71 citations · 604 across the 67 of their papers we have counts for
7 papers · 1 filter
Disentangling Disentanglement in Variational Autoencoders
Emile Mathieu, Tom Rainforth, N. Siddharth +1
We develop a generalisation of disentanglement in VAEs---decomposition of the latent representation---characterising it as the fulfilment of two factors: a) the latent encodings of…
A Statistical Approach to Assessing Neural Network Robustness
Stefan Webb, Tom Rainforth, Yee Whye Teh +1
We present a new approach to assessing the robustness of neural networks based on estimating the proportion of inputs for which a property is violated. Specifically, we estimate th…
On Exploration, Exploitation and Learning in Adaptive Importance Sampling
Xiaoyu Lu, Tom Rainforth, Yuan Zhou +2
We study adaptive importance sampling (AIS) as an online learning problem and argue for the importance of the trade-off between exploration and exploitation in this adaptation. Bor…
Inference Trees: Adaptive Inference with Exploration
Tom Rainforth, Yuan Zhou, Xiaoyu Lu +4
We introduce inference trees (ITs), a new class of inference methods that build on ideas from Monte Carlo tree search to perform adaptive sampling in a manner that balances explora…
Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities
Bradley Gram-Hansen, Yuan Zhou, Tobias Kohn +3
Hamiltonian Monte Carlo (HMC) is arguably the dominant statistical inference algorithm used in most popular "first-order differentiable" Probabilistic Programming Languages (PPLs).…
Nesting Probabilistic Programs
Tom Rainforth
We formalize the notion of nesting probabilistic programming queries and investigate the resulting statistical implications. We demonstrate that while query nesting allows the defi…