11 citations · 13 across the 4 of their papers we have counts for
5 papers
Q-learning with online random forests
Joosung Min, Lloyd T. Elliott
-learning is the most fundamental model-free reinforcement learning algorithm. Deployment of -learning requires approximation of the state-action value function (also known a…
Shape Modeling with Spline Partitions
Shufei Ge, Shijia Wang, Lloyd Elliott
Shape modelling (with methods that output shapes) is a new and important task in Bayesian nonparametrics and bioinformatics. In this work, we focus on Bayesian nonparametric method…
Random Tessellation Forests
Shufei Ge, Shijia Wang, Yee Whye Teh +2
Space partitioning methods such as random forests and the Mondrian process are powerful machine learning methods for multi-dimensional and relational data, and are based on recursi…
A nonparametric HMM for genetic imputation and coalescent inference
Lloyd T. Elliott, Yee Whye Teh
Genetic sequence data are well described by hidden Markov models (HMMs) in which latent states correspond to clusters of similar mutation patterns. Theory from statistical genetics…
Driving Markov chain Monte Carlo with a dependent random stream
Iain Murray, Lloyd T. Elliott
Markov chain Monte Carlo is a widely-used technique for generating a dependent sequence of samples from complex distributions. Conventionally, these methods require a source of ind…