Tom Rainforth

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 91

Top Topics

Experimental Design

Variational Inference

Top Venues

Publications

Guneet S. Dhillon, George Deligiannidis, Tom Rainforth
On the Expected Size of Conformal Prediction Sets. AISTATS (2024)
Ning Miao, Yee Whye Teh, Tom Rainforth
SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning. ICLR (2024)
Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin
Incorporating Unlabelled Data into Bayesian Neural Networks. Trans. Mach. Learn. Res. 2024 (2024)
Freddie Bickford Smith, Adam Foster, Tom Rainforth
Making Better Use of Unlabelled Data in Bayesian Active Learning. CoRR (2024)
Freddie Bickford Smith, Adam Foster, Tom Rainforth
Making Better Use of Unlabelled Data in Bayesian Active Learning. AISTATS (2024)
Jannik Kossen, Yarin Gal, Tom Rainforth
In-Context Learning Learns Label Relationships but Is Not Conventional Learning. ICLR (2024)
Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, Tommi S. Jaakkola
Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design. CoRR (2024)
Tim Reichelt, Luke Ong, Tom Rainforth
Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support. AISTATS (2024)
Tim Reichelt, Luke Ong, Tom Rainforth
Rethinking Variational Inference for Probabilistic Programs with Stochastic Support. CoRR (2023)
Jannik Kossen, Tom Rainforth, Yarin Gal
In-Context Learning in Large Language Models Learns Label Relationships but Is Not Conventional Learning. CoRR (2023)
Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. CoRR (2023)
Tim Reichelt, Luke Ong, Tom Rainforth
Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support. CoRR (2023)
Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin
Incorporating Unlabelled Data into Bayesian Neural Networks. CoRR (2023)
Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh, Adam Foster, Hyunjik Kim
Learning Instance-Specific Augmentations by Capturing Local Invariances. ICML (2023)
Tom Rainforth, Adam Foster, Desi R. Ivanova, Freddie Bickford Smith
Modern Bayesian Experimental Design. CoRR (2023)
Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth
Prediction-Oriented Bayesian Active Learning. AISTATS (2023)
Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. ICML (2023)
Andrew Campbell, William Harvey, Christian Weilbach, Valentin De Bortoli, Tom Rainforth, Arnaud Doucet
Trans-Dimensional Generative Modeling via Jump Diffusion Models. CoRR (2023)
Guneet S. Dhillon, George Deligiannidis, Tom Rainforth
On the Expected Size of Conformal Prediction Sets. CoRR (2023)
Ning Miao, Yee Whye Teh, Tom Rainforth
SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning. CoRR (2023)
Mrinank Sharma, Sebastian Farquhar, Eric T. Nalisnick, Tom Rainforth
Do Bayesian Neural Networks Need To Be Fully Stochastic? AISTATS (2023)
Jin Xu, Emilien Dupont, Kaspar Märtens, Tom Rainforth, Yee Whye Teh
Deep Stochastic Processes via Functional Markov Transition Operators. CoRR (2023)
Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth
Prediction-Oriented Bayesian Active Learning. CoRR (2023)
Andrew Campbell, Joe Benton, Valentin De Bortoli, Tom Rainforth, George Deligiannidis, Arnaud Doucet
A Continuous Time Framework for Discrete Denoising Models. CoRR (2022)
Ning Miao, Emile Mathieu, Siddharth N, Yee Whye Teh, Tom Rainforth
On Incorporating Inductive Biases into VAEs. ICLR (2022)
Ning Miao, Emile Mathieu, Yann Dubois, Tom Rainforth, Yee Whye Teh, Adam Foster, Hyunjik Kim
Learning Instance-Specific Data Augmentations. CoRR (2022)
Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation. CoRR (2022)
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
Amortized Rejection Sampling in Universal Probabilistic Programming. AISTATS (2022)
Ben Barrett, Alexander Camuto, Matthew Willetts, Tom Rainforth
Certifiably Robust Variational Autoencoders. AISTATS (2022)
Tim Reichelt, Adam Golinski, Luke Ong, Tom Rainforth
Expectation programming: Adapting probabilistic programming systems to estimate expectations efficiently. UAI (2022)
Mrinank Sharma, Sebastian Farquhar, Eric Nalisnick, Tom Rainforth
Do Bayesian Neural Networks Need To Be Fully Stochastic? CoRR (2022)
Tom Joy, Yuge Shi, Philip H. S. Torr, Tom Rainforth, Sebastian M. Schmon, Siddharth Narayanaswamy
Learning Multimodal VAEs through Mutual Supervision. ICLR (2022)
Jannik Kossen, Neil Band, Clare Lyle, Aidan N. Gomez, Tom Rainforth, Yarin Gal
Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning. CoRR (2021)
Matthew Willetts, Alexander Camuto, Tom Rainforth, Stephen J. Roberts, Christopher C. Holmes
Improving VAEs' Robustness to Adversarial Attack. ICLR (2021)
Sebastian Farquhar, Yarin Gal, Tom Rainforth
On Statistical Bias In Active Learning: How and When To Fix It. CoRR (2021)
Benjie Wang, Stefan Webb, Tom Rainforth
Statistically robust neural network classification. UAI (2021)
Adam Foster, Rattana Pukdee, Tom Rainforth
Improving Transformation Invariance in Contrastive Representation Learning. ICLR (2021)
Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes. ICML (2021)
Sebastian Farquhar, Yarin Gal, Tom Rainforth
On Statistical Bias In Active Learning: How and When to Fix It. ICLR (2021)
Tom Joy, Sebastian M. Schmon, Philip H. S. Torr, Siddharth Narayanaswamy, Tom Rainforth
Capturing Label Characteristics in VAEs. ICLR (2021)
Tim Reichelt, Adam Golinski, Luke Ong, Tom Rainforth
Expectation Programming. CoRR (2021)
Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth
Active Testing: Sample-Efficient Model Evaluation. ICML (2021)
Adam Foster, Desi R. Ivanova, Ilyas Malik, Tom Rainforth
Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design. ICML (2021)
Adam Foster, Desi R. Ivanova, Ilyas Malik, Tom Rainforth
Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design. CoRR (2021)
Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth
Active Testing: Sample-Efficient Model Evaluation. CoRR (2021)
Andrew Campbell, Yuyang Shi, Tom Rainforth, Arnaud Doucet
Online Variational Filtering and Parameter Learning. CoRR (2021)
Ben Barrett, Alexander Camuto, Matthew Willetts, Tom Rainforth
Certifiably Robust Variational Autoencoders. CoRR (2021)
David Tolpin, Yuan Zhou, Tom Rainforth, Hongseok Yang
Probabilistic Programs with Stochastic Conditioning. ICML (2021)
Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Tom Rainforth
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods. CoRR (2021)
Ning Miao, Emile Mathieu, N. Siddharth, Yee Whye Teh, Tom Rainforth
InteL-VAEs: Adding Inductive Biases to Variational Auto-Encoders via Intermediary Latents. CoRR (2021)
Andreas Kirsch, Tom Rainforth, Yarin Gal
Active Learning under Pool Set Distribution Shift and Noisy Data. CoRR (2021)
Jin Xu, Hyunjik Kim, Tom Rainforth, Yee Whye Teh
Group Equivariant Subsampling. CoRR (2021)
Tom Joy, Yuge Shi, Philip H. S. Torr, Tom Rainforth, Sebastian M. Schmon, N. Siddharth
Learning Multimodal VAEs through Mutual Supervision. CoRR (2021)
Alexander Camuto, Matthew Willetts, Stephen J. Roberts, Chris C. Holmes, Tom Rainforth
Towards a Theoretical Understanding of the Robustness of Variational Autoencoders. AISTATS (2021)
Alexander Camuto, Matthew Willetts, Stephen J. Roberts, Chris C. Holmes, Tom Rainforth
Towards a Theoretical Understanding of the Robustness of Variational Autoencoders. CoRR (2020)
Adam Foster, Rattana Pukdee, Tom Rainforth
Improving Transformation Invariance in Contrastive Representation Learning. CoRR (2020)
Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes. CoRR (2020)
Adam Foster, Martin Jankowiak, Matthew O'Meara, Yee Whye Teh, Tom Rainforth
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. AISTATS (2020)
Jack K. Fitzsimons, Atul Mantri, Robert Pisarczyk, Tom Rainforth, Zhikuan Zhao
A note on blind contact tracing at scale with applications to the COVID-19 pandemic. CoRR (2020)
Tom Rainforth, Adam Golinski, Frank Wood, Sheheryar Zaidi
Target-Aware Bayesian Inference: How to Beat Optimal Conventional Estimators. J. Mach. Learn. Res. 21 (2020)
Tom Joy, Sebastian M. Schmon, Philip H. S. Torr, N. Siddharth, Tom Rainforth
Rethinking Semi-Supervised Learning in VAEs. CoRR (2020)
Yuan Zhou, Hongseok Yang, Yee Whye Teh, Tom Rainforth
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support. ICML (2020)
Jack K. Fitzsimons, Atul Mantri, Robert Pisarczyk, Tom Rainforth, Zhikuan Zhao
A note on blind contact tracing at scale with applications to the COVID-19 pandemic. ARES (2020)
Benjie Wang, Stefan Webb, Tom Rainforth
Statistically Robust Neural Network Classification. CoRR (2019)
Adam Foster, Martin Jankowiak, Matthew O'Meara, Yee Whye Teh, Tom Rainforth
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. CoRR (2019)
Yuan Zhou, Hongseok Yang, Yee Whye Teh, Tom Rainforth
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support. CoRR (2019)
Saeid Naderiparizi, Adam Scibior, Andreas Munk, Mehrdad Ghadiri, Atilim Günes Baydin, Bradley Gram-Hansen, Christian Schröder de Witt, Robert Zinkov, Philip H. S. Torr, Tom Rainforth, Yee Whye Teh, Frank Wood
Amortized Rejection Sampling in Universal Probabilistic Programming. CoRR (2019)
Bradley Gram-Hansen, Christian Schröder de Witt, Tom Rainforth, Philip H. S. Torr, Yee Whye Teh, Atilim Günes Baydin
Hijacking Malaria Simulators with Probabilistic Programming. CoRR (2019)
Emile Mathieu, Tom Rainforth, N. Siddharth, Yee Whye Teh
Disentangling Disentanglement in Variational Autoencoders. ICML (2019)
Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar
A Statistical Approach to Assessing Neural Network Robustness. ICLR (Poster) (2019)
Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, Noah D. Goodman
Variational Estimators for Bayesian Optimal Experimental Design. CoRR (2019)
Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, Noah D. Goodman
Variational Bayesian Optimal Experimental Design. NeurIPS (2019)
Yuan Zhou, Bradley J. Gram-Hansen, Tobias Kohn, Tom Rainforth, Hongseok Yang, Frank Wood
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models. CoRR (2019)
Yuan Zhou, Bradley J. Gram-Hansen, Tobias Kohn, Tom Rainforth, Hongseok Yang, Frank Wood
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models. AISTATS (2019)
Adam Golinski, Frank Wood, Tom Rainforth
Amortized Monte Carlo Integration. ICML (2019)
Francesco Locatello, Gabriele Abbati, Tom Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem
On the Fairness of Disentangled Representations. CoRR (2019)
Adam Golinski, Frank Wood, Tom Rainforth
Amortized Monte Carlo Integration. CoRR (2019)
Emile Mathieu, Tom Rainforth, Siddharth Narayanaswamy, Yee Whye Teh
Disentangling Disentanglement. CoRR (2018)
Stefan Webb, Adam Golinski, Robert Zinkov, Siddharth Narayanaswamy, Tom Rainforth, Yee Whye Teh, Frank Wood
Faithful Inversion of Generative Models for Effective Amortized Inference. NeurIPS (2018)
Tuan Anh Le, Maximilian Igl, Tom Rainforth, Tom Jin, Frank Wood
Auto-Encoding Sequential Monte Carlo. ICLR (Poster) (2018)
Tom Rainforth, Robert Cornish, Hongseok Yang, Andrew Warrington
On Nesting Monte Carlo Estimators. ICML (2018)
Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh
Tighter Variational Bounds are Not Necessarily Better. CoRR (2018)
Xiaoyu Lu, Tom Rainforth, Yuan Zhou, Jan-Willem van de Meent, Yee Whye Teh
On Exploration, Exploitation and Learning in Adaptive Importance Sampling. CoRR (2018)
Tom Rainforth
Nesting Probabilistic Programs. UAI (2018)
Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh
Tighter Variational Bounds are Not Necessarily Better. ICML (2018)
Tom Rainforth
Nesting Probabilistic Programs. CoRR (2018)
Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar
A Statistical Approach to Assessing Neural Network Robustness. CoRR (2018)
Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank D. Wood
Bayesian Optimization for Probabilistic Programs. CoRR (2017)
David Janz, Brooks Paige, Tom Rainforth, Jan-Willem van de Meent, Frank D. Wood
Probabilistic structure discovery in time series data. CoRR (2016)
Tom Rainforth, Christian A. Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem van de Meent, Arnaud Doucet, Frank D. Wood
Interacting Particle Markov Chain Monte Carlo. ICML (2016)
Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank D. Wood
Bayesian Optimization for Probabilistic Programs. NIPS (2016)
Tom Rainforth, Frank D. Wood
Canonical Correlation Forests. CoRR (2015)