
Importance Weighted Policy Learning and Adaption
The ability to exploit prior experience to solve novel problems rapidly ...
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Bootstrapping Neural Processes
Unlike in the traditional statistical modeling for which a user typicall...
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On the robustness of effectiveness estimation of nonpharmaceutical interventions against COVID19 transmission
There remains much uncertainty about the relative effectiveness of diffe...
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Lottery Tickets in Linear Models: An Analysis of Iterative Magnitude Pruning
We analyse the pruning procedure behind the lottery ticket hypothesis ar...
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Bayesian Deep Ensembles via the Neural Tangent Kernel
We explore the link between deep ensembles and Gaussian processes (GPs) ...
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Neural Ensemble Search for Performant and Calibrated Predictions
Ensembles of neural networks achieve superior performance compared to st...
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Nonexchangeable feature allocation models with sublinear growth of the feature sizes
Feature allocation models are popular models used in different applicati...
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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...
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Pruning untrained neural networks: Principles and Analysis
Overparameterized neural networks display stateofthe art performance. ...
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Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under HeavyTailed Gradient Noise
Stochastic gradient descent with momentum (SGDm) is one of the most popu...
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MetaFun: MetaLearning with Iterative Functional Updates
Fewshot supervised learning leverages experience from previous learning...
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A Unified Stochastic Gradient Approach to Designing BayesianOptimal Experiments
We introduce a fully stochastic gradient based approach to Bayesian opti...
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Continual Unsupervised Representation Learning
Continual learning aims to improve the ability of modern learning system...
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Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
Universal probabilistic programming systems (PPSs) provide a powerful an...
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Amortized Rejection Sampling in Universal Probabilistic Programming
Existing approaches to amortized inference in probabilistic programs wit...
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Deep Amortized Clustering
We propose a deep amortized clustering (DAC), a neural architecture whic...
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Stacked Capsule Autoencoders
An object can be seen as a geometrically organized set of interrelated p...
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Random Tessellation Forests
Space partitioning methods such as random forests and the Mondrian proce...
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Task Agnostic Continual Learning via Meta Learning
While neural networks are powerful function approximators, they suffer f...
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Detecting OutofDistribution Inputs to Deep Generative Models Using a Test for Typicality
Recent work has shown that deep generative models can assign higher like...
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Noise Contrastive MetaLearning for Conditional Density Estimation using Kernel Mean Embeddings
Current metalearning approaches focus on learning functional representa...
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Hijacking Malaria Simulators with Probabilistic Programming
Epidemiology simulations have become a fundamental tool in the fight aga...
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Meta reinforcement learning as task inference
Humans achieve efficient learning by relying on prior knowledge about th...
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Metalearning of Sequential Strategies
In this report we review memorybased metalearning as a tool for buildi...
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Information asymmetry in KLregularized RL
Many real world tasks exhibit rich structure that is repeated across dif...
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Augmented Neural ODEs
We show that Neural Ordinary Differential Equations (ODEs) learn represe...
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MetaLearning surrogate models for sequential decision making
Metalearning methods leverage past experience to learn datadriven indu...
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An Exact Auxiliary Variable Gibbs Sampler for a Class of Diffusions
Stochastic differential equations (SDEs) or diffusions are continuousva...
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Exploiting Hierarchy for Learning and Transfer in KLregularized RL
As reinforcement learning agents are tasked with solving more challengin...
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Variational Estimators for Bayesian Optimal Experimental Design
Bayesian optimal experimental design (BOED) is a principled framework fo...
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Variational Bayesian Optimal Experimental Design
Bayesian optimal experimental design (BOED) is a principled framework fo...
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Hybrid Models with Deep and Invertible Features
We propose a neural hybrid model consisting of a linear model defined on...
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Functional Regularisation for Continual Learning using Gaussian Processes
We introduce a novel approach for supervised continual learning based on...
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Probabilistic symmetry and invariant neural networks
In an effort to improve the performance of deep neural networks in data...
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Hierarchical Representations with Poincaré Variational AutoEncoders
The Variational AutoEncoder (VAE) model has become widely popular as a ...
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Attentive Neural Processes
Neural Processes (NPs) (Garnelo et al 2018a;b) approach regression by le...
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Disentangling Disentanglement
We develop a generalised notion of disentanglement in Variational AutoE...
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Neural probabilistic motor primitives for humanoid control
We focus on the problem of learning a single motor module that can flexi...
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Statistical Verification of Neural Networks
We present a new approach to neural network verification based on estima...
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On Exploration, Exploitation and Learning in Adaptive Importance Sampling
We study adaptive importance sampling (AIS) as an online learning proble...
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Do Deep Generative Models Know What They Don't Know?
A neural network deployed in the wild may be asked to make predictions f...
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Set Transformer
Many machine learning tasks such as multiple instance learning, 3D shape...
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Hamiltonian Descent Methods
We propose a family of optimization methods that achieve linear converge...
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Sampling and Inference for Beta NeutraltotheLeft Models of Sparse Networks
Empirical evidence suggests that heavytailed degree distributions occur...
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Neural Processes
A neural network (NN) is a parameterised function that can be tuned via ...
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Conditional Neural Processes
Deep neural networks excel at function approximation, yet they are typic...
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Inference Trees: Adaptive Inference with Exploration
We introduce inference trees (ITs), a new class of inference methods tha...
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Controllable Semantic Image Inpainting
We develop a method for usercontrollable semantic image inpainting: Giv...
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Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects
We present Sequential Attend, Infer, Repeat (SQAIR), an interpretable de...
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Mix&Match  Agent Curricula for Reinforcement Learning
We introduce Mix&Match (M&M)  a training framework designed to facilita...
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Yee Whye Teh
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Professorial Research Fellow (RSIV) of Statistical Machine Learning at University of Oxford