Mariia Vladimirova

Scalable Fair Learning via Cramér-von Mises Regularization

Albert Gimó, Mariia Vladimirova, Olga Petrova, Reda Chhaibi et autres

A standard way to enforce group fairness in machine learning models is to add a fairness regularizer to the training loss. Existing dependence-based regularizers, however, are often computationally expensive, with per-batch costs that are typically quadratic or higher in the batch size …

Handbook of Bayesian Deep Learning

Claudio Agostinelli, Laurence Aitchison, Emanuel Aldea, Richard Allmendinger et autres

International audience

it, gb, fr, us, hk, de, ch, sa, dk, no, cn, nl, ca, au (code pays fourni par la source)

Handbook of Bayesian Deep Learning

Claudio Agostinelli, Laurence Aitchison, Emanuel Aldea, Richard Allmendinger et autres

it, gb, fr, us, hk, de, ch, sa, dk, no, cn, nl, ca, au (code pays fourni par la source)

A Primer on Bayesian Neural Networks: Review and Debates

Julyan Arbel, Konstantinos Pitas, Mariia Vladimirova, Vincent Fortuin

Neural networks have achieved remarkable performance across various problem domains, but their widespread applicability is hindered by inherent limitations such as overconfidence in predictions, lack of interpretability and vulnerability to adversarial attacks. To address these challenges, Bayesian neural networks (BNNs) have emerged …

fr, de (code pays fourni par la source)

CUVET: A Partitioning Approach for Continuous Treatment Assignment At Scale

Artem Betlei, Mariia Vladimirova, Victor Girou, Thibaud Rahier

International audience

Fairness Through Independence via Cramér-von Mises Regularization

Albert Gimó Contreras, Mariia Vladimirova, Olga Petrova, Federico Pavone et autres

International audience

Fairness in Generative AI is Understudied, Underachieved, Undervalued

Mariia Vladimirova, Jean-Yves Franceschi, Thibaut Issenhuth

Despite groundbreaking advancements in generative models during the last decade, concerns about their fairness remain underexplored. Behind their impressive capabilities, these models perpetuate and amplify biases present in their training data, reinforcing societal inequalities and harming marginalized groups. Yet, fairness in generative …

fr (code pays fourni par la source)

FairJob: A Real-World Dataset for Fairness in Online Systems

Mariia Vladimirova, Federico Pavone, Eustache Diemert

We introduce a fairness-aware dataset for job recommendations in advertising, designed to foster research in algorithmic fairness within real-world scenarios. It was collected and prepared to comply with privacy standards and business confidentiality. An additional challenge is the lack of access to …

fr (code pays fourni par la source)

Maximizing the Success Probability of Policy Allocations in Online Systems

Artem Betlei, Mariia Vladimirova, Mehdi Sebbar, Nicolas Urien et autres

The effectiveness of advertising in e-commerce largely depends on the ability of merchants to bid on and win impressions for their targeted users. The bidding procedure is highly complex due to various factors such as market competition, user behavior, and the diverse …

fr (code pays fourni par la source)

FairJob: A Real-World Dataset for Fairness in Online Systems

Mariia Vladimirova, Eustache Diemert, Federico Pavone

Maximizing the Success Probability of Policy Allocations in Online Systems

Artem Betlei, Mariia Vladimirova, Mehdi Sebbar, Nicolas Urien et autres

The effectiveness of advertising in e-commerce largely depends on the ability of merchants to bid on and win impressions for their targeted users. The bidding procedure is highly complex due to various factors such as market competition, user behavior, and the diverse …

fr (code pays fourni par la source)

Bayes in action in deep learning and dictionary learning

Julyan Arbel, Hong-Phuong Dang, Clément Elvira, Cédric Herzet et autres

This article summarizes some recent works and associated challenges in the field of Bayesian statistics that were presented during the Journées MAS 2020. The goal of the session was to give an overview of the many aspects of Bayesian statistics investigated by …

fr (code pays fourni par la source)