1 citations · 1 across the 7 of their papers we have counts for
7 papers
Stochastic Penalty-Barrier Method for Constrained Machine Learning
Adam Bosák, Andrii Kliachkin, Jana Lepšová +4
Constrained Machine Learning (CML) enables fairness-aware training, physics-informed neural networks, and integration of symbolic domain knowledge into statistical models. In this…
EVEREST: An Evidential, Tail-Aware Transformer for Rare-Event Time-Series Forecasting
Antanas Zilinskas, Robert N. Shorten, Jakub Marecek
Forecasting rare events in multivariate time-series data is challenging due to severe class imbalance, long-range dependencies, and distributional uncertainty. We introduce EVEREST…
humancompatible.train: Implementing Optimization Algorithms for Stochastically-Constrained Stochastic Optimization Problems
Andrii Kliachkin, Jana Lepšová, Gilles Bareilles +1
There has been a considerable interest in constrained training of deep neural networks (DNNs) recently for applications such as fairness and safety. Several toolkits have been prop…
humancompatible.detect: a Python Toolkit for Detecting Bias in AI Models
German M. Matilla, Jiri Nemecek, Illia Kryvoviaz +1
There is a strong recent emphasis on trustworthy AI. In particular, international regulations, such as the AI Act, demand that AI practitioners measure data quality on the input an…
Benchmarking Stochastic Approximation Algorithms for Fairness-Constrained Training of Deep Neural Networks
Andrii Kliachkin, Jana Lepšová, Gilles Bareilles +1
The ability to train Deep Neural Networks (DNNs) with constraints is instrumental in improving the fairness of modern machine-learning models. Many algorithms have been analysed in…
Fairness in AI: challenges in bridging the gap between algorithms and law
Giorgos Giannopoulos, Maria Psalla, Loukas Kavouras +4
In this paper we examine algorithmic fairness from the perspective of law aiming to identify best practices and strategies for the specification and adoption of fairness definition…
Fairness in Ranking: Robustness through Randomization without the Protected Attribute
Andrii Kliachkin, Eleni Psaroudaki, Jakub Marecek +1
There has been great interest in fairness in machine learning, especially in relation to classification problems. In ranking-related problems, such as in online advertising, recomm…