Tom Rainforth - RankMe

106 papers A* 27A 13B 1Journal 62Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	A Geometric Approach to Optimal Experimental Design. CoRR Gavin Kerrigan, Christian A. Naesseth, Tom Rainforth
2025	J	`jnl`	Active Learning with Task-Driven Representations for Messy Pools. CoRR Kianoosh Ashouritaklimi, Tom Rainforth
2025	J	`jnl`	BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Design. CoRR Deepro Choudhury, Sinead Williamson, Adam Golinski, Ning Miao, Freddie Bickford Smith, Michael Kirchhof, Yizhe Zhang, Tom Rainforth
2025	A*	`conf`	Do Bayesian Neural Networks Actually Behave Like Bayesian Models? ICML Gábor Pituk, Vik Shirvaikar, Tom Rainforth
2025	J	`jnl`	Kosmos: An AI Scientist for Autonomous Discovery. CoRR Ludovico Mitchener, Angela Yiu, Benjamin Chang, Mathieu Bourdenx, Tyler Nadolski, Arvis Sulovari, Eric C. Landsness, Daniel L. Barabasi, Siddharth Narayanan, Nicky Evans, Shriya Reddy, Martha Foiani, Aizad Kamal, Leah P. Shriver, Fang Cao, Asmamaw T. Wassie, Jon M. Laurent, Edwin Melville-Green, Mayk Caldas Ramos, Albert Bou, Kaleigh F. Roberts, Sladjana Zagorac, Timothy C. Orr, Miranda E. Orr, Kevin J. Zwezdaryk, Ali Essa Ghareeb, Laurie McCoy, Bruna Gomes, Euan A. Ashley, Karen E. Duff, Tonio Buonassisi, Tom Rainforth, Randall J. Bateman, Michael D. Skarlinski, Samuel G. Rodriques, Michaela M. Hinks, Andrew D. White
2025	J	`jnl`	Prediction-Oriented Subsampling from Data Streams. CoRR Benedetta Lavinia Mussati, Freddie Bickford Smith, Tom Rainforth, Stephen Roberts
2025	A*	`conf`	Rethinking Aleatoric and Epistemic Uncertainty. ICML Freddie Bickford Smith, Jannik Kossen, Eleanor Trollope, Mark van der Wilk, Adam Foster, Tom Rainforth
2025	J	`jnl`	Scaling Up Active Testing to Large Language Models. CoRR Gabrielle Berrada, Jannik Kossen, Muhammed Razzak, Freddie Bickford Smith, Yarin Gal, Tom Rainforth
2025	A*	`conf`	Shh, don't say that! Domain Certification in LLMs. ICLR Cornelius Emde, Alasdair Paren, Preetham Arvind, Maxime Guillaume Kayser, Tom Rainforth, Thomas Lukasiewicz, Philip Torr, Adel Bibi
2025	J	`jnl`	Shh, don't say that! Domain Certification in LLMs. CoRR Cornelius Emde, Alasdair Paren, Preetham Arvind, Maxime Kayser, Tom Rainforth, Thomas Lukasiewicz, Bernard Ghanem, Philip H. S. Torr, Adel Bibi
2025	A*	`conf`	Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design. ICML Marcel Hedman, Desi R. Ivanova, Cong Guan, Tom Rainforth
2025	J	`jnl`	Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design. CoRR Marcel Hedman, Desi R. Ivanova, Cong Guan, Tom Rainforth
2024	A	`conf`	Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support. AISTATS Tim Reichelt, Luke Ong, Tom Rainforth
2024	A*	`conf`	Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design. ICML Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, Tommi S. Jaakkola
2024	J	`jnl`	Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design. CoRR Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, Tommi S. Jaakkola
2024	A*	`conf`	In-Context Learning Learns Label Relationships but Is Not Conventional Learning. ICLR Jannik Kossen, Yarin Gal, Tom Rainforth
2024	J	`jnl`	Incorporating Unlabelled Data into Bayesian Neural Networks. Trans. Mach. Learn. Res. Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin
2024	A	`conf`	Making Better Use of Unlabelled Data in Bayesian Active Learning. AISTATS Freddie Bickford Smith, Adam Foster, Tom Rainforth
2024	J	`jnl`	Making Better Use of Unlabelled Data in Bayesian Active Learning. CoRR Freddie Bickford Smith, Adam Foster, Tom Rainforth
2024	A	`conf`	On the Expected Size of Conformal Prediction Sets. AISTATS Guneet S. Dhillon, George Deligiannidis, Tom Rainforth
2024	J	`jnl`	Rethinking Aleatoric and Epistemic Uncertainty. CoRR Freddie Bickford Smith, Jannik Kossen, Eleanor Trollope, Mark van der Wilk, Adam Foster, Tom Rainforth
2024	A*	`conf`	SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning. ICLR Ning Miao, Yee Whye Teh, Tom Rainforth
2023	J	`jnl`	Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support. CoRR Tim Reichelt, Luke Ong, Tom Rainforth
2023	A*	`conf`	CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. ICML Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster
2023	J	`jnl`	CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. CoRR Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster
2023	J	`jnl`	Deep Stochastic Processes via Functional Markov Transition Operators. CoRR Jin Xu, Emilien Dupont, Kaspar Märtens, Tom Rainforth, Yee Whye Teh
2023	A	`conf`	Do Bayesian Neural Networks Need To Be Fully Stochastic? AISTATS Mrinank Sharma, Sebastian Farquhar, Eric T. Nalisnick, Tom Rainforth
2023	J	`jnl`	In-Context Learning in Large Language Models Learns Label Relationships but Is Not Conventional Learning. CoRR Jannik Kossen, Tom Rainforth, Yarin Gal
2023	J	`jnl`	Incorporating Unlabelled Data into Bayesian Neural Networks. CoRR Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin
2023	A*	`conf`	Learning Instance-Specific Augmentations by Capturing Local Invariances. ICML Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh, Adam Foster, Hyunjik Kim
2023	J	`jnl`	Modern Bayesian Experimental Design. CoRR Tom Rainforth, Adam Foster, Desi R. Ivanova, Freddie Bickford Smith
2023	J	`jnl`	On the Expected Size of Conformal Prediction Sets. CoRR Guneet S. Dhillon, George Deligiannidis, Tom Rainforth
2023	A	`conf`	Prediction-Oriented Bayesian Active Learning. AISTATS Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth
2023	J	`jnl`	Prediction-Oriented Bayesian Active Learning. CoRR Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth
2023	J	`jnl`	Rethinking Variational Inference for Probabilistic Programs with Stochastic Support. CoRR Tim Reichelt, Luke Ong, Tom Rainforth
2023	J	`jnl`	SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning. CoRR Ning Miao, Yee Whye Teh, Tom Rainforth
2023	J	`jnl`	Trans-Dimensional Generative Modeling via Jump Diffusion Models. CoRR Andrew Campbell, William Harvey, Christian Weilbach, Valentin De Bortoli, Tom Rainforth, Arnaud Doucet
2022	J	`jnl`	A Continuous Time Framework for Discrete Denoising Models. CoRR Andrew Campbell, Joe Benton, Valentin De Bortoli, Tom Rainforth, George Deligiannidis, Arnaud Doucet
2022	J	`jnl`	Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation. CoRR Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth
2022	A	`conf`	Amortized Rejection Sampling in Universal Probabilistic Programming. AISTATS 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
2022	A	`conf`	Certifiably Robust Variational Autoencoders. AISTATS Ben Barrett, Alexander Camuto, Matthew Willetts, Tom Rainforth
2022	J	`jnl`	Do Bayesian Neural Networks Need To Be Fully Stochastic? CoRR Mrinank Sharma, Sebastian Farquhar, Eric T. Nalisnick, Tom Rainforth
2022	A	`conf`	Expectation programming: Adapting probabilistic programming systems to estimate expectations efficiently. UAI Tim Reichelt, Adam Golinski, Luke Ong, Tom Rainforth
2022	J	`jnl`	Learning Instance-Specific Data Augmentations. CoRR Ning Miao, Emile Mathieu, Yann Dubois, Tom Rainforth, Yee Whye Teh, Adam Foster, Hyunjik Kim
2022	A*	`conf`	Learning Multimodal VAEs through Mutual Supervision. ICLR Tom Joy, Yuge Shi, Philip H. S. Torr, Tom Rainforth, Sebastian M. Schmon, Siddharth Narayanaswamy
2022	A*	`conf`	On Incorporating Inductive Biases into VAEs. ICLR Ning Miao, Emile Mathieu, Siddharth N, Yee Whye Teh, Tom Rainforth
2021	J	`jnl`	Active Learning under Pool Set Distribution Shift and Noisy Data. CoRR Andreas Kirsch, Tom Rainforth, Yarin Gal
2021	A*	`conf`	Active Testing: Sample-Efficient Model Evaluation. ICML Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth
2021	J	`jnl`	Active Testing: Sample-Efficient Model Evaluation. CoRR Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth
2021	A*	`conf`	Capturing Label Characteristics in VAEs. ICLR Tom Joy, Sebastian M. Schmon, Philip H. S. Torr, Siddharth Narayanaswamy, Tom Rainforth
2021	J	`jnl`	Certifiably Robust Variational Autoencoders. CoRR Ben Barrett, Alexander Camuto, Matthew Willetts, Tom Rainforth
2021	A*	`conf`	Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design. ICML Adam Foster, Desi R. Ivanova, Ilyas Malik, Tom Rainforth
2021	J	`jnl`	Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design. CoRR Adam Foster, Desi R. Ivanova, Ilyas Malik, Tom Rainforth
2021	J	`jnl`	Expectation Programming. CoRR Tim Reichelt, Adam Golinski, Luke Ong, Tom Rainforth
2021	J	`jnl`	Group Equivariant Subsampling. CoRR Jin Xu, Hyunjik Kim, Tom Rainforth, Yee Whye Teh
2021	J	`jnl`	Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods. CoRR Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Tom Rainforth
2021	A*	`conf`	Improving Transformation Invariance in Contrastive Representation Learning. ICLR Adam Foster, Rattana Pukdee, Tom Rainforth
2021	A*	`conf`	Improving VAEs' Robustness to Adversarial Attack. ICLR Matthew Willetts, Alexander Camuto, Tom Rainforth, Stephen J. Roberts, Christopher C. Holmes
2021	J	`jnl`	InteL-VAEs: Adding Inductive Biases to Variational Auto-Encoders via Intermediary Latents. CoRR Ning Miao, Emile Mathieu, N. Siddharth, Yee Whye Teh, Tom Rainforth
2021	J	`jnl`	Learning Multimodal VAEs through Mutual Supervision. CoRR Tom Joy, Yuge Shi, Philip H. S. Torr, Tom Rainforth, Sebastian M. Schmon, N. Siddharth
2021	A*	`conf`	On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes. ICML Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth
2021	J	`jnl`	On Statistical Bias In Active Learning: How and When To Fix It. CoRR Sebastian Farquhar, Yarin Gal, Tom Rainforth
2021	A*	`conf`	On Statistical Bias In Active Learning: How and When to Fix It. ICLR Sebastian Farquhar, Yarin Gal, Tom Rainforth
2021	J	`jnl`	Online Variational Filtering and Parameter Learning. CoRR Andrew Campbell, Yuyang Shi, Tom Rainforth, Arnaud Doucet
2021	A*	`conf`	Probabilistic Programs with Stochastic Conditioning. ICML David Tolpin, Yuan Zhou, Tom Rainforth, Hongseok Yang
2021	J	`jnl`	Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning. CoRR Jannik Kossen, Neil Band, Clare Lyle, Aidan N. Gomez, Tom Rainforth, Yarin Gal
2021	A	`conf`	Statistically robust neural network classification. UAI Benjie Wang, Stefan Webb, Tom Rainforth
2021	A	`conf`	Towards a Theoretical Understanding of the Robustness of Variational Autoencoders. AISTATS Alexander Camuto, Matthew Willetts, Stephen J. Roberts, Chris C. Holmes, Tom Rainforth
2020	A	`conf`	A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. AISTATS Adam Foster, Martin Jankowiak, Matthew O'Meara, Yee Whye Teh, Tom Rainforth
2020	B	`conf`	A note on blind contact tracing at scale with applications to the COVID-19 pandemic. ARES Jack K. Fitzsimons, Atul Mantri, Robert Pisarczyk, Tom Rainforth, Zhikuan Zhao
2020	J	`jnl`	A note on blind contact tracing at scale with applications to the COVID-19 pandemic. CoRR Jack K. Fitzsimons, Atul Mantri, Robert Pisarczyk, Tom Rainforth, Zhikuan Zhao
2020	A*	`conf`	Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support. ICML Yuan Zhou, Hongseok Yang, Yee Whye Teh, Tom Rainforth
2020	J	`jnl`	Improving Transformation Invariance in Contrastive Representation Learning. CoRR Adam Foster, Rattana Pukdee, Tom Rainforth
2020	J	`jnl`	On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes. CoRR Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth
2020	J	`jnl`	Rethinking Semi-Supervised Learning in VAEs. CoRR Tom Joy, Sebastian M. Schmon, Philip H. S. Torr, N. Siddharth, Tom Rainforth
2020	J	`jnl`	Target-Aware Bayesian Inference: How to Beat Optimal Conventional Estimators. J. Mach. Learn. Res. Tom Rainforth, Adam Golinski, Frank Wood, Sheheryar Zaidi
2020	J	`jnl`	Towards a Theoretical Understanding of the Robustness of Variational Autoencoders. CoRR Alexander Camuto, Matthew Willetts, Stephen J. Roberts, Chris C. Holmes, Tom Rainforth
2019	—	`conf`	A Statistical Approach to Assessing Neural Network Robustness. ICLR (Poster) Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar
2019	J	`jnl`	A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. CoRR Adam Foster, Martin Jankowiak, Matthew O'Meara, Yee Whye Teh, Tom Rainforth
2019	A*	`conf`	Amortized Monte Carlo Integration. ICML Adam Golinski, Frank Wood, Tom Rainforth
2019	J	`jnl`	Amortized Monte Carlo Integration. CoRR Adam Golinski, Frank Wood, Tom Rainforth
2019	J	`jnl`	Amortized Rejection Sampling in Universal Probabilistic Programming. CoRR 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
2019	A*	`conf`	Disentangling Disentanglement in Variational Autoencoders. ICML Emile Mathieu, Tom Rainforth, N. Siddharth, Yee Whye Teh
2019	J	`jnl`	Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support. CoRR Yuan Zhou, Hongseok Yang, Yee Whye Teh, Tom Rainforth
2019	J	`jnl`	Hijacking Malaria Simulators with Probabilistic Programming. CoRR Bradley Gram-Hansen, Christian Schröder de Witt, Tom Rainforth, Philip H. S. Torr, Yee Whye Teh, Atilim Günes Baydin
2019	A	`conf`	LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models. AISTATS Yuan Zhou, Bradley J. Gram-Hansen, Tobias Kohn, Tom Rainforth, Hongseok Yang, Frank Wood
2019	J	`jnl`	LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models. CoRR Yuan Zhou, Bradley J. Gram-Hansen, Tobias Kohn, Tom Rainforth, Hongseok Yang, Frank Wood
2019	J	`jnl`	On the Fairness of Disentangled Representations. CoRR Francesco Locatello, Gabriele Abbati, Tom Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem
2019	J	`jnl`	Statistically Robust Neural Network Classification. CoRR Benjie Wang, Stefan Webb, Tom Rainforth
2019	A*	`conf`	Variational Bayesian Optimal Experimental Design. NeurIPS Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, Noah D. Goodman
2019	J	`jnl`	Variational Estimators for Bayesian Optimal Experimental Design. CoRR Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, Noah D. Goodman
2018	J	`jnl`	A Statistical Approach to Assessing Neural Network Robustness. CoRR Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar
2018	—	`conf`	Auto-Encoding Sequential Monte Carlo. ICLR (Poster) Tuan Anh Le, Maximilian Igl, Tom Rainforth, Tom Jin, Frank Wood
2018	J	`jnl`	Disentangling Disentanglement. CoRR Emile Mathieu, Tom Rainforth, Siddharth Narayanaswamy, Yee Whye Teh
2018	A*	`conf`	Faithful Inversion of Generative Models for Effective Amortized Inference. NeurIPS Stefan Webb, Adam Golinski, Robert Zinkov, Siddharth Narayanaswamy, Tom Rainforth, Yee Whye Teh, Frank Wood
2018	A	`conf`	Nesting Probabilistic Programs. UAI Tom Rainforth
2018	J	`jnl`	Nesting Probabilistic Programs. CoRR Tom Rainforth
2018	J	`jnl`	On Exploration, Exploitation and Learning in Adaptive Importance Sampling. CoRR Xiaoyu Lu, Tom Rainforth, Yuan Zhou, Jan-Willem van de Meent, Yee Whye Teh
2018	A*	`conf`	On Nesting Monte Carlo Estimators. ICML Tom Rainforth, Robert Cornish, Hongseok Yang, Andrew Warrington
2018	A*	`conf`	Tighter Variational Bounds are Not Necessarily Better. ICML Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh
2018	J	`jnl`	Tighter Variational Bounds are Not Necessarily Better. CoRR Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh
2017	J	`jnl`	Bayesian Optimization for Probabilistic Programs. CoRR Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank D. Wood
2016	—	`conf`	Bayesian Optimization for Probabilistic Programs. NIPS Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank D. Wood
2016	A*	`conf`	Interacting Particle Markov Chain Monte Carlo. ICML Tom Rainforth, Christian A. Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem van de Meent, Arnaud Doucet, Frank D. Wood
2016	J	`jnl`	Probabilistic structure discovery in time series data. CoRR David Janz, Brooks Paige, Tom Rainforth, Jan-Willem van de Meent, Frank D. Wood
2015	J	`jnl`	Canonical Correlation Forests. CoRR Tom Rainforth, Frank D. Wood