Datasets often have their intrinsic symmetries, and particular deep-lear...
We consider the optimisation of large and shallow neural networks via
gr...
We present a static analysis for discovering differentiable or more gene...
SATNet is a differentiable constraint solver with a custom backpropagati...
This article studies the infinite-width limit of deep feedforward neural...
We consider the problem of imitation from observation (IfO), in which th...
Recent works have revealed that infinitely-wide feed-forward or recurren...
We consider infinitely-wide multi-layer perceptrons (MLPs) which are lim...
We present a meta-algorithm for learning a posterior-inference algorithm...
AI planning can be cast as inference in probabilistic models, and
probab...
We tackle the problem of conditioning probabilistic programs on distribu...
Differentiation lies at the core of many machine-learning algorithms, an...
Probabilistic programs with mixed support (both continuous and discrete
...
We present an algorithm for marginalising changepoints in time-series mo...
Universal probabilistic programming systems (PPSs) provide a powerful an...
Probabilistic programming is the idea of writing models from statistics ...
We develop a new Low-level, First-order Probabilistic Programming Langua...
Motivated by problems in Bayesian nonparametrics and probabilistic
progr...
This document is designed to be a first-year graduate-level introduction...
We introduce inference trees (ITs), a new class of inference methods tha...
We present a new algorithm for stochastic variational inference that tar...
Hamiltonian Monte Carlo (HMC) is the dominant statistical inference algo...
In this paper we analyze the Beta-Bernoulli process from Bayesian
nonpar...
We present a modular semantic account of Bayesian inference algorithms f...
We present a formalization of nested Monte Carlo (NMC) estimation, where...
Higher-order probabilistic programming languages allow programmers to wr...
Spreadsheet workbook contents are simple programs. Because of this,
prob...
We study the semantic foundation of expressive probabilistic programming...
Particle Markov chain Monte Carlo techniques rank among current
state-of...