Search Results for author: Matteo Russo

Found 2 papers, 1 papers with code

Fully Dynamic Online Selection through Online Contention Resolution Schemes

no code implementations8 Jan 2023 Vashist Avadhanula, Andrea Celli, Riccardo Colini-Baldeschi, Stefano Leonardi, Matteo Russo

A successful approach to online selection problems in the adversarial setting is given by the notion of Online Contention Resolution Scheme (OCRS), that uses a priori information to formulate a linear relaxation of the underlying optimization problem, whose optimal fractional solution is rounded online for any adversarial order of the input sequence.

Poisoning Attacks with Generative Adversarial Nets

1 code implementation18 Jun 2019 Luis Muñoz-González, Bjarne Pfitzner, Matteo Russo, Javier Carnerero-Cano, Emil C. Lupu

In this paper we introduce a novel generative model to craft systematic poisoning attacks against machine learning classifiers generating adversarial training examples, i. e. samples that look like genuine data points but that degrade the classifier's accuracy when used for training.

BIG-bench Machine Learning Data Poisoning

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