Tom Rainforth
University of Oxford
H-index: 24
Europe-United Kingdom
Top articles of Tom Rainforth
Title | Journal | Author(s) | Publication Date |
|---|---|---|---|
On the expected size of conformal prediction sets | Guneet S Dhillon George Deligiannidis Tom Rainforth | 2024/4/18 | |
Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design | arXiv preprint arXiv:2402.04997 | Andrew Campbell Jason Yim Regina Barzilay Tom Rainforth Tommi Jaakkola | 2024/2/7 |
Modern Bayesian experimental design | Tom Rainforth Adam Foster Desi R Ivanova Freddie Bickford Smith | 2024/2 | |
Selfcheck: Using LLMs to zero-shot check their own step-by-step reasoning | International Conference on Learning Representations | Ning Miao Yee Whye Teh Tom Rainforth | 2024 |
Making Better Use of Unlabelled Data in Bayesian Active Learning | Freddie Bickford Smith Adam Foster Tom Rainforth | 2024/4/18 | |
Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support | Tim Reichelt Luke Ong Tom Rainforth | 2024/4/18 | |
In-context learning learns label relationships but is not conventional learning | Jannik Kossen Yarin Gal Tom Rainforth | 2023 | |
Trans-Dimensional Generative Modeling via Jump Diffusion Models | Advances in Neural Information Processing Systems | Andrew Campbell William Harvey Christian Weilbach Valentin De Bortoli Thomas Rainforth | 2024/2/13 |
Daisee: Adaptive Importance Sampling by Balancing Exploration and Exploitation | Scandinavian Journal of Statistics | Xiaoyu Lu Tom Rainforth Yee Whye Teh | 2023 |
Deep Stochastic Processes via Functional Markov Transition Operators | Advances in Neural Information Processing Systems | Jin Xu Emilien Dupont Kaspar Märtens Thomas Rainforth Yee Whye Teh | 2024/2/13 |
Do Bayesian Neural Networks Need To Be Fully Stochastic? | Mrinank Sharma Sebastian Farquhar Eric Nalisnick Tom Rainforth | 2023/4/11 | |
Prediction-oriented bayesian active learning | Freddie Bickford Smith Andreas Kirsch Sebastian Farquhar Yarin Gal Adam Foster | 2023/4/11 | |
Incorporating unlabelled data into Bayesian neural networks | arXiv preprint arXiv:2304.01762 | Mrinank Sharma Tom Rainforth Yee Whye Teh Vincent Fortuin | 2023/4/4 |
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design | Desi R. Ivanova Joel Jennings Tom Rainforth Cheng Zhang Adam Foster | 2023/7 | |
Learning Instance-Specific Augmentations by Capturing Local Invariances | arXiv preprint arXiv:2206.00051 | Ning Miao Tom Rainforth Emile Mathieu Yann Dubois Yee Whye Teh | 2022/5/31 |
Learning Multimodal VAEs through Mutual Supervision | International Conference on Learning Representations | Tom Joy Yuge Shi Philip HS Torr Tom Rainforth Sebastian M Schmon | 2022 |
Certifiably robust variational autoencoders | Advances in Neural Information Processing Systems (NeurIPS), Bayesian Deep Learning Workshop, 2021 | Ben Barrett Alexander Camuto Matthew Willetts Tom Rainforth | 2021/2/15 |
Amortized rejection sampling in universal probabilistic programming | Saeid Naderiparizi Adam Scibior Andreas Munk Mehrdad Ghadiri Atilim Gunes Baydin | 2022/5/3 | |
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation | Advances in Neural Information Processing Systems | Jannik Kossen Sebastian Farquhar Yarin Gal Thomas Rainforth | 2022/12/6 |
Expectation programming: Adapting probabilistic programming systems to estimate expectations efficiently | Tim Reichelt Adam Goliński Luke Ong Tom Rainforth | 2022/8/17 |
Co-Authors
