Canonical Correlation Forests. - Tom Rainforth; Frank D. Wood (CoRR, 2015)

Interacting Particle Markov Chain Monte Carlo. - Tom Rainforth; Christian A. Naesseth; Fredrik Lindsten; Brooks Paige; Jan-Willem van de Meent; Arnaud Doucet; Frank D. Wood (ICML, 2016)

Bayesian Optimization for Probabilistic Programs. - Tom Rainforth; Tuan Anh Le 001; Jan-Willem van de Meent; Michael A. Osborne; Frank D. Wood (NIPS, 2016)

Probabilistic structure discovery in time series data. - David Janz; Brooks Paige; Tom Rainforth; Jan-Willem van de Meent; Frank D. Wood (CoRR, 2016)

Automating inference, learning, and design using probabilistic programming. - Thomas Rainforth (2017)

Auto-Encoding Sequential Monte Carlo. - Tuan Anh Le 001; Maximilian Igl; Tom Rainforth; Tom Jin; Frank Wood (ICLR (Poster), 2018)

On Nesting Monte Carlo Estimators. - Tom Rainforth; Robert Cornish; Hongseok Yang; Andrew Warrington (ICML, 2018)

Tighter Variational Bounds are Not Necessarily Better. - Tom Rainforth; Adam R. Kosiorek; Tuan Anh Le 001; Chris J. Maddison; Maximilian Igl; Frank Wood; Yee Whye Teh (ICML, 2018)

Faithful Inversion of Generative Models for Effective Amortized Inference. - Stefan Webb; Adam Golinski; Robert Zinkov; Siddharth Narayanaswamy; Tom Rainforth; Yee Whye Teh; Frank Wood (NeurIPS, 2018)

Nesting Probabilistic Programs. - Tom Rainforth (UAI, 2018)

On Exploration, Exploitation and Learning in Adaptive Importance Sampling. - Xiaoyu Lu; Tom Rainforth; Yuan Zhou 013; Jan-Willem van de Meent; Yee Whye Teh (CoRR, 2018)

Disentangling Disentanglement. - Emile Mathieu; Tom Rainforth; Siddharth Narayanaswamy; Yee Whye Teh (CoRR, 2018)

LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models. - Yuan Zhou 013; Bradley J. Gram-Hansen; Tobias Kohn; Tom Rainforth; Hongseok Yang; Frank Wood (AISTATS, 2019)

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

Amortized Monte Carlo Integration. - Adam Golinski; Frank Wood; Tom Rainforth (ICML, 2019)

Disentangling Disentanglement in Variational Autoencoders. - Emile Mathieu; Tom Rainforth; N. Siddharth 001; Yee Whye Teh (ICML, 2019)

Variational Bayesian Optimal Experimental Design. - Adam Foster 001; Martin Jankowiak; Eli Bingham; Paul Horsfall; Yee Whye Teh; Tom Rainforth; Noah D. Goodman (NeurIPS, 2019)

On the Fairness of Disentangled Representations. - Francesco Locatello; Gabriele Abbati; Thomas Rainforth; Stefan Bauer; Bernhard Schölkopf; Olivier Bachem (NeurIPS, 2019)

Variational Estimators for Bayesian Optimal Experimental Design. - Adam Foster 001; Martin Jankowiak; Eli Bingham; Paul Horsfall; Yee Whye Teh; Tom Rainforth; Noah D. Goodman (CoRR, 2019)

Hijacking Malaria Simulators with Probabilistic Programming. - Bradley Gram-Hansen; Christian Schröder de Witt; Tom Rainforth; Philip H. S. Torr; Yee Whye Teh; Atilim Günes Baydin (CoRR, 2019)

A note on blind contact tracing at scale with applications to the COVID-19 pandemic. - Jack K. Fitzsimons; Atul Mantri; Robert Pisarczyk; Tom Rainforth; Zhikuan Zhao (ARES, 2020)

A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. - Adam Foster 001; Martin Jankowiak; Matthew O'Meara; Yee Whye Teh; Tom Rainforth (AISTATS, 2020)

Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support. - Yuan Zhou 013; Hongseok Yang; Yee Whye Teh; Tom Rainforth (ICML, 2020)

Rethinking Semi-Supervised Learning in VAEs. - Tom Joy; Sebastian M. Schmon; Philip H. S. Torr; N. Siddharth 001; Tom Rainforth (CoRR, 2020)

Target-Aware Bayesian Inference: How to Beat Optimal Conventional Estimators. - Tom Rainforth; Adam Golinski; Frank Wood; Sheheryar Zaidi (J. Mach. Learn. Res., 2020)

Towards a Theoretical Understanding of the Robustness of Variational Autoencoders. - Alexander Camuto; Matthew Willetts; Stephen J. Roberts; Chris C. Holmes; Tom Rainforth (AISTATS, 2021)

On Statistical Bias In Active Learning: How and When to Fix It. - Sebastian Farquhar; Yarin Gal; Tom Rainforth (ICLR, 2021)

Improving Transformation Invariance in Contrastive Representation Learning. - Adam Foster 001; Rattana Pukdee; Tom Rainforth (ICLR, 2021)

Capturing Label Characteristics in VAEs. - Tom Joy; Sebastian M. Schmon; Philip H. S. Torr; Siddharth Narayanaswamy; Tom Rainforth (ICLR, 2021)

Improving VAEs' Robustness to Adversarial Attack. - Matthew Willetts; Alexander Camuto; Tom Rainforth; Stephen J. Roberts; Christopher C. Holmes (ICLR, 2021)

Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design. - Adam Foster 001; Desi R. Ivanova; Ilyas Malik; Tom Rainforth (ICML, 2021)

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

Probabilistic Programs with Stochastic Conditioning. - David Tolpin; Yuan Zhou 013; Tom Rainforth; Hongseok Yang (ICML, 2021)

Online Variational Filtering and Parameter Learning. - Andrew Campbell; Yuyang Shi 002; Thomas Rainforth; Arnaud Doucet (NeurIPS, 2021)

Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods. - Desi R. Ivanova; Adam Foster 001; Steven Kleinegesse; Michael U. Gutmann; Thomas Rainforth (NeurIPS, 2021)

Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning. - Jannik Kossen; Neil Band; Clare Lyle; Aidan N. Gomez; Thomas Rainforth; Yarin Gal (NeurIPS, 2021)

Group Equivariant Subsampling. - Jin Xu 011; Hyunjik Kim; Thomas Rainforth; Yee Whye Teh (NeurIPS, 2021)

Statistically robust neural network classification. - Benjie Wang 001; Stefan Webb; Tom Rainforth (UAI, 2021)

Expectation Programming. - Tim Reichelt; Adam Golinski; Luke Ong; Tom Rainforth (CoRR, 2021)

Active Learning under Pool Set Distribution Shift and Noisy Data. - Andreas Kirsch 002; Tom Rainforth; Yarin Gal (CoRR, 2021)

InteL-VAEs: Adding Inductive Biases to Variational Auto-Encoders via Intermediary Latents. - Ning Miao; Emile Mathieu; N. Siddharth 001; Yee Whye Teh; Tom Rainforth (CoRR, 2021)

Certifiably Robust Variational Autoencoders. - Ben Barrett; Alexander Camuto; Matthew Willetts; Tom Rainforth (AISTATS, 2022)

Amortized Rejection Sampling in Universal Probabilistic Programming. - Saeid Naderiparizi; Adam Scibior; Andreas Munk 001; Mehrdad Ghadiri; Atilim Gunes Baydin; Bradley J. Gram-Hansen; Christian A. Schröder de Witt; Robert Zinkov; Philip H. S. Torr; Tom Rainforth; Yee Whye Teh; Frank Wood (AISTATS, 2022)

Learning Multimodal VAEs through Mutual Supervision. - Tom Joy; Yuge Shi; Philip H. S. Torr; Tom Rainforth; Sebastian M. Schmon; Siddharth Narayanaswamy (ICLR, 2022)

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

A Continuous Time Framework for Discrete Denoising Models. - Andrew Campbell; Joe Benton; Valentin De Bortoli; Thomas Rainforth; George Deligiannidis; Arnaud Doucet (NeurIPS, 2022)

Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation. - Jannik Kossen; Sebastian Farquhar; Yarin Gal; Thomas Rainforth (NeurIPS, 2022)

Rethinking Variational Inference for Probabilistic Programs with Stochastic Support. - Tim Reichelt; Luke Ong; Thomas Rainforth (NeurIPS, 2022)

Expectation programming: Adapting probabilistic programming systems to estimate expectations efficiently. - Tim Reichelt; Adam Golinski; Luke Ong; Tom Rainforth (UAI, 2022)

Learning Instance-Specific Data Augmentations. - Ning Miao; Emile Mathieu; Yann Dubois; Tom Rainforth; Yee Whye Teh; Adam Foster 001; Hyunjik Kim (CoRR, 2022)

Do Bayesian Neural Networks Need To Be Fully Stochastic? - Mrinank Sharma; Sebastian Farquhar; Eric T. Nalisnick; Tom Rainforth (AISTATS, 2023)

Prediction-Oriented Bayesian Active Learning. - Freddie Bickford Smith; Andreas Kirsch 002; Sebastian Farquhar; Yarin Gal; Adam Foster 001; Tom Rainforth (AISTATS, 2023)

CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. - Desi R. Ivanova; Joel Jennings; Tom Rainforth; Cheng Zhang 005; Adam Foster 001 (ICML, 2023)

Learning Instance-Specific Augmentations by Capturing Local Invariances. - Ning Miao; Tom Rainforth; Emile Mathieu; Yann Dubois; Yee Whye Teh; Adam Foster 001; Hyunjik Kim (ICML, 2023)

Deep Stochastic Processes via Functional Markov Transition Operators. - Jin Xu 011; Emilien Dupont; Kaspar Märtens; Thomas Rainforth; Yee Whye Teh (NeurIPS, 2023)

Trans-Dimensional Generative Modeling via Jump Diffusion Models. - Andrew Campbell; William Harvey 002; Christian Weilbach; Valentin De Bortoli; Thomas Rainforth; Arnaud Doucet (NeurIPS, 2023)

Modern Bayesian Experimental Design. - Tom Rainforth; Adam Foster 001; Desi R. Ivanova; Freddie Bickford Smith (CoRR, 2023)

In-Context Learning in Large Language Models Learns Label Relationships but Is Not Conventional Learning. - Jannik Kossen; Tom Rainforth; Yarin Gal (CoRR, 2023)

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

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

Making Better Use of Unlabelled Data in Bayesian Active Learning. - Freddie Bickford Smith; Adam Foster 001; Tom Rainforth (AISTATS, 2024)

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

SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning. - Ning Miao; Yee Whye Teh; Tom Rainforth (ICLR, 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 S. Jaakkola (ICML, 2024)

Rethinking Aleatoric and Epistemic Uncertainty. - Freddie Bickford Smith; Jannik Kossen; Eleanor Trollope; Mark van der Wilk; Adam Foster 001; Tom Rainforth (CoRR, 2024)

Incorporating Unlabelled Data into Bayesian Neural Networks. - Mrinank Sharma; Tom Rainforth; Yee Whye Teh; Vincent Fortuin (Trans. Mach. Learn. Res., 2024)

Shh, don't say that! Domain Certification in LLMs. - Cornelius Emde; Alasdair Paren; Preetham Arvind; Maxime Guillaume Kayser; Tom Rainforth; Thomas Lukasiewicz; Philip Torr 001; Adel Bibi (ICLR, 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 H. S. Torr; Adel Bibi (CoRR, 2025)

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

Prediction-Oriented Subsampling from Data Streams. - Benedetta Lavinia Mussati; Freddie Bickford Smith; Tom Rainforth; Stephen Roberts 001 (CoRR, 2025)

Scaling Up Active Testing to Large Language Models. - Gabrielle Berrada; Jannik Kossen; Muhammed Razzak; Freddie Bickford Smith; Yarin Gal; Tom Rainforth (CoRR, 2025)