Tom Rainforth

According to our database, Tom Rainforth authored at least 66 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Incorporating Unlabelled Data into Bayesian Neural Networks.

Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin

Trans. Mach. Learn. Res., 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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning.

Ning Miao, Yee Whye Teh, Tom Rainforth

Proceedings of the Twelfth International Conference on Learning Representations, 2024

In-Context Learning Learns Label Relationships but Is Not Conventional Learning.

Jannik Kossen, Yarin Gal, Tom Rainforth

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Making Better Use of Unlabelled Data in Bayesian Active Learning.

Freddie Bickford Smith, Adam Foster, Tom Rainforth

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support.

Tim Reichelt, Luke Ong, Tom Rainforth

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

On the Expected Size of Conformal Prediction Sets.

Guneet S. Dhillon, George Deligiannidis, Tom Rainforth

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

In-Context Learning in Large Language Models Learns Label Relationships but Is Not Conventional Learning.

Jannik Kossen, Tom Rainforth, Yarin Gal

CoRR, 2023

Modern Bayesian Experimental Design.

Tom Rainforth, Adam Foster, Desi R. Ivanova, Freddie Bickford Smith

CoRR, 2023

Trans-Dimensional Generative Modeling via Jump Diffusion Models.

Andrew Campbell, William Harvey, Christian Weilbach, Valentin De Bortoli, Thomas Rainforth, Arnaud Doucet

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Deep Stochastic Processes via Functional Markov Transition Operators.

Jin Xu, Emilien Dupont, Kaspar Märtens, Thomas Rainforth, Yee Whye Teh

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning Instance-Specific Augmentations by Capturing Local Invariances.

Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh, Adam Foster, Hyunjik Kim

Proceedings of the International Conference on Machine Learning, 2023

CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design.

Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster

Proceedings of the International Conference on Machine Learning, 2023

Prediction-Oriented Bayesian Active Learning.

Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Do Bayesian Neural Networks Need To Be Fully Stochastic?

Mrinank Sharma, Sebastian Farquhar, Eric T. Nalisnick, Tom Rainforth

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Learning Instance-Specific Data Augmentations.

Ning Miao, Emile Mathieu, Yann Dubois, Tom Rainforth, Yee Whye Teh, Adam Foster, Hyunjik Kim

CoRR, 2022

Expectation programming: Adapting probabilistic programming systems to estimate expectations efficiently.

Tim Reichelt, Adam Golinski, Luke Ong, Tom Rainforth

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Rethinking Variational Inference for Probabilistic Programs with Stochastic Support.

Tim Reichelt, Luke Ong, Thomas Rainforth

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation.

Jannik Kossen, Sebastian Farquhar, Yarin Gal, Thomas Rainforth

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

A Continuous Time Framework for Discrete Denoising Models.

Andrew Campbell, Joe Benton, Valentin De Bortoli, Thomas Rainforth, George Deligiannidis, Arnaud Doucet

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On Incorporating Inductive Biases into VAEs.

Ning Miao, Emile Mathieu, Siddharth N, Yee Whye Teh, Tom Rainforth

Proceedings of the Tenth International Conference on Learning Representations, 2022

Learning Multimodal VAEs through Mutual Supervision.

Tom Joy, Yuge Shi, Philip H. S. Torr, Tom Rainforth, Sebastian M. Schmon, Siddharth Narayanaswamy

Proceedings of the Tenth International Conference on Learning Representations, 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. Schröder de Witt, Robert Zinkov, Philip H. S. Torr, Tom Rainforth, Yee Whye Teh, Frank Wood

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Certifiably Robust Variational Autoencoders.

Ben Barrett, Alexander Camuto, Matthew Willetts, Tom Rainforth

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

InteL-VAEs: Adding Inductive Biases to Variational Auto-Encoders via Intermediary Latents.

Ning Miao, Emile Mathieu, N. Siddharth, Yee Whye Teh, Tom Rainforth

CoRR, 2021

Active Learning under Pool Set Distribution Shift and Noisy Data.

Andreas Kirsch, Tom Rainforth, Yarin Gal

CoRR, 2021

Expectation Programming.

Tim Reichelt, Adam Golinski, Luke Ong, Tom Rainforth

CoRR, 2021

Statistically robust neural network classification.

Benjie Wang, Stefan Webb, Tom Rainforth

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Group Equivariant Subsampling.

Jin Xu, Hyunjik Kim, Thomas Rainforth, Yee Whye Teh

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods.

Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Thomas Rainforth

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Online Variational Filtering and Parameter Learning.

Andrew Campbell, Yuyang Shi, Thomas Rainforth, Arnaud Doucet

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Probabilistic Programs with Stochastic Conditioning.

David Tolpin, Yuan Zhou, Tom Rainforth, Hongseok Yang

Proceedings of the 38th International Conference on Machine Learning, 2021

On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes.

Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth

Proceedings of the 38th International Conference on Machine Learning, 2021

Active Testing: Sample-Efficient Model Evaluation.

Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth

Proceedings of the 38th International Conference on Machine Learning, 2021

Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design.

Adam Foster, Desi R. Ivanova, Ilyas Malik, Tom Rainforth

Proceedings of the 38th International Conference on Machine Learning, 2021

Improving VAEs' Robustness to Adversarial Attack.

Matthew Willetts, Alexander Camuto, Tom Rainforth, Stephen J. Roberts, Christopher C. Holmes

Proceedings of the 9th International Conference on Learning Representations, 2021

Capturing Label Characteristics in VAEs.

Tom Joy, Sebastian M. Schmon, Philip H. S. Torr, Siddharth Narayanaswamy, Tom Rainforth

Proceedings of the 9th International Conference on Learning Representations, 2021

Improving Transformation Invariance in Contrastive Representation Learning.

Adam Foster, Rattana Pukdee, Tom Rainforth

Proceedings of the 9th International Conference on Learning Representations, 2021

On Statistical Bias In Active Learning: How and When to Fix It.

Sebastian Farquhar, Yarin Gal, Tom Rainforth

Proceedings of the 9th International Conference on Learning Representations, 2021

Towards a Theoretical Understanding of the Robustness of Variational Autoencoders.

Alexander Camuto, Matthew Willetts, Stephen J. Roberts, Chris C. Holmes, Tom Rainforth

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Target-Aware Bayesian Inference: How to Beat Optimal Conventional Estimators.

Tom Rainforth, Adam Golinski, Frank Wood, Sheheryar Zaidi

J. Mach. Learn. Res., 2020

Rethinking Semi-Supervised Learning in VAEs.

Tom Joy, Sebastian M. Schmon, Philip H. S. Torr, N. Siddharth, Tom Rainforth

CoRR, 2020

Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support.

Yuan Zhou, Hongseok Yang, Yee Whye Teh, Tom Rainforth

Proceedings of the 37th International Conference on Machine Learning, 2020

A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments.

Adam Foster, Martin Jankowiak, Matthew O'Meara, Yee Whye Teh, Tom Rainforth

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

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

Proceedings of the ARES 2020: The 15th International Conference on Availability, 2020

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

Variational Estimators for Bayesian Optimal Experimental Design.

Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, Noah D. Goodman

CoRR, 2019

On the Fairness of Disentangled Representations.

Francesco Locatello, Gabriele Abbati, Thomas Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Variational Bayesian Optimal Experimental Design.

Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, Noah D. Goodman

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Disentangling Disentanglement in Variational Autoencoders.

Emile Mathieu, Tom Rainforth, N. Siddharth, Yee Whye Teh

Proceedings of the 36th International Conference on Machine Learning, 2019

Amortized Monte Carlo Integration.

Adam Golinski, Frank Wood, Tom Rainforth

Proceedings of the 36th International Conference on Machine Learning, 2019

A Statistical Approach to Assessing Neural Network Robustness.

Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar

Proceedings of the 7th International Conference on Learning Representations, 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

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Disentangling Disentanglement.

Emile Mathieu, Tom Rainforth, Siddharth Narayanaswamy, Yee Whye Teh

CoRR, 2018

On Exploration, Exploitation and Learning in Adaptive Importance Sampling.

Xiaoyu Lu, Tom Rainforth, Yuan Zhou, Jan-Willem van de Meent, Yee Whye Teh

CoRR, 2018

Nesting Probabilistic Programs.

Tom Rainforth

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 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

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Tighter Variational Bounds are Not Necessarily Better.

Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh

Proceedings of the 35th International Conference on Machine Learning, 2018

On Nesting Monte Carlo Estimators.

Tom Rainforth, Robert Cornish, Hongseok Yang, Andrew Warrington

Proceedings of the 35th International Conference on Machine Learning, 2018

Auto-Encoding Sequential Monte Carlo.

Tuan Anh Le, Maximilian Igl, Tom Rainforth, Tom Jin, Frank Wood

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Automating inference, learning, and design using probabilistic programming.

Thomas Rainforth

PhD thesis, 2017

2016

Probabilistic structure discovery in time series data.

David Janz, Brooks Paige, Tom Rainforth, Jan-Willem van de Meent, Frank D. Wood

CoRR, 2016

Bayesian Optimization for Probabilistic Programs.

Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank D. Wood

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

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

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Canonical Correlation Forests.

Tom Rainforth, Frank D. Wood

CoRR, 2015