40 citations · 45 across the 5 of their papers we have counts for
6 papers
How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies
Edward Small, Wei Shao, Zeliang Zhang +4
With the introduction of machine learning in high-stakes decision making, ensuring algorithmic fairness has become an increasingly important problem to solve. In response to this,…
Sample-Efficient, Exploration-Based Policy Optimisation for Routing Problems
Nasrin Sultana, Jeffrey Chan, Tabinda Sarwar +1
Model-free deep-reinforcement-based learning algorithms have been applied to a range of COPs~\cite{bello2016neural}~\cite{kool2018attention}~\cite{nazari2018reinforcement}. However…
Learning Enhanced Optimisation for Routing Problems
Nasrin Sultana, Jeffrey Chan, Tabinda Sarwar +2
Deep learning approaches have shown promising results in solving routing problems. However, there is still a substantial gap in solution quality between machine learning and operat…
Parallelizing Contextual Bandits
Jeffrey Chan, Aldo Pacchiano, Nilesh Tripuraneni +3
Standard approaches to decision-making under uncertainty focus on sequential exploration of the space of decisions. However, \textit{simultaneously} proposing a batch of decisions,…
A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks
Jeffrey Chan, Valerio Perrone, Jeffrey P. Spence +3
An explosion of high-throughput DNA sequencing in the past decade has led to a surge of interest in population-scale inference with whole-genome data. Recent work in population gen…
Two-Locus Likelihoods under Variable Population Size and Fine-Scale Recombination Rate Estimation
John A. Kamm, Jeffrey P. Spence, Jeffrey Chan +1
Two-locus sampling probabilities have played a central role in devising an efficient composite likelihood method for estimating fine-scale recombination rates. Due to mathematical…