14 citations · 46 across the 10 of their papers we have counts for
5 papers · 1 filter
Statistically Efficient Bayesian Sequential Experiment Design via Reinforcement Learning with Cross-Entropy Estimators
Tom Blau, Iadine Chades, Amir Dezfouli +2
Reinforcement learning can learn amortised design policies for designing sequences of experiments. However, current amortised methods rely on estimators of expected information gai…
Transformed Distribution Matching for Missing Value Imputation
He Zhao, Ke Sun, Amir Dezfouli +1
We study the problem of imputing missing values in a dataset, which has important applications in many domains. The key to missing value imputation is to capture the data distribut…
Optimizing Sequential Experimental Design with Deep Reinforcement Learning
Tom Blau, Edwin V. Bonilla, Iadine Chades +1
Bayesian approaches developed to solve the optimal design of sequential experiments are mathematically elegant but computationally challenging. Recently, techniques using amortizat…
TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning
Minchao Wu, Michael Norrish, Christian Walder +1
We propose a novel approach to interactive theorem-proving (ITP) using deep reinforcement learning. The proposed framework is able to learn proof search strategies as well as tacti…
Semi-parametric Network Structure Discovery Models
Amir Dezfouli, Edwin V. Bonilla, Richard Nock
We propose a network structure discovery model for continuous observations that generalizes linear causal models by incorporating a Gaussian process (GP) prior on a network-indepen…