14 citations · 46 across the 10 of their papers we have counts for
10 papers
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…
The Contextual Lasso: Sparse Linear Models via Deep Neural Networks
Ryan Thompson, Amir Dezfouli, Robert Kohn
Sparse linear models are one of several core tools for interpretable machine learning, a field of emerging importance as predictive models permeate decision-making in many domains.…
Bayesian Optimisation for Mixed-Variable Inputs using Value Proposals
Yan Zuo, Amir Dezfouli, Iadine Chades +2
Many real-world optimisation problems are defined over both categorical and continuous variables, yet efficient optimisation methods such asBayesian Optimisation (BO) are not desig…
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…
EAPS: Edge-Assisted Predictive Sleep Scheduling for 802.11 IoT Stations
Jaykumar Sheth, Cyrus Miremadi, Amir Dezfouli +1
The broad deployment of 802.11 (a.k.a., WiFi) access points and significant enhancement of the energy efficiency of these wireless transceivers has resulted in increasing interest…
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…
Gray-box inference for structured Gaussian process models
Pietro Galliani, Amir Dezfouli, Edwin V. Bonilla +1
We develop an automated variational inference method for Bayesian structured prediction problems with Gaussian process (GP) priors and linear-chain likelihoods. Our approach does n…
Generic Inference in Latent Gaussian Process Models
Edwin V. Bonilla, Karl Krauth, Amir Dezfouli
We develop an automated variational method for inference in models with Gaussian process (GP) priors and general likelihoods. The method supports multiple outputs and multiple late…