
COIN: COmpression with Implicit Neural representations
We propose a new simple approach for image compression: instead of stori...
read it

Generative Models as Distributions of Functions
Generative models are typically trained on gridlike data such as images...
read it

LieTransformer: Equivariant selfattention for Lie Groups
Group equivariant neural networks are used as building blocks of group i...
read it

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

Attentive Clustering Processes
Amortized approaches to clustering have recently received renewed attent...
read it

Behavior Priors for Efficient Reinforcement Learning
As we deploy reinforcement learning agents to solve increasingly challen...
read it

Importance Weighted Policy Learning and Adaption
The ability to exploit prior experience to solve novel problems rapidly ...
read it

Bootstrapping Neural Processes
Unlike in the traditional statistical modeling for which a user typicall...
read it

On the robustness of effectiveness estimation of nonpharmaceutical interventions against COVID19 transmission
There remains much uncertainty about the relative effectiveness of diffe...
read it

Lottery Tickets in Linear Models: An Analysis of Iterative Magnitude Pruning
We analyse the pruning procedure behind the lottery ticket hypothesis ar...
read it

Bayesian Deep Ensembles via the Neural Tangent Kernel
We explore the link between deep ensembles and Gaussian processes (GPs) ...
read it

Neural Ensemble Search for Performant and Calibrated Predictions
Ensembles of neural networks achieve superior performance compared to st...
read it

Nonexchangeable feature allocation models with sublinear growth of the feature sizes
Feature allocation models are popular models used in different applicati...
read it

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...
read it

Pruning untrained neural networks: Principles and Analysis
Overparameterized neural networks display stateofthe art performance. ...
read it

Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under HeavyTailed Gradient Noise
Stochastic gradient descent with momentum (SGDm) is one of the most popu...
read it

MetaFun: MetaLearning with Iterative Functional Updates
Fewshot supervised learning leverages experience from previous learning...
read it

A Unified Stochastic Gradient Approach to Designing BayesianOptimal Experiments
We introduce a fully stochastic gradient based approach to Bayesian opti...
read it

Continual Unsupervised Representation Learning
Continual learning aims to improve the ability of modern learning system...
read it

Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
Universal probabilistic programming systems (PPSs) provide a powerful an...
read it

Amortized Rejection Sampling in Universal Probabilistic Programming
Existing approaches to amortized inference in probabilistic programs wit...
read it

Deep Amortized Clustering
We propose a deep amortized clustering (DAC), a neural architecture whic...
read it

Stacked Capsule Autoencoders
An object can be seen as a geometrically organized set of interrelated p...
read it

Random Tessellation Forests
Space partitioning methods such as random forests and the Mondrian proce...
read it

Task Agnostic Continual Learning via Meta Learning
While neural networks are powerful function approximators, they suffer f...
read it

Detecting OutofDistribution Inputs to Deep Generative Models Using a Test for Typicality
Recent work has shown that deep generative models can assign higher like...
read it

Noise Contrastive MetaLearning for Conditional Density Estimation using Kernel Mean Embeddings
Current metalearning approaches focus on learning functional representa...
read it

Hijacking Malaria Simulators with Probabilistic Programming
Epidemiology simulations have become a fundamental tool in the fight aga...
read it

Meta reinforcement learning as task inference
Humans achieve efficient learning by relying on prior knowledge about th...
read it

Metalearning of Sequential Strategies
In this report we review memorybased metalearning as a tool for buildi...
read it

Information asymmetry in KLregularized RL
Many real world tasks exhibit rich structure that is repeated across dif...
read it

Augmented Neural ODEs
We show that Neural Ordinary Differential Equations (ODEs) learn represe...
read it

MetaLearning surrogate models for sequential decision making
Metalearning methods leverage past experience to learn datadriven indu...
read it

An Exact Auxiliary Variable Gibbs Sampler for a Class of Diffusions
Stochastic differential equations (SDEs) or diffusions are continuousva...
read it

Exploiting Hierarchy for Learning and Transfer in KLregularized RL
As reinforcement learning agents are tasked with solving more challengin...
read it

Variational Estimators for Bayesian Optimal Experimental Design
Bayesian optimal experimental design (BOED) is a principled framework fo...
read it

Variational Bayesian Optimal Experimental Design
Bayesian optimal experimental design (BOED) is a principled framework fo...
read it

Hybrid Models with Deep and Invertible Features
We propose a neural hybrid model consisting of a linear model defined on...
read it

Functional Regularisation for Continual Learning using Gaussian Processes
We introduce a novel approach for supervised continual learning based on...
read it

Probabilistic symmetry and invariant neural networks
In an effort to improve the performance of deep neural networks in data...
read it

Hierarchical Representations with Poincaré Variational AutoEncoders
The Variational AutoEncoder (VAE) model has become widely popular as a ...
read it

Attentive Neural Processes
Neural Processes (NPs) (Garnelo et al 2018a;b) approach regression by le...
read it

Disentangling Disentanglement
We develop a generalised notion of disentanglement in Variational AutoE...
read it

Neural probabilistic motor primitives for humanoid control
We focus on the problem of learning a single motor module that can flexi...
read it

Statistical Verification of Neural Networks
We present a new approach to neural network verification based on estima...
read it

On Exploration, Exploitation and Learning in Adaptive Importance Sampling
We study adaptive importance sampling (AIS) as an online learning proble...
read it

Do Deep Generative Models Know What They Don't Know?
A neural network deployed in the wild may be asked to make predictions f...
read it

Set Transformer
Many machine learning tasks such as multiple instance learning, 3D shape...
read it

Hamiltonian Descent Methods
We propose a family of optimization methods that achieve linear converge...
read it

Sampling and Inference for Beta NeutraltotheLeft Models of Sparse Networks
Empirical evidence suggests that heavytailed degree distributions occur...
read it
Yee Whye Teh
is this you? claim profile
Professorial Research Fellow (RSIV) of Statistical Machine Learning at University of Oxford