b'Yee Whye Teh'

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

∙ 07/14/2023

Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions

Accelerating the discovery of novel and more effective therapeutics is a...

0 Leo Klarner, et al. ∙

research

∙ 07/11/2023

Geometric Neural Diffusion Processes

Denoising diffusion models have proven to be a flexible and effective pa...

0 Emile Mathieu, et al. ∙

research

∙ 06/14/2023

Kalman Filter for Online Classification of Non-Stationary Data

In Online Continual Learning (OCL) a learning system receives a stream o...

0 Michalis K. Titsias, 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

∙ 04/04/2023

Incorporating Unlabelled Data into Bayesian Neural Networks

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

0 Mrinank Sharma, et al. ∙

research

∙ 02/20/2023

Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation

Skip connections and normalisation layers form two standard architectura...

0 Bobby He, et al. ∙

research

∙ 01/23/2023

Modality-Agnostic Variational Compression of Implicit Neural Representations

We introduce a modality-agnostic neural data compression algorithm based...

0 Jonathan Richard Schwarz, et al. ∙

research

∙ 12/28/2022

On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations

KL-regularized reinforcement learning from expert demonstrations has pro...

0 Tim G. J. Rudner, et al. ∙

research

∙ 07/07/2022

Riemannian Diffusion Schrödinger Bridge

Score-based generative models exhibit state of the art performance on de...

32 James Thornton, et al. ∙

research

∙ 06/20/2022

When Does Re-initialization Work?

Re-initializing a neural network during training has been observed to im...

0 Sheheryar Zaidi, et al. ∙

research

∙ 06/09/2022

Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations

Offline reinforcement learning has shown great promise in leveraging lar...

0 Cong Lu, et al. ∙

research

∙ 06/09/2022

Conformal Off-Policy Prediction in Contextual Bandits

Most off-policy evaluation methods for contextual bandits have focused o...

0 Muhammad Faaiz Taufiq, et al. ∙

research

∙ 05/31/2022

Pre-training via Denoising for Molecular Property Prediction

Many important problems involving molecular property prediction from 3D ...

0 Sheheryar Zaidi, et al. ∙

research

∙ 05/18/2022

Meta-Learning Sparse Compression Networks

Recent work in Deep Learning has re-imagined the representation of data ...

54 Jonathan Richard Schwarz, et al. ∙

research

∙ 02/06/2022

Riemannian Score-Based Generative Modeling

Score-based generative models (SGMs) are a novel class of generative mod...

5 Valentin De Bortoli, et al. ∙

research

∙ 01/30/2022

COIN++: Data Agnostic Neural Compression

Neural compression algorithms are typically based on autoencoders that r...

0 Emilien Dupont, et al. ∙

research

∙ 10/27/2021

Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels

Gaussian processes are machine learning models capable of learning unkno...

15 Michael Hutchinson, et al. ∙

research

∙ 10/01/2021

Powerpropagation: A sparsity inducing weight reparameterisation

The training of sparse neural networks is becoming an increasingly impor...

0 Jonathan Schwarz, et al. ∙

research

∙ 09/28/2021

Interoperability of statistical models in pandemic preparedness: principles and reality

We present "interoperability" as a guiding framework for statistical mod...

0 George Nicholson, 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

∙ 06/18/2021

On Contrastive Representations of Stochastic Processes

Learning representations of stochastic processes is an emerging problem ...

0 Emile Mathieu, 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

∙ 06/07/2021

BayesIMP: Uncertainty Quantification for Causal Data Fusion

While causal models are becoming one of the mainstays of machine learnin...

0 Siu Lun Chau, et al. ∙

research

∙ 03/03/2021

COIN: COmpression with Implicit Neural representations

We propose a new simple approach for image compression: instead of stori...

0 Emilien Dupont, et al. ∙

research

∙ 02/09/2021

Generative Models as Distributions of Functions

Generative models are typically trained on grid-like data such as images...

0 Emilien Dupont, et al. ∙

research

∙ 12/20/2020

LieTransformer: Equivariant self-attention for Lie Groups

Group equivariant neural networks are used as building blocks of group i...

6 Michael Hutchinson, et al. ∙

research

∙ 11/25/2020

Equivariant Conditional Neural Processes

We introduce Equivariant Conditional Neural Processes (EquivCNPs), a new...

10 Peter Holderrieth, et al. ∙

research

∙ 10/29/2020

Attentive Clustering Processes

Amortized approaches to clustering have recently received renewed attent...

11 Ari Pakman, et al. ∙

research

∙ 10/27/2020

Behavior Priors for Efficient Reinforcement Learning

As we deploy reinforcement learning agents to solve increasingly challen...

10 Dhruva Tirumala, et al. ∙

research

∙ 09/10/2020

Importance Weighted Policy Learning and Adaption

The ability to exploit prior experience to solve novel problems rapidly ...

6 Alexandre Galashov, et al. ∙

research

∙ 08/07/2020

Bootstrapping Neural Processes

Unlike in the traditional statistical modeling for which a user typicall...

1 Juho Lee, et al. ∙

research

∙ 07/27/2020

On the robustness of effectiveness estimation of nonpharmaceutical interventions against COVID-19 transmission

There remains much uncertainty about the relative effectiveness of diffe...

4 Mrinank Sharma, et al. ∙

research

∙ 07/16/2020

Lottery Tickets in Linear Models: An Analysis of Iterative Magnitude Pruning

We analyse the pruning procedure behind the lottery ticket hypothesis ar...

10 Bryn Elesedy, et al. ∙

research

∙ 07/11/2020

Bayesian Deep Ensembles via the Neural Tangent Kernel

We explore the link between deep ensembles and Gaussian processes (GPs) ...

11 Bobby He, et al. ∙

research

∙ 06/15/2020

Neural Ensemble Search for Performant and Calibrated Predictions

Ensembles of neural networks achieve superior performance compared to st...

15 Sheheryar Zaidi, et al. ∙

research

∙ 03/30/2020

Non-exchangeable feature allocation models with sublinear growth of the feature sizes

Feature allocation models are popular models used in different applicati...

1 Giuseppe Di Benedetto, et al. ∙

research

∙ 03/04/2020

Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network

We propose a method for training a deterministic deep model that can fin...

22 Joost van Amersfoort, et al. ∙

research

∙ 02/19/2020

Pruning untrained neural networks: Principles and Analysis

Overparameterized neural networks display state-of-the art performance. ...

21 Soufiane Hayou, et al. ∙

research

∙ 02/13/2020

Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise

Stochastic gradient descent with momentum (SGDm) is one of the most popu...

5 Umut Şimşekli, et al. ∙

research

∙ 12/05/2019

MetaFun: Meta-Learning with Iterative Functional Updates

Few-shot supervised learning leverages experience from previous learning...

18 Jin Xu, 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

∙ 10/31/2019

Continual Unsupervised Representation Learning

Continual learning aims to improve the ability of modern learning system...

52 Dushyant Rao, 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

∙ 09/30/2019

Deep Amortized Clustering

We propose a deep amortized clustering (DAC), a neural architecture whic...

30 Juho Lee, et al. ∙

research

∙ 06/17/2019

Stacked Capsule Autoencoders

An object can be seen as a geometrically organized set of interrelated p...

4 Adam R. Kosiorek, et al. ∙

research

∙ 06/13/2019

Random Tessellation Forests

Space partitioning methods such as random forests and the Mondrian proce...

5 Shufei Ge, et al. ∙

research

∙ 06/12/2019

Task Agnostic Continual Learning via Meta Learning

While neural networks are powerful function approximators, they suffer f...

0 Xu He, et al. ∙

research

∙ 06/07/2019

Detecting Out-of-Distribution Inputs to Deep Generative Models Using a Test for Typicality

Recent work has shown that deep generative models can assign higher like...

5 Eric Nalisnick, et al. ∙

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