Search Results for author: Patrick Loiseau

Found 8 papers, 5 papers with code

Scalable Optimal Classifiers for Adversarial Settings under Uncertainty

no code implementations28 Jun 2021 Patrick Loiseau, Benjamin Roussillon

We consider the problem of finding optimal classifiers in an adversarial setting where the class-1 data is generated by an attacker whose objective is not known to the defender -- an aspect that is key to realistic applications but has so far been overlooked in the literature.

On Fair Selection in the Presence of Implicit Variance

no code implementations24 Jun 2020 Vitalii Emelianov, Nicolas Gast, Krishna P. Gummadi, Patrick Loiseau

We then compare the utility obtained by imposing a fairness mechanism that we term $\gamma$-rule (it includes demographic parity and the four-fifths rule as special cases), to that of a group-oblivious selection algorithm that picks the candidates with the highest estimated quality independently of their group.

Fairness

Path Planning Problems with Side Observations-When Colonels Play Hide-and-Seek

no code implementations19 Nov 2019 Dong Quan Vu, Patrick Loiseau, Alonso Silva, Long Tran-Thanh

Resource allocation games such as the famous Colonel Blotto (CB) and Hide-and-Seek (HS) games are often used to model a large variety of practical problems, but only in their one-shot versions.

Computer Science and Game Theory

Nonzero-sum Adversarial Hypothesis Testing Games

1 code implementation NeurIPS 2019 Sarath Yasodharan, Patrick Loiseau

We study nonzero-sum hypothesis testing games that arise in the context of adversarial classification, in both the Bayesian as well as the Neyman-Pearson frameworks.

Classification General Classification +1

The Price of Local Fairness in Multistage Selection

1 code implementation15 Jun 2019 Vitalii Emelianov, George Arvanitakis, Nicolas Gast, Krishna Gummadi, Patrick Loiseau

In particular, our experiments show that the price of local fairness is generally smaller when the sensitive attribute is observed at the first stage; but globally fair selections are more locally fair when the sensitive attribute is observed at the second stage---hence in both cases it is often possible to have a selection that has a small price of local fairness and is close to locally fair.

Decision Making Fairness

Path Planning Problems with Side Observations-When Colonels Play Hide-and-Seek

2 code implementations27 May 2019 Dong Quan Vu, Patrick Loiseau, Alonso Silva, Long Tran-Thanh

Resource allocation games such as the famous Colonel Blotto (CB) and Hide-and-Seek (HS) games are often used to model a large variety of practical problems, but only in their one-shot versions.

An Approximate Dynamic Programming Approach to Adversarial Online Learning

1 code implementation16 Mar 2016 Vijay Kamble, Patrick Loiseau, Jean Walrand

We describe an approximate dynamic programming (ADP) approach to compute approximations of the optimal strategies and of the minimal losses that can be guaranteed in discounted repeated games with vector-valued losses.

Decision Making

Linear Regression from Strategic Data Sources

1 code implementation30 Sep 2013 Nicolas Gast, Stratis Ioannidis, Patrick Loiseau, Benjamin Roussillon

In this paper, we study a setting in which features are public but individuals choose the precision of the outputs they reveal to an analyst.

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