research
∙
06/01/2023
Regularizing Towards Soft Equivariance Under Mixed Symmetries
Datasets often have their intrinsic symmetries, and particular deep-lear...
share
research
∙
02/02/2023
Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature Learning
We consider the optimisation of large and shallow neural networks via gr...
research
∙
08/22/2022
Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference
We present a static analysis for discovering differentiable or more gene...
research
∙
06/28/2022
Learning Symmetric Rules with SATNet
SATNet is a differentiable constraint solver with a custom backpropagati...
research
∙
05/17/2022
Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility
This article studies the infinite-width limit of deep feedforward neural...
research
∙
02/28/2022
LobsDICE: Offline Imitation Learning from Observation via Stationary Distribution Correction Estimation
We consider the problem of imitation from observation (IfO), in which th...
research
∙
07/03/2021
Scale Mixtures of Neural Network Gaussian Processes
Recent works have revealed that infinitely-wide feed-forward or recurren...
research
∙
06/18/2021
α-Stable convergence of heavy-tailed infinitely-wide neural networks
We consider infinitely-wide multi-layer perceptrons (MLPs) which are lim...
research
∙
03/01/2021
Meta-Learning an Inference Algorithm for Probabilistic Programs
We present a meta-algorithm for learning a posterior-inference algorithm...
research
∙
10/01/2020
Bayesian Policy Search for Stochastic Domains
AI planning can be cast as inference in probabilistic models, and probab...
research
∙
10/01/2020
Probabilistic Programs with Stochastic Conditioning
We tackle the problem of conditioning probabilistic programs on distribu...
research
∙
06/12/2020
On Correctness of Automatic Differentiation for Non-Differentiable Functions
Differentiation lies at the core of many machine-learning algorithms, an...
research
∙
03/02/2020
Stochastically Differentiable Probabilistic Programs
Probabilistic programs with mixed support (both continuous and discrete ...
research
∙
11/22/2019
Differentiable Algorithm for Marginalising Changepoints
We present an algorithm for marginalising changepoints in time-series mo...
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...
research
∙
07/20/2019
Towards Verified Stochastic Variational Inference for Probabilistic Programs
Probabilistic programming is the idea of writing models from statistics ...
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...
research
∙
12/15/2018
A Generalization of Hierarchical Exchangeability on Trees to Directed Acyclic Graphs
Motivated by problems in Bayesian nonparametrics and probabilistic progr...
research
∙
09/27/2018
An Introduction to Probabilistic Programming
This document is designed to be a first-year graduate-level introduction...
research
∙
06/25/2018
Inference Trees: Adaptive Inference with Exploration
We introduce inference trees (ITs), a new class of inference methods tha...
research
∙
06/01/2018
Reparameterization Gradient for Non-differentiable Models
We present a new algorithm for stochastic variational inference that tar...
research
∙
04/07/2018
Discontinuous Hamiltonian Monte Carlo for Probabilistic Programs
Hamiltonian Monte Carlo (HMC) is the dominant statistical inference algo...
research
∙
02/26/2018
The Beta-Bernoulli process and algebraic effects
In this paper we analyze the Beta-Bernoulli process from Bayesian nonpar...
research
∙
11/09/2017
Denotational validation of higher-order Bayesian inference
We present a modular semantic account of Bayesian inference algorithms f...
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...
research
∙
01/10/2017
A Convenient Category for Higher-Order Probability Theory
Higher-order probabilistic programming languages allow programmers to wr...
research
∙
06/14/2016
Spreadsheet Probabilistic Programming
Spreadsheet workbook contents are simple programs. Because of this, prob...
research
∙
01/19/2016
Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints
We study the semantic foundation of expressive probabilistic programming...
research
∙
01/27/2015
