Incorporating Inductive Biases into VAEs ICLR 2022 Active Testing: Sample-Efficient Model Evaluation ICML 2021 Capturing Label Characteristics in VAEs ICLR 2021 Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design ICML 2021 Improving Transformation Invariance in Contrastive Representation Learning ICLR 2021 Improving VAEs' Robustness to Adversarial Attack ICLR 2021 On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes ICML 2021 On Statistical Bias In Active Learning: How and When to Fix It ICLR 2021 Probabilistic Programs with Stochastic Conditioning ICML 2021 Statistically robust neural network classification UAI 2021 Towards a Theoretical Understanding of the Robustness of Variational Autoencoders AISTATS 2021 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 Target–Aware Bayesian Inference: How to Beat Optimal Conventional Estimators JMLR 2020 Amortized Monte Carlo Integration ICML 2019 A Statistical Approach to Assessing Neural Network Robustness ICLR 2019 Disentangling Disentanglement in Variational Autoencoders ICML 2019 LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models AISTATS 2019 Auto-Encoding Sequential Monte Carlo ICLR 2018 Faithful Inversion of Generative Models for Effective Amortized Inference NeurIPS 2018 On Nesting Monte Carlo Estimators ICML 2018 Tighter Variational Bounds are Not Necessarily Better ICML 2018 Bayesian Optimization for Probabilistic Programs NeurIPS 2016 Interacting Particle Markov Chain Monte Carlo ICML 2016