Tom Rainforth
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
40
Venues
4
Active years
2016–2025
Best venue rank
A*
Where they publish
Papers
40 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | ICLR | Shh, don't say that! Domain Certification in LLMs. | Cornelius Emde, Alasdair Paren, Preetham Arvind, Maxime Guillaume Kayser, Tom Rainforth, Thomas Lukasiewicz, Philip Torr, Adel Bibi |
| 2025 | ICML | Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design. | Marcel Hedman, Desi R. Ivanova, Cong Guan, Tom Rainforth |
| 2025 | ICML | Do Bayesian Neural Networks Actually Behave Like Bayesian Models? | Gbor Pituk, Vik Shirvaikar, Tom Rainforth |
| 2025 | ICML | Rethinking Aleatoric and Epistemic Uncertainty. | Freddie Bickford Smith, Jannik Kossen, Eleanor Trollope, Mark van der Wilk, Adam Foster, Tom Rainforth |
| 2024 | AISTATS | On the Expected Size of Conformal Prediction Sets. | Guneet S. Dhillon, George Deligiannidis, Tom Rainforth |
| 2024 | AISTATS | Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support. | Tim Reichelt, Luke Ong, Tom Rainforth |
| 2024 | AISTATS | Making Better Use of Unlabelled Data in Bayesian Active Learning. | Freddie Bickford Smith, Adam Foster, Tom Rainforth |
| 2024 | ICLR | In-Context Learning Learns Label Relationships but Is Not Conventional Learning. | Jannik Kossen, Yarin Gal, Tom Rainforth |
| 2024 | ICLR | SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning. | Ning Miao, Yee Whye Teh, Tom Rainforth |
| 2024 | ICML | 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 |
| 2023 | AISTATS | Do Bayesian Neural Networks Need To Be Fully Stochastic? | Mrinank Sharma, Sebastian Farquhar, Eric T. Nalisnick, Tom Rainforth |
| 2023 | AISTATS | Prediction-Oriented Bayesian Active Learning. | Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth |
| 2023 | ICML | CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. | Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster |
| 2023 | ICML | Learning Instance-Specific Augmentations by Capturing Local Invariances. | Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh, Adam Foster, Hyunjik Kim |
| 2022 | AISTATS | Certifiably Robust Variational Autoencoders. | Ben Barrett, Alexander Camuto, Matthew Willetts, Tom Rainforth |
| 2022 | AISTATS | Amortized Rejection Sampling in Universal Probabilistic Programming. | Saeid Naderiparizi, Adam Scibior, Andreas Munk, Mehrdad Ghadiri, Atilim Gunes Baydin, Bradley J. Gram-Hansen, Christian A. Schrder de Witt, Robert Zinkov, Philip H. S. Torr, Tom Rainforth, Yee Whye Teh, Frank Wood |
| 2022 | ICLR | Learning Multimodal VAEs through Mutual Supervision. | Tom Joy, Yuge Shi, Philip H. S. Torr, Tom Rainforth, Sebastian M. Schmon, Siddharth Narayanaswamy |
| 2022 | ICLR | On Incorporating Inductive Biases into VAEs. | Ning Miao, Emile Mathieu, Siddharth N, Yee Whye Teh, Tom Rainforth |
| 2022 | UAI | Expectation programming: Adapting probabilistic programming systems to estimate expectations efficiently. | Tim Reichelt, Adam Golinski, Luke Ong, Tom Rainforth |
| 2021 | AISTATS | Towards a Theoretical Understanding of the Robustness of Variational Autoencoders. | Alexander Camuto, Matthew Willetts, Stephen J. Roberts, Chris C. Holmes, Tom Rainforth |
| 2021 | ICLR | On Statistical Bias In Active Learning: How and When to Fix It. | Sebastian Farquhar, Yarin Gal, Tom Rainforth |
| 2021 | ICLR | Improving Transformation Invariance in Contrastive Representation Learning. | Adam Foster, Rattana Pukdee, Tom Rainforth |
| 2021 | ICLR | Capturing Label Characteristics in VAEs. | Tom Joy, Sebastian M. Schmon, Philip H. S. Torr, Siddharth Narayanaswamy, Tom Rainforth |
| 2021 | ICLR | Improving VAEs' Robustness to Adversarial Attack. | Matthew Willetts, Alexander Camuto, Tom Rainforth, Stephen J. Roberts, Christopher C. Holmes |
| 2021 | ICML | Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design. | Adam Foster, Desi R. Ivanova, Ilyas Malik, Tom Rainforth |
| 2021 | ICML | Active Testing: Sample-Efficient Model Evaluation. | Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth |
| 2021 | ICML | On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes. | Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth |
| 2021 | ICML | Probabilistic Programs with Stochastic Conditioning. | David Tolpin, Yuan Zhou, Tom Rainforth, Hongseok Yang |
| 2021 | UAI | Statistically robust neural network classification. | Benjie Wang, Stefan Webb, Tom Rainforth |
| 2020 | AISTATS | A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. | Adam Foster, Martin Jankowiak, Matthew O'Meara, Yee Whye Teh, Tom Rainforth |
| 2020 | ICML | Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support. | Yuan Zhou, Hongseok Yang, Yee Whye Teh, Tom Rainforth |
| 2019 | AISTATS | 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 |
| 2019 | ICLR | A Statistical Approach to Assessing Neural Network Robustness. | Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar |
| 2019 | ICML | Amortized Monte Carlo Integration. | Adam Golinski, Frank Wood, Tom Rainforth |
| 2019 | ICML | Disentangling Disentanglement in Variational Autoencoders. | Emile Mathieu, Tom Rainforth, N. Siddharth, Yee Whye Teh |
| 2018 | ICLR | Auto-Encoding Sequential Monte Carlo. | Tuan Anh Le, Maximilian Igl, Tom Rainforth, Tom Jin, Frank Wood |
| 2018 | ICML | On Nesting Monte Carlo Estimators. | Tom Rainforth, Robert Cornish, Hongseok Yang, Andrew Warrington |
| 2018 | ICML | 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 |
| 2018 | UAI | Nesting Probabilistic Programs. | Tom Rainforth |
| 2016 | ICML | 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 |