Tom Rainforth

发表

On the Fairness of Disentangled Representations

Stefan Bauer, Bernhard Schölkopf, Francesco Locatello, 2019, NeurIPS.

Inference Trees: Adaptive Inference with Exploration

Yee Whye Teh, Hongseok Yang, Tom Rainforth, 2018, 1806.09550.

Interacting Particle Markov Chain Monte Carlo

Fredrik Lindsten, Arnaud Doucet, Frank D. Wood, 2016, ICML.

Amortized Monte Carlo Integration

Tom Rainforth, Frank Wood, Adam Golinski, 2019, ICML.

A Statistical Approach to Assessing Neural Network Robustness pdf

Yee Whye Teh, Tom Rainforth, M. Pawan Kumar, 2018, ICLR.

Towards a Theoretical Understanding of the Robustness of Variational Autoencoders pdf

Stephen Roberts, Tom Rainforth, Matthew Willetts, 2020, ArXiv.

Faithful Inversion of Generative Models for Effective Amortized Inference

Yee Whye Teh, Tom Rainforth, Frank Wood, 2017, NeurIPS.

Amortized Rejection Sampling in Universal Probabilistic Programming pdf

Yee Whye Teh, Philip H. S. Torr, Tom Rainforth, 2019, ArXiv.

A note on blind contact tracing at scale with applications to the COVID-19 pandemic

Tom Rainforth, Jack K. Fitzsimons, Atul Mantri, 2020, ARES.

LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models

Frank D. Wood, Hongseok Yang, Tom Rainforth, 2019, AISTATS.

Nesting Probabilistic Programs pdf

Tom Rainforth, Tom Rainforth, 2018, UAI.

On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes pdf

Tom Rainforth, Yarin Gal, Tim G. J. Rudner, 2020, ArXiv.

Variational Bayesian Optimal Experimental Design

Yee Whye Teh, Noah D. Goodman, Tom Rainforth, 2019, NeurIPS.

Statistically Robust Neural Network Classification pdf

Tom Rainforth, Stefan Webb, Benjie Wang, 2019, ArXiv.

Bayesian Optimization for Probabilistic Programs

Frank D. Wood, Michael A. Osborne, Tuan Anh Le, 2017, NIPS.

Auto-Encoding Sequential Monte Carlo

Tuan Anh Le, Tom Rainforth, Frank Wood, 2017, ICLR.

Probabilistic structure discovery in time series data pdf

Frank D. Wood, Tom Rainforth, Brooks Paige, 2016, ArXiv.

Improving VAEs' Robustness to Adversarial Attack

Stephen Roberts, Christopher Holmes, Tom Rainforth, 2019 .

On Exploration, Exploitation and Learning in Adaptive Importance Sampling pdf

Yee Whye Teh, Yuan Zhou, Xiaoyu Lu, 2018, ArXiv.

Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities

Frank D. Wood, Hongseok Yang, Tom Rainforth, 2018, 1804.03523.

Hijacking Malaria Simulators with Probabilistic Programming pdf

Yee Whye Teh, Philip H. S. Torr, Tom Rainforth, 2019, ArXiv.

Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support pdf

Yee Whye Teh, Hongseok Yang, Tom Rainforth, 2019, ICML.

On Nesting Monte Carlo Estimators

Hongseok Yang, Tom Rainforth, Andrew Warrington, 2017, ICML.

Canonical Correlation Forests pdf

Frank D. Wood, Tom Rainforth, F. Wood, 2015, ArXiv.

Disentangling Disentanglement in Variational Autoencoders

Yee Whye Teh, Tom Rainforth, Emile Mathieu, 2018, ICML.

Target-Aware Bayesian Inference: How to Beat Optimal Conventional Estimators

Kilian Q. Weinberger, Tom Rainforth, Frank Wood, 2020, J. Mach. Learn. Res..

Disentangling Disentanglement pdf

Yee Whye Teh, Tom Rainforth, Emile Mathieu, 2018, ArXiv.

Automating inference, learning, and design using probabilistic programming

Tom Rainforth, Tom Rainforth, 2017 .

Improving Transformation Invariance in Contrastive Representation Learning pdf

Tom Rainforth, Adam Foster, Rattana Pukdee, 2020, ICLR.

Sampling and inference for discrete random probability measures in probabilistic programs

Yee Whye Teh, Tom Rainforth, Emile Mathieu, 2017 .

Rethinking Semi-Supervised Learning in VAEs pdf

Tom Rainforth, N. Siddharth, Philip H. S. Torr, 2020, ArXiv.

A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments pdf

Yee Whye Teh, Tom Rainforth, Martin Jankowiak, 2020, AISTATS.

Minimally Faithful Inversion of Graphical Models

Yee Whye Teh, Tom Rainforth, Frank Wood, 2017 .

Tighter Variational Bounds are Not Necessarily Better

Yee Whye Teh, Tuan Anh Le, Tom Rainforth, 2018, ICML.

The DARC Toolbox: automated, flexible, and efficient delayed and risky choice experiments using Bayesian adaptive design

Tom Rainforth, Benjamin T. Vincent, B. Vincent, 2017 .

Variational Estimators for Bayesian Optimal Experimental Design pdf

Yee Whye Teh, Noah D. Goodman, Tom Rainforth, 2019, ArXiv.

Test Distribution-Aware Active Learning: A Principled Approach Against Distribution Shift and Outliers

Tom Rainforth, Yarin Gal, Andreas Kirsch, 2021, 2106.11719.

Statistically Robust Neural Network Classification

Tom Rainforth, Stefan Webb, Benjie Wang, 2019, UAI.

Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning pdf

Tom Rainforth, Yarin Gal, Clare Lyle, 2021, ArXiv.

Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design

Tom Rainforth, Adam Foster, Desi R. Ivanova, 2021, ICML.

Expectation Programming pdf

C.-H. Luke Ong, Tom Rainforth, Tim Reichelt, 2021, ArXiv.

Improving VAEs' Robustness to Adversarial Attack

Tom Rainforth, Matthew Willetts, Alexander Camuto, 2019, ICLR.

Online Variational Filtering and Parameter Learning pdf

Arnaud Doucet, Tom Rainforth, Andrew Campbell, 2021, ArXiv.

On Statistical Bias In Active Learning: How and When To Fix It pdf

Tom Rainforth, Yarin Gal, Sebastian Farquhar, 2021, ICLR.

On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes

Tom Rainforth, Yarin Gal, Tim G. J. Rudner, 2020, ICML.

Active Learning under Pool Set Distribution Shift and Noisy Data pdf

Tom Rainforth, Yarin Gal, Andreas Kirsch, 2021, ArXiv.

Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods pdf

Michael U. Gutmann, Tom Rainforth, Steven Kleinegesse, 2021, NeurIPS.

Group Equivariant Subsampling pdf

Yee Whye Teh, Tom Rainforth, Hyunjik Kim, 2021, NeurIPS.

Active Testing: Sample-Efficient Model Evaluation

Tom Rainforth, Yarin Gal, Sebastian Farquhar, 2021, ICML.

Certifiably Robust Variational Autoencoders pdf

Tom Rainforth, Matthew Willetts, Alexander Camuto, 2021, ArXiv.

InteL-VAEs: Adding Inductive Biases to Variational Auto-Encoders via Intermediary Latents pdf

Yee Whye Teh, Tom Rainforth, Emile Mathieu, 2021, ArXiv.

Learning Multimodal VAEs through Mutual Supervision pdf

Philip H.S. Torr, Tom Rainforth, N. Siddharth, 2021, ArXiv.

Capturing Label Characteristics in VAEs

Philip H. S. Torr, Tom Rainforth, N. Siddharth, 2021, ICLR.

On the Pitfalls of Nested Monte Carlo

Frank D. Wood, Hongseok Yang, Tom Rainforth, 2016, 1612.00951.