b'Tom Rainforth'

research

∙ 08/01/2023

SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning

The recent progress in large language models (LLMs), especially the inve...

0 Ning Miao, et al. ∙

research

∙ 07/23/2023

In-Context Learning in Large Language Models Learns Label Relationships but Is Not Conventional Learning

The performance of Large Language Models (LLMs) on downstream tasks ofte...

0 Jannik Kossen, et al. ∙

research

∙ 06/12/2023

On the Expected Size of Conformal Prediction Sets

While conformal predictors reap the benefits of rigorous statistical gua...

0 Guneet S. Dhillon, et al. ∙

research

∙ 05/25/2023

Trans-Dimensional Generative Modeling via Jump Diffusion Models

We propose a new class of generative models that naturally handle data o...

0 Andrew Campbell, et al. ∙

research

∙ 05/24/2023

Deep Stochastic Processes via Functional Markov Transition Operators

We introduce Markov Neural Processes (MNPs), a new class of Stochastic P...

0 Jin Xu, et al. ∙

research

∙ 04/17/2023

Prediction-Oriented Bayesian Active Learning

Information-theoretic approaches to active learning have traditionally f...

0 Freddie Bickford Smith, et al. ∙

research

∙ 04/04/2023

Incorporating Unlabelled Data into Bayesian Neural Networks

We develop a contrastive framework for learning better prior distributio...

0 Mrinank Sharma, et al. ∙

research

∙ 02/28/2023

Modern Bayesian Experimental Design

Bayesian experimental design (BED) provides a powerful and general frame...

0 Tom Rainforth, et al. ∙

research

∙ 11/11/2022

Do Bayesian Neural Networks Need To Be Fully Stochastic?

We investigate the efficacy of treating all the parameters in a Bayesian...

0 Mrinank Sharma, et al. ∙

research

∙ 05/30/2022

A Continuous Time Framework for Discrete Denoising Models

We provide the first complete continuous time framework for denoising di...

0 Andrew Campbell, et al. ∙

research

∙ 02/14/2022

Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation

We propose Active Surrogate Estimators (ASEs), a new method for label-ef...

3 Jannik Kossen, et al. ∙

research

∙ 11/03/2021

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

We introduce implicit Deep Adaptive Design (iDAD), a new method for perf...

0 Desi R. Ivanova, et al. ∙

research

∙ 10/26/2021

Online Variational Filtering and Parameter Learning

We present a variational method for online state estimation and paramete...

0 Andrew Campbell, et al. ∙

research

∙ 06/25/2021

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

We introduce a simple and effective method for learning VAEs with contro...

0 Ning Miao, et al. ∙

research

∙ 06/23/2021

Learning Multimodal VAEs through Mutual Supervision

Multimodal VAEs seek to model the joint distribution over heterogeneous ...

0 Tom Joy, et al. ∙

research

∙ 06/22/2021

Active Learning under Pool Set Distribution Shift and Noisy Data

Active Learning is essential for more label-efficient deep learning. Bay...

39 Andreas Kirsch, et al. ∙

research

∙ 06/10/2021

Group Equivariant Subsampling

Subsampling is used in convolutional neural networks (CNNs) in the form ...

0 Jin Xu, et al. ∙

research

∙ 06/09/2021

Expectation Programming

Building on ideas from probabilistic programming, we introduce the conce...

0 Tim Reichelt, et al. ∙

research

∙ 06/04/2021

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

We challenge a common assumption underlying most supervised deep learnin...

19 Jannik Kossen, et al. ∙

research

∙ 03/09/2021

Active Testing: Sample-Efficient Model Evaluation

We introduce active testing: a new framework for sample-efficient model ...

0 Jannik Kossen, et al. ∙

research

∙ 03/03/2021

Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design

We introduce Deep Adaptive Design (DAD), a method for amortizing the cos...

0 Adam Foster, et al. ∙

research

∙ 02/15/2021

Certifiably Robust Variational Autoencoders

We introduce an approach for training Variational Autoencoders (VAEs) th...

0 Ben Barrett, et al. ∙

research

∙ 01/27/2021

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

Active learning is a powerful tool when labelling data is expensive, but...

0 Sebastian Farquhar, et al. ∙

research

∙ 11/01/2020

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

We show that the gradient estimates used in training Deep Gaussian Proce...

0 Tim G. J. Rudner, et al. ∙

research

∙ 10/19/2020

Improving Transformation Invariance in Contrastive Representation Learning

We propose methods to strengthen the invariance properties of representa...

0 Adam Foster, et al. ∙

research

∙ 07/14/2020

Towards a Theoretical Understanding of the Robustness of Variational Autoencoders

We make inroads into understanding the robustness of Variational Autoenc...

16 Alexander Camuto, et al. ∙

research

∙ 06/17/2020

Rethinking Semi-Supervised Learning in VAEs

We present an alternative approach to semi-supervision in variational au...

21 Tom Joy, et al. ∙

research

∙ 04/10/2020

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

The current COVID-19 pandemic highlights the utility of contact tracing,...

0 Jack K. Fitzsimons, et al. ∙

research

∙ 12/10/2019

Statistically Robust Neural Network Classification

Recently there has been much interest in quantifying the robustness of n...

0 Benjie Wang, et al. ∙

research

∙ 11/01/2019

A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments

We introduce a fully stochastic gradient based approach to Bayesian opti...

23 Adam Foster, et al. ∙

research

∙ 10/29/2019

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

Universal probabilistic programming systems (PPSs) provide a powerful an...

21 Yuan Zhou, et al. ∙

research

∙ 10/20/2019

Amortized Rejection Sampling in Universal Probabilistic Programming

Existing approaches to amortized inference in probabilistic programs wit...

11 Saeid Naderiparizi, et al. ∙

research

∙ 07/18/2019

Amortized Monte Carlo Integration

Current approaches to amortizing Bayesian inference focus solely on appr...

2 Adam Golinski, et al. ∙

research

∙ 05/31/2019

On the Fairness of Disentangled Representations

Recently there has been a significant interest in learning disentangled ...

6 Francesco Locatello, et al. ∙

research

∙ 05/29/2019

Hijacking Malaria Simulators with Probabilistic Programming

Epidemiology simulations have become a fundamental tool in the fight aga...

4 Bradley Gram-Hansen, et al. ∙

research

∙ 03/13/2019

Variational Estimators for Bayesian Optimal Experimental Design

Bayesian optimal experimental design (BOED) is a principled framework fo...

16 Adam Foster, et al. ∙

research

∙ 03/13/2019

Variational Bayesian Optimal Experimental Design

Bayesian optimal experimental design (BOED) is a principled framework fo...

0 Adam Foster, et al. ∙

research

∙ 03/06/2019

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

We develop a new Low-level, First-order Probabilistic Programming Langua...

12 Yuan Zhou, et al. ∙

research

∙ 12/06/2018

Disentangling Disentanglement

We develop a generalised notion of disentanglement in Variational Auto-E...

6 Emile Mathieu, et al. ∙

research

∙ 11/17/2018

Statistical Verification of Neural Networks

We present a new approach to neural network verification based on estima...

0 Stefan Webb, et al. ∙

research

∙ 10/31/2018

On Exploration, Exploitation and Learning in Adaptive Importance Sampling

We study adaptive importance sampling (AIS) as an online learning proble...

16 Xiaoyu Lu, et al. ∙

research

∙ 06/25/2018

Inference Trees: Adaptive Inference with Exploration

We introduce inference trees (ITs), a new class of inference methods tha...

2 Tom Rainforth, et al. ∙

research

∙ 03/16/2018

Nesting Probabilistic Programs

We formalize the notion of nesting probabilistic programming queries and...

0 Tom Rainforth, et al. ∙

research

∙ 02/13/2018

Tighter Variational Bounds are Not Necessarily Better

We provide theoretical and empirical evidence that using tighter evidenc...

0 Tom Rainforth, et al. ∙

research

∙ 09/18/2017

On the Opportunities and Pitfalls of Nesting Monte Carlo Estimators

We present a formalization of nested Monte Carlo (NMC) estimation, where...

0 Tom Rainforth, et al. ∙

research

∙ 07/13/2017

Bayesian Optimization for Probabilistic Programs

We present the first general purpose framework for marginal maximum a po...

0 Tom Rainforth, et al. ∙

research

∙ 05/29/2017

Auto-Encoding Sequential Monte Carlo

We introduce AESMC: a method for using deep neural networks for simultan...

0 Tuan Anh Le, et al. ∙

research

∙ 11/21/2016

Probabilistic structure discovery in time series data

Existing methods for structure discovery in time series data construct i...

0 David Janz, et al. ∙

research

∙ 02/16/2016

Interacting Particle Markov Chain Monte Carlo

We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a P...

0 Tom Rainforth, et al. ∙

Tom Rainforth

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