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Tom Rainforth
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- affiliation: University of Oxford, UK
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2020 – today
- 2024
- [j2]Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin:
Incorporating Unlabelled Data into Bayesian Neural Networks. Trans. Mach. Learn. Res. 2024 (2024) - [c50]Tim Reichelt, Luke Ong, Tom Rainforth:
Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support. AISTATS 2024: 829-837 - [c49]Freddie Bickford Smith, Adam Foster, Tom Rainforth:
Making Better Use of Unlabelled Data in Bayesian Active Learning. AISTATS 2024: 847-855 - [c48]Guneet S. Dhillon, George Deligiannidis, Tom Rainforth:
On the Expected Size of Conformal Prediction Sets. AISTATS 2024: 1549-1557 - [c47]Jannik Kossen, Yarin Gal, Tom Rainforth:
In-Context Learning Learns Label Relationships but Is Not Conventional Learning. ICLR 2024 - [c46]Ning Miao, Yee Whye Teh, Tom Rainforth:
SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning. ICLR 2024 - [c45]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. ICML 2024 - [i51]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 abs/2402.04997 (2024) - [i50]Freddie Bickford Smith, Adam Foster, Tom Rainforth:
Making Better Use of Unlabelled Data in Bayesian Active Learning. CoRR abs/2404.17249 (2024) - 2023
- [c44]Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth:
Prediction-Oriented Bayesian Active Learning. AISTATS 2023: 7331-7348 - [c43]Mrinank Sharma, Sebastian Farquhar, Eric T. Nalisnick, Tom Rainforth:
Do Bayesian Neural Networks Need To Be Fully Stochastic? AISTATS 2023: 7694-7722 - [c42]Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster:
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. ICML 2023: 14445-14464 - [c41]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: 24720-24736 - [c40]Jin Xu, Emilien Dupont, Kaspar Märtens, Thomas Rainforth, Yee Whye Teh:
Deep Stochastic Processes via Functional Markov Transition Operators. NeurIPS 2023 - [c39]Andrew Campbell, William Harvey, Christian Weilbach, Valentin De Bortoli, Thomas Rainforth, Arnaud Doucet:
Trans-Dimensional Generative Modeling via Jump Diffusion Models. NeurIPS 2023 - [i49]Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster:
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. CoRR abs/2302.14015 (2023) - [i48]Tom Rainforth, Adam Foster, Desi R. Ivanova, Freddie Bickford Smith:
Modern Bayesian Experimental Design. CoRR abs/2302.14545 (2023) - [i47]Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin:
Incorporating Unlabelled Data into Bayesian Neural Networks. CoRR abs/2304.01762 (2023) - [i46]Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth:
Prediction-Oriented Bayesian Active Learning. CoRR abs/2304.08151 (2023) - [i45]Jin Xu, Emilien Dupont, Kaspar Märtens, Tom Rainforth, Yee Whye Teh:
Deep Stochastic Processes via Functional Markov Transition Operators. CoRR abs/2305.15574 (2023) - [i44]Andrew Campbell, William Harvey, Christian Weilbach, Valentin De Bortoli, Tom Rainforth, Arnaud Doucet:
Trans-Dimensional Generative Modeling via Jump Diffusion Models. CoRR abs/2305.16261 (2023) - [i43]Guneet S. Dhillon, George Deligiannidis, Tom Rainforth:
On the Expected Size of Conformal Prediction Sets. CoRR abs/2306.07254 (2023) - [i42]Jannik Kossen, Tom Rainforth, Yarin Gal:
In-Context Learning in Large Language Models Learns Label Relationships but Is Not Conventional Learning. CoRR abs/2307.12375 (2023) - [i41]Ning Miao, Yee Whye Teh, Tom Rainforth:
SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning. CoRR abs/2308.00436 (2023) - [i40]Tim Reichelt, Luke Ong, Tom Rainforth:
Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support. CoRR abs/2310.14888 (2023) - [i39]Tim Reichelt, Luke Ong, Tom Rainforth:
Rethinking Variational Inference for Probabilistic Programs with Stochastic Support. CoRR abs/2311.00594 (2023) - 2022
- [c38]Ben Barrett, Alexander Camuto, Matthew Willetts, Tom Rainforth:
Certifiably Robust Variational Autoencoders. AISTATS 2022: 3663-3683 - [c37]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: 8392-8412 - [c36]Tom Joy, Yuge Shi, Philip H. S. Torr, Tom Rainforth, Sebastian M. Schmon, Siddharth Narayanaswamy:
Learning Multimodal VAEs through Mutual Supervision. ICLR 2022 - [c35]Ning Miao, Emile Mathieu, Siddharth N, Yee Whye Teh, Tom Rainforth:
On Incorporating Inductive Biases into VAEs. ICLR 2022 - [c34]Andrew Campbell, Joe Benton, Valentin De Bortoli, Thomas Rainforth, George Deligiannidis, Arnaud Doucet:
A Continuous Time Framework for Discrete Denoising Models. NeurIPS 2022 - [c33]Jannik Kossen, Sebastian Farquhar, Yarin Gal, Thomas Rainforth:
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation. NeurIPS 2022 - [c32]Tim Reichelt, Luke Ong, Thomas Rainforth:
Rethinking Variational Inference for Probabilistic Programs with Stochastic Support. NeurIPS 2022 - [c31]Tim Reichelt, Adam Golinski, Luke Ong, Tom Rainforth:
Expectation programming: Adapting probabilistic programming systems to estimate expectations efficiently. UAI 2022: 1676-1685 - [i38]Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth:
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation. CoRR abs/2202.06881 (2022) - [i37]Andrew Campbell, Joe Benton, Valentin De Bortoli, Tom Rainforth, George Deligiannidis, Arnaud Doucet:
A Continuous Time Framework for Discrete Denoising Models. CoRR abs/2205.14987 (2022) - [i36]Ning Miao, Emile Mathieu, Yann Dubois, Tom Rainforth, Yee Whye Teh, Adam Foster, Hyunjik Kim:
Learning Instance-Specific Data Augmentations. CoRR abs/2206.00051 (2022) - [i35]Mrinank Sharma, Sebastian Farquhar, Eric T. Nalisnick, Tom Rainforth:
Do Bayesian Neural Networks Need To Be Fully Stochastic? CoRR abs/2211.06291 (2022) - 2021
- [c30]Alexander Camuto, Matthew Willetts, Stephen J. Roberts, Chris C. Holmes, Tom Rainforth:
Towards a Theoretical Understanding of the Robustness of Variational Autoencoders. AISTATS 2021: 3565-3573 - [c29]Sebastian Farquhar, Yarin Gal, Tom Rainforth:
On Statistical Bias In Active Learning: How and When to Fix It. ICLR 2021 - [c28]Adam Foster, Rattana Pukdee, Tom Rainforth:
Improving Transformation Invariance in Contrastive Representation Learning. ICLR 2021 - [c27]Tom Joy, Sebastian M. Schmon, Philip H. S. Torr, Siddharth Narayanaswamy, Tom Rainforth:
Capturing Label Characteristics in VAEs. ICLR 2021 - [c26]Matthew Willetts, Alexander Camuto, Tom Rainforth, Stephen J. Roberts, Christopher C. Holmes:
Improving VAEs' Robustness to Adversarial Attack. ICLR 2021 - [c25]Adam Foster, Desi R. Ivanova, Ilyas Malik, Tom Rainforth:
Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design. ICML 2021: 3384-3395 - [c24]Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth:
Active Testing: Sample-Efficient Model Evaluation. ICML 2021: 5753-5763 - [c23]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: 9148-9156 - [c22]David Tolpin, Yuan Zhou, Tom Rainforth, Hongseok Yang:
Probabilistic Programs with Stochastic Conditioning. ICML 2021: 10312-10323 - [c21]Jin Xu, Hyunjik Kim, Thomas Rainforth, Yee Whye Teh:
Group Equivariant Subsampling. NeurIPS 2021: 5934-5946 - [c20]Andrew Campbell, Yuyang Shi, Thomas Rainforth, Arnaud Doucet:
Online Variational Filtering and Parameter Learning. NeurIPS 2021: 18633-18645 - [c19]Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Thomas Rainforth:
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods. NeurIPS 2021: 25785-25798 - [c18]Jannik Kossen, Neil Band, Clare Lyle, Aidan N. Gomez, Thomas Rainforth, Yarin Gal:
Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning. NeurIPS 2021: 28742-28756 - [c17]Benjie Wang, Stefan Webb, Tom Rainforth:
Statistically robust neural network classification. UAI 2021: 1735-1745 - [i34]Sebastian Farquhar, Yarin Gal, Tom Rainforth:
On Statistical Bias In Active Learning: How and When To Fix It. CoRR abs/2101.11665 (2021) - [i33]Ben Barrett, Alexander Camuto, Matthew Willetts, Tom Rainforth:
Certifiably Robust Variational Autoencoders. CoRR abs/2102.07559 (2021) - [i32]Adam Foster, Desi R. Ivanova, Ilyas Malik, Tom Rainforth:
Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design. CoRR abs/2103.02438 (2021) - [i31]Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth:
Active Testing: Sample-Efficient Model Evaluation. CoRR abs/2103.05331 (2021) - [i30]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 abs/2106.02584 (2021) - [i29]Tim Reichelt, Adam Golinski, Luke Ong, Tom Rainforth:
Expectation Programming. CoRR abs/2106.04953 (2021) - [i28]Jin Xu, Hyunjik Kim, Tom Rainforth, Yee Whye Teh:
Group Equivariant Subsampling. CoRR abs/2106.05886 (2021) - [i27]Andreas Kirsch, Tom Rainforth, Yarin Gal:
Active Learning under Pool Set Distribution Shift and Noisy Data. CoRR abs/2106.11719 (2021) - [i26]Tom Joy, Yuge Shi, Philip H. S. Torr, Tom Rainforth, Sebastian M. Schmon, N. Siddharth:
Learning Multimodal VAEs through Mutual Supervision. CoRR abs/2106.12570 (2021) - [i25]Ning Miao, Emile Mathieu, N. Siddharth, Yee Whye Teh, Tom Rainforth:
InteL-VAEs: Adding Inductive Biases to Variational Auto-Encoders via Intermediary Latents. CoRR abs/2106.13746 (2021) - [i24]Andrew Campbell, Yuyang Shi, Tom Rainforth, Arnaud Doucet:
Online Variational Filtering and Parameter Learning. CoRR abs/2110.13549 (2021) - [i23]Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Tom Rainforth:
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods. CoRR abs/2111.02329 (2021) - 2020
- [j1]Tom Rainforth, Adam Golinski, Frank Wood, Sheheryar Zaidi:
Target-Aware Bayesian Inference: How to Beat Optimal Conventional Estimators. J. Mach. Learn. Res. 21: 88:1-88:54 (2020) - [c16]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: 92:1-92:6 - [c15]Adam Foster, Martin Jankowiak, Matthew O'Meara, Yee Whye Teh, Tom Rainforth:
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. AISTATS 2020: 2959-2969 - [c14]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: 11534-11545 - [i22]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 abs/2004.05116 (2020) - [i21]Tom Joy, Sebastian M. Schmon, Philip H. S. Torr, N. Siddharth, Tom Rainforth:
Rethinking Semi-Supervised Learning in VAEs. CoRR abs/2006.10102 (2020) - [i20]Alexander Camuto, Matthew Willetts, Stephen J. Roberts, Chris C. Holmes, Tom Rainforth:
Towards a Theoretical Understanding of the Robustness of Variational Autoencoders. CoRR abs/2007.07365 (2020) - [i19]Adam Foster, Rattana Pukdee, Tom Rainforth:
Improving Transformation Invariance in Contrastive Representation Learning. CoRR abs/2010.09515 (2020) - [i18]Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth:
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes. CoRR abs/2011.00515 (2020)
2010 – 2019
- 2019
- [c13]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: 148-157 - [c12]Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar:
A Statistical Approach to Assessing Neural Network Robustness. ICLR (Poster) 2019 - [c11]Adam Golinski, Frank Wood, Tom Rainforth:
Amortized Monte Carlo Integration. ICML 2019: 2309-2318 - [c10]Emile Mathieu, Tom Rainforth, N. Siddharth, Yee Whye Teh:
Disentangling Disentanglement in Variational Autoencoders. ICML 2019: 4402-4412 - [c9]Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, Noah D. Goodman:
Variational Bayesian Optimal Experimental Design. NeurIPS 2019: 14036-14047 - [c8]Francesco Locatello, Gabriele Abbati, Thomas Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem:
On the Fairness of Disentangled Representations. NeurIPS 2019: 14584-14597 - [i17]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 abs/1903.02482 (2019) - [i16]Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, Noah D. Goodman:
Variational Estimators for Bayesian Optimal Experimental Design. CoRR abs/1903.05480 (2019) - [i15]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 abs/1905.12432 (2019) - [i14]Francesco Locatello, Gabriele Abbati, Tom Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem:
On the Fairness of Disentangled Representations. CoRR abs/1905.13662 (2019) - [i13]Adam Golinski, Frank Wood, Tom Rainforth:
Amortized Monte Carlo Integration. CoRR abs/1907.08082 (2019) - [i12]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 abs/1910.09056 (2019) - [i11]Yuan Zhou, Hongseok Yang, Yee Whye Teh, Tom Rainforth:
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support. CoRR abs/1910.13324 (2019) - [i10]Adam Foster, Martin Jankowiak, Matthew O'Meara, Yee Whye Teh, Tom Rainforth:
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. CoRR abs/1911.00294 (2019) - [i9]Benjie Wang, Stefan Webb, Tom Rainforth:
Statistically Robust Neural Network Classification. CoRR abs/1912.04884 (2019) - 2018
- [c7]Tuan Anh Le, Maximilian Igl, Tom Rainforth, Tom Jin, Frank Wood:
Auto-Encoding Sequential Monte Carlo. ICLR (Poster) 2018 - [c6]Tom Rainforth, Robert Cornish, Hongseok Yang, Andrew Warrington:
On Nesting Monte Carlo Estimators. ICML 2018: 4264-4273 - [c5]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: 4274-4282 - [c4]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: 3074-3084 - [c3]Tom Rainforth:
Nesting Probabilistic Programs. UAI 2018: 249-258 - [i8]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 abs/1802.04537 (2018) - [i7]Tom Rainforth:
Nesting Probabilistic Programs. CoRR abs/1803.06328 (2018) - [i6]Xiaoyu Lu, Tom Rainforth, Yuan Zhou, Jan-Willem van de Meent, Yee Whye Teh:
On Exploration, Exploitation and Learning in Adaptive Importance Sampling. CoRR abs/1810.13296 (2018) - [i5]Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar:
A Statistical Approach to Assessing Neural Network Robustness. CoRR abs/1811.07209 (2018) - [i4]Emile Mathieu, Tom Rainforth, Siddharth Narayanaswamy, Yee Whye Teh:
Disentangling Disentanglement. CoRR abs/1812.02833 (2018) - 2017
- [b1]Thomas Rainforth:
Automating inference, learning, and design using probabilistic programming. University of Oxford, UK, 2017 - [i3]Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank D. Wood:
Bayesian Optimization for Probabilistic Programs. CoRR abs/1707.04314 (2017) - 2016
- [c2]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: 2616-2625 - [c1]Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank D. Wood:
Bayesian Optimization for Probabilistic Programs. NIPS 2016: 280-288 - [i2]David Janz, Brooks Paige, Tom Rainforth, Jan-Willem van de Meent, Frank D. Wood:
Probabilistic structure discovery in time series data. CoRR abs/1611.06863 (2016) - 2015
- [i1]Tom Rainforth, Frank D. Wood:
Canonical Correlation Forests. CoRR abs/1507.05444 (2015)
Coauthor Index
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