Brown, Gavin

32 publications

TMLR 2024 Bias/Variance Is Not the Same as Approximation/Estimation Gavin Brown, Riccardo Ali

COLT 2024 Insufficient Statistics Perturbation: Stable Estimators for Private Least Squares Extended Abstract Gavin Brown, Jonathan Hayase, Samuel Hopkins, Weihao Kong, Xiyang Liu, Sewoong Oh, Juan C Perdomo, Adam Smith

COLT 2024 Metalearning with Very Few Samples per Task Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown, Adam Smith, Nathan Srebro, Jonathan Ullman

JMLR 2023 A Unified Theory of Diversity in Ensemble Learning Danny Wood, Tingting Mu, Andrew M. Webb, Henry W. J. Reeve, Mikel Luján, Gavin Brown

COLT 2023 Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions Gavin Brown, Samuel Hopkins, Adam Smith

AISTATS 2022 Bias-Variance Decompositions for Margin Losses Danny Wood, Tingting Mu, Gavin Brown

AISTATS 2022 Performative Prediction in a Stateful World Gavin Brown, Shlomi Hod, Iden Kalemaj

AAAI 2022 EnnCore: End-to-End Conceptual Guarding of Neural Architectures Edoardo Manino, Danilo S. Carvalho, Yi Dong, Julia Rozanova, Xidan Song, Mustafa A. Mustafa, André Freitas, Gavin Brown, Mikel Luján, Xiaowei Huang, Lucas C. Cordeiro

COLT 2022 Strong Memory Lower Bounds for Learning Natural Models Gavin Brown, Mark Bun, Adam Smith

NeurIPS 2021 Covariance-Aware Private Mean Estimation Without Private Covariance Estimation Gavin Brown, Marco Gaboardi, Adam Smith, Jonathan Ullman, Lydia Zakynthinou

MLJ 2020 Correction to: Efficient Feature Selection Using Shrinkage Estimators Konstantinos Sechidis, Laura Azzimonti, Adam Craig Pocock, Giorgio Corani, James Weatherall, Gavin Brown

ECML-PKDD 2020 To Ensemble or Not Ensemble: When Does End-to-End Training Fail? Andrew M. Webb, Charles Reynolds, Wenlin Chen, Henry W. J. Reeve, Dan-Andrei Iliescu, Mikel Luján, Gavin Brown

MLJ 2019 Efficient Feature Selection Using Shrinkage Estimators Konstantinos Sechidis, Laura Azzimonti, Adam Craig Pocock, Giorgio Corani, James Weatherall, Gavin Brown

ECML-PKDD 2019 On the Stability of Feature Selection in the Presence of Feature Correlations Konstantinos Sechidis, Konstantinos Papangelou, Sarah Nogueira, James Weatherall, Gavin Brown

ECML-PKDD 2018 Modular Dimensionality Reduction Henry W. J. Reeve, Tingting Mu, Gavin Brown

MLJ 2018 Simple Strategies for Semi-Supervised Feature Selection Konstantinos Sechidis, Gavin Brown

ALT 2018 The K-Nearest Neighbour UCB Algorithm for Multi-Armed Bandits with Covariates Henry Reeve, Joe Mellor, Gavin Brown

ECML-PKDD 2018 Toward an Understanding of Adversarial Examples in Clinical Trials Konstantinos Papangelou, Konstantinos Sechidis, James Weatherall, Gavin Brown

ICCVW 2017 Is Deep Learning Safe for Robot Vision? Adversarial Examples Against the iCub Humanoid Marco Melis, Ambra Demontis, Battista Biggio, Gavin Brown, Giorgio Fumera, Fabio Roli

ALT 2017 Minimax Rates for Cost-Sensitive Learning on Manifolds with Approximate Nearest Neighbours Henry W. J. Reeve, Gavin Brown

MLJ 2016 Cost-Sensitive Boosting Algorithms: Do We Really Need Them? Nikolaos Nikolaou, Narayanan Unny Edakunni, Meelis Kull, Peter A. Flach, Gavin Brown

PGM 2016 Estimating Mutual Information in Under-Reported Variables Konstantinos Sechidis, Matthew Sperrin, Emily Petherick, Gavin Brown

ECML-PKDD 2016 Measuring the Stability of Feature Selection Sarah Nogueira, Gavin Brown

ICML 2015 Is Feature Selection Secure Against Training Data Poisoning? Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, Fabio Roli

ECML-PKDD 2015 Markov Blanket Discovery in Positive-Unlabelled and Semi-Supervised Data Konstantinos Sechidis, Gavin Brown

ECML-PKDD 2014 Statistical Hypothesis Testing in Positive Unlabelled Data Konstantinos Sechidis, Borja Calvo, Gavin Brown

JMLR 2013 Beyond Fano's Inequality: Bounds on the Optimal F-Score, BER, and Cost-Sensitive Risk and Their Implications Ming-Jie Zhao, Narayanan Edakunni, Adam Pocock, Gavin Brown

JMLR 2012 Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection Gavin Brown, Adam Pocock, Ming-Jie Zhao, Mikel Luján

AISTATS 2012 Informative Priors for Markov Blanket Discovery Adam Pocock, Mikel Lujan, Gavin Brown

AISTATS 2009 A New Perspective for Information Theoretic Feature Selection Gavin Brown

JMLR 2005 Managing Diversity in Regression Ensembles Gavin Brown, Jeremy L. Wyatt, Peter Tiňo

ICML 2003 The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning Methods Gavin Brown, Jeremy L. Wyatt