7 citations · 31 across the 7 of their papers we have counts for
7 papers
Additive Gaussian Processes Revisited
Xiaoyu Lu, Alexis Boukouvalas, James Hensman
Gaussian Process (GP) models are a class of flexible non-parametric models that have rich representational power. By using a Gaussian process with additive structure, complex respo…
Structure Mapping for Transferability of Causal Models
Purva Pruthi, Javier González, Xiaoyu Lu +1
Human beings learn causal models and constantly use them to transfer knowledge between similar environments. We use this intuition to design a transfer-learning framework using obj…
Causal Bayesian Optimization
Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes +1
This paper studies the problem of globally optimizing a variable of interest that is part of a causal model in which a sequence of interventions can be performed. This problem aris…
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…
Relativistic Monte Carlo
Xiaoyu Lu, Valerio Perrone, Leonard Hasenclever +2
Hamiltonian Monte Carlo (HMC) is a popular Markov chain Monte Carlo (MCMC) algorithm that generates proposals for a Metropolis-Hastings algorithm by simulating the dynamics of a Ha…