Author name cluster

Francois Buet-Golfouse

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

ECAI Conference 2024 Conference Paper

A Comprehensive Sustainable Framework for Machine Learning and Artificial Intelligence

Roberto Pagliari
Peter Hill
Po-Yu Chen
Maciej Dabrowny
Tingsheng Tan
Francois Buet-Golfouse

In many applications, regulations or best practices often lead to specific requirements in machine learning relating to four key pillars: fairness, privacy, interpretability and greenhouse gas emissions. These all sit in the broader context of sustainability in AI, an emerging practical AI topic. However, although these pillars have been individually addressed by past literature, none of these works have considered all the pillars. There are inherent trade-offs between each of the pillars (for example, utility vs fairness or utility vs privacy), making it even more important to consider them together. This paper outlines a new framework for Sustainable Machine Learning. It proposes FPIG, a general AI pipeline that allows for simultaneous consideration and a better understanding of the tradeoffs between the pillars. Based on the FPIG framework, we propose a meta-learning algorithm to estimate the four key pillars given a dataset summary, model architecture, and hyperparameters before model training. This algorithm allows users to select the optimal model architecture for a given dataset and a set of user requirements on the pillars. We illustrate the trade-offs under the FPIG model on three classical datasets and demonstrate the meta-learning approach with an example of real-world datasets and models with different interpretability, showcasing how it can aid model selection.

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AAAI Conference 2023 Short Paper

Optimal Execution via Multi-Objective Multi-Armed Bandits (Student Abstract)

Francois Buet-Golfouse
Peter Hill

When trying to liquidate a large quantity of a particular stock, the price of that stock is likely to be affected by trades, thus leading to a reduced expected return if we were to sell the entire quantity at once. This leads to the problem of optimal execution, where the aim is to split the sell order into several smaller sell orders over the course of a period of time, to optimally balance stock price with market risk. This problem can be defined in terms of difference equations. Here, we show how we can reformulate this as a multi-objective problem, which we solve with a novel multi-armed bandit algorithm.

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AAAI Conference 2022 Short Paper

Numerical Approximations of Log Gaussian Cox Process (Student Abstract)

Francois Buet-Golfouse
Hans Roggeman

This paper considers a multi-state Log Gaussian Cox Process (“LGCP”) on a graph, where transmissions amongst states are calibrated using a non-parametric approach. We thus consider multi-output LGCPs and introduce numerical approximations to compute posterior distributions extremely quickly and in a completely transparent and reproducible fashion. The model is tested on historical data and shows very good performance.

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ICML Conference 2021 Conference Paper

Narrow Margins: Classification, Margins and Fat Tails

Francois Buet-Golfouse

It is well-known that, for separable data, the regularised two-class logistic regression or support vector machine re-normalised estimate converges to the maximal margin classifier as the regularisation hyper-parameter $\lambda$ goes to 0. The fact that different loss functions may lead to the same solution is of theoretical and practical relevance as margin maximisation allows more straightforward considerations in terms of generalisation and geometric interpretation. We investigate the case where this convergence property is not guaranteed to hold and show that it can be fully characterised by the distribution of error terms in the latent variable interpretation of linear classifiers. In particular, if errors follow a regularly varying distribution, then the regularised and re-normalised estimate does not converge to the maximal margin classifier. This shows that classification with fat tails has a qualitatively different behaviour, which should be taken into account when considering real-life data.

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