Faking feature importance: A cautionary tale on the use of differentially-private synthetic data

no code implementations • 2 Mar 2022 • Oscar Giles, Kasra Hosseini, Grigorios Mingas, Oliver Strickson, Louise Bowler, Camila Rangel Smith, Harrison Wilde, Jen Ning Lim, Bilal Mateen, Kasun Amarasinghe, Rayid Ghani, Alison Heppenstall, Nik Lomax, Nick Malleson, Martin O'Reilly, Sebastian Vollmerteke

Synthetic datasets are often presented as a silver-bullet solution to the problem of privacy-preserving data publishing.

Feature Importance Privacy Preserving

A Recommendation and Risk Classification System for Connecting Rough Sleepers to Essential Outreach Services

1 code implementation • 30 Jul 2020 • Harrison Wilde, Lucia Lushi Chen, Austin Nguyen, Zoe Kimpel, Joshua Sidgwick, Adolfo De Unanue, Davide Veronese, Bilal Mateen, Rayid Ghani, Sebastian Vollmer

Rough sleeping is a chronic problem faced by some of the most disadvantaged people in modern society.

Ethics General Classification

NIPS - Not Even Wrong? A Systematic Review of Empirically Complete Demonstrations of Algorithmic Effectiveness in the Machine Learning and Artificial Intelligence Literature

no code implementations • 18 Dec 2018 • Franz J. Király, Bilal Mateen, Raphael Sonabend

Objective: To determine the completeness of argumentative steps necessary to conclude effectiveness of an algorithm in a sample of current ML/AI supervised learning literature.

Probabilistic supervised learning

no code implementations • 2 Jan 2018 • Frithjof Gressmann, Franz J. Király, Bilal Mateen, Harald Oberhauser

Predictive modelling and supervised learning are central to modern data science.

BIG-bench Machine Learning Decision Making