Rainforth, Tom

50 publications

ICML 2025 Do Bayesian Neural Networks Actually Behave like Bayesian Models? Gábor Pituk, Vik Shirvaikar, Tom Rainforth

ICML 2025 Rethinking Aleatoric and Epistemic Uncertainty Freddie Bickford Smith, Jannik Kossen, Eleanor Trollope, Mark Van Der Wilk, Adam Foster, Tom Rainforth

NeurIPS 2025 Scaling up Active Testing to Large Language Models Gabrielle Berrada, Jannik Kossen, Freddie Bickford Smith, Muhammed Razzak, Yarin Gal, Tom Rainforth

ICLR 2025 Shh, Don't Say That! Domain Certification in LLMs Cornelius Emde, Alasdair Paren, Preetham Arvind, Maxime Guillaume Kayser, Tom Rainforth, Thomas Lukasiewicz, Philip Torr, Adel Bibi

ICLRW 2025 Shh, Don't Say That! Domain Certification in LLMs Cornelius Emde, Alasdair Paren, Preetham Arvind, Maxime Kayser, Tom Rainforth, Thomas Lukasiewicz, Bernard Ghanem, Philip Torr, Adel Bibi

ICML 2025 Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design Marcel Hedman, Desi R. Ivanova, Cong Guan, Tom Rainforth

AISTATS 2024 Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support Tim Reichelt, Luke Ong, Tom Rainforth

ICML 2024 Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, Tommi Jaakkola

ICLRW 2024 Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, Tommi Jaakkola

ICLR 2024 In-Context Learning Learns Label Relationships but Is Not Conventional Learning Jannik Kossen, Yarin Gal, Tom Rainforth

TMLR 2024 Incorporating Unlabelled Data into Bayesian Neural Networks Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin

AISTATS 2024 Making Better Use of Unlabelled Data in Bayesian Active Learning Freddie Bickford Smith, Adam Foster, Tom Rainforth

AISTATS 2024 On the Expected Size of Conformal Prediction Sets Guneet S. Dhillon, George Deligiannidis, Tom Rainforth

NeurIPSW 2024 Rethinking Aleatoric and Epistemic Uncertainty Freddie Bickford Smith, Jannik Kossen, Eleanor Trollope, Mark van der Wilk, Adam Foster, Tom Rainforth

ICLR 2024 SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning Ning Miao, Yee Whye Teh, Tom Rainforth

NeurIPSW 2024 Shh, Don't Say That! Domain Certification in LLMs Cornelius Emde, Preetham Arvind, Alasdair Paren, Maxime Kayser, Tom Rainforth, Thomas Lukasiewicz, Philip Torr, Adel Bibi

ICML 2023 CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster

AISTATS 2023 Do Bayesian Neural Networks Need to Be Fully Stochastic? Mrinank Sharma, Sebastian Farquhar, Eric Nalisnick, Tom Rainforth

ICML 2023 Learning Instance-Specific Augmentations by Capturing Local Invariances Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh, Adam Foster, Hyunjik Kim

AISTATS 2023 Prediction-Oriented Bayesian Active Learning Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth

AISTATS 2022 Amortized Rejection Sampling in Universal Probabilistic Programming Saeid Naderiparizi, Adam Scibior, Andreas Munk, Mehrdad Ghadiri, Atilim Gunes Baydin, Bradley J. Gram-Hansen, Christian A. Schroeder De Witt, Robert Zinkov, Philip Torr, Tom Rainforth, Yee Whye Teh, Frank Wood

AISTATS 2022 Certifiably Robust Variational Autoencoders Ben Barrett, Alexander Camuto, Matthew Willetts, Tom Rainforth

UAI 2022 Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently Tim Reichelt, Adam Goliński, Luke Ong, Tom Rainforth

ICLR 2022 Learning Multimodal VAEs Through Mutual Supervision Tom Joy, Yuge Shi, Philip Torr, Tom Rainforth, Sebastian M Schmon, Siddharth N

ICLR 2022 On Incorporating Inductive Biases into VAEs Ning Miao, Emile Mathieu, Siddharth N, Yee Whye Teh, Tom Rainforth

AISTATS 2021 Towards a Theoretical Understanding of the Robustness of Variational Autoencoders Alexander Camuto, Matthew Willetts, Stephen Roberts, Chris Holmes, Tom Rainforth

ICML 2021 Active Testing: Sample-Efficient Model Evaluation Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth

ICLR 2021 Capturing Label Characteristics in VAEs Tom Joy, Sebastian Schmon, Philip Torr, Siddharth N, Tom Rainforth

NeurIPSW 2021 Certifiably Robust Variational Autoencoders Ben Barrett, Alexander Camuto, Matthew Willetts, Tom Rainforth

ICML 2021 Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design Adam Foster, Desi R Ivanova, Ilyas Malik, Tom Rainforth

ICLR 2021 Improving Transformation Invariance in Contrastive Representation Learning Adam Foster, Rattana Pukdee, Tom Rainforth

ICLR 2021 Improving VAEs' Robustness to Adversarial Attack Matthew JF Willetts, Alexander Camuto, Tom Rainforth, S Roberts, Christopher C Holmes

ICML 2021 On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth

ICLR 2021 On Statistical Bias in Active Learning: How and When to Fix It Sebastian Farquhar, Yarin Gal, Tom Rainforth

ICML 2021 Probabilistic Programs with Stochastic Conditioning David Tolpin, Yuan Zhou, Tom Rainforth, Hongseok Yang

UAI 2021 Statistically Robust Neural Network Classification Benjie Wang, Stefan Webb, Tom Rainforth

AISTATS 2020 A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments Adam Foster, Martin Jankowiak, Matthew O’Meara, Yee Whye Teh, Tom Rainforth

ICML 2020 Divide, Conquer, and Combine: A New Inference Strategy for Probabilistic Programs with Stochastic Support Yuan Zhou, Hongseok Yang, Yee Whye Teh, Tom Rainforth

JMLR 2020 Target–Aware Bayesian Inference: How to Beat Optimal Conventional Estimators Tom Rainforth, Adam Golinski, Frank Wood, Sheheryar Zaidi

ICLR 2019 A Statistical Approach to Assessing Neural Network Robustness Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar

ICML 2019 Amortized Monte Carlo Integration Adam Golinski, Frank Wood, Tom Rainforth

ICML 2019 Disentangling Disentanglement in Variational Autoencoders Emile Mathieu, Tom Rainforth, N Siddharth, Yee Whye Teh

AISTATS 2019 LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models Yuan Zhou, Bradley J. Gram-Hansen, Tobias Kohn, Tom Rainforth, Hongseok Yang, Frank Wood

ICLR 2018 Auto-Encoding Sequential Monte Carlo Tuan Anh Le, Maximilian Igl, Tom Rainforth, Tom Jin, Frank Wood

NeurIPS 2018 Faithful Inversion of Generative Models for Effective Amortized Inference Stefan Webb, Adam Golinski, Rob Zinkov, Siddharth N, Tom Rainforth, Yee Whye Teh, Frank Wood

UAI 2018 Nesting Probabilistic Programs Tom Rainforth

ICML 2018 On Nesting Monte Carlo Estimators Tom Rainforth, Rob Cornish, Hongseok Yang, Andrew Warrington, Frank Wood

ICML 2018 Tighter Variational Bounds Are Not Necessarily Better Tom Rainforth, Adam Kosiorek, Tuan Anh Le, Chris Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh

NeurIPS 2016 Bayesian Optimization for Probabilistic Programs Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A Osborne, Frank Wood

ICML 2016 Interacting Particle Markov Chain Monte Carlo Tom Rainforth, Christian Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem Vandemeent, Arnaud Doucet, Frank Wood