1 citations · 1 across the 4 of their papers we have counts for
4 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…
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…
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 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…