36 citations · 75 across the 7 of their papers we have counts for
8 papers
Position: Fairness Failure in Generative Models is an Evaluation Problem
Mariia Vladimirova, Jean-Yves Franceschi, Thibaut Issenhuth
Despite groundbreaking advancements in generative models during the last decade, concerns about their lack of fairness, reinforcing societal inequalities and harming marginalized g…
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…
Maximizing the Success Probability of Policy Allocations in Online Systems
Artem Betlei, Mariia Vladimirova, Mehdi Sebbar +3
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 c…
A Primer on Bayesian Neural Networks: Review and Debates
Julyan Arbel, Konstantinos Pitas, Mariia Vladimirova +1
Neural networks have achieved remarkable performance across various problem domains, but their widespread applicability is hindered by inherent limitations such as overconfidence i…
Dependence between Bayesian neural network units
Mariia Vladimirova, Julyan Arbel, Stéphane Girard
The connection between Bayesian neural networks and Gaussian processes gained a lot of attention in the last few years, with the flagship result that hidden units converge to a Gau…
Bayesian neural network unit priors and generalized Weibull-tail property
Mariia Vladimirova, Julyan Arbel, Stéphane Girard
The connection between Bayesian neural networks and Gaussian processes gained a lot of attention in the last few years. Hidden units are proven to follow a Gaussian process limit w…
Sub-Weibull distributions: generalizing sub-Gaussian and sub-Exponential properties to heavier-tailed distributions
Mariia Vladimirova, Stephane Girard, Hien Nguyen +1
We propose the notion of sub-Weibull distributions, which are characterised by tails lighter than (or equally light as) the right tail of a Weibull distribution. This novel class g…
Understanding Priors in Bayesian Neural Networks at the Unit Level
Mariia Vladimirova, Jakob Verbeek, Pablo Mesejo +1
We investigate deep Bayesian neural networks with Gaussian weight priors and a class of ReLU-like nonlinearities. Bayesian neural networks with Gaussian priors are well known to in…