2 citations · 3 across the 9 of their papers we have counts for
8 papers · 1 filter
Score the Algebra, Not the Span: Dimension Reduction for Transfer Operator Models of Dynamical Systems
Mark Kozdoba, Shie Mannor
Dimension reduction for dynamical systems is standard practice, and the standard route is spectral: model the transfer (Koopman) operator by its leading modes. We show that on syst…
How Deep Are Deep GPs, Really? A Sharp Threshold and a Non-Gaussian Limit for Compositional GPs
Mark Kozdoba, Shie Mannor
Compositional priors describe the generic properties of layered functions in deep Bayesian models, where deep neural networks with random weights are a canonical example.In the wid…
Intersectional Fairness via Mixed-Integer Optimization
Jiří Němeček, Mark Kozdoba, Illia Kryvoviaz +2
The deployment of Artificial Intelligence in high-risk domains, such as finance and healthcare, necessitates models that are both fair and transparent. While regulatory frameworks,…
Representative Action Selection for Large Action Space Bandit Families
Quan Zhou, Mark Kozdoba, Shie Mannor
We study the problem of selecting a subset from a large action space shared by a family of bandits. In many natural situations, while the nominal set of actions is large, actions a…
Bias Detection via Maximum Subgroup Discrepancy
Jiří Němeček, Mark Kozdoba, Illia Kryvoviaz +2
Bias evaluation is fundamental to trustworthy AI, both in terms of checking data quality and in terms of checking the outputs of AI systems. In testing data quality, for example, o…
Dimension Free Generalization Bounds for Non Linear Metric Learning
Mark Kozdoba, Shie Mannor
In this work we study generalization guarantees for the metric learning problem, where the metric is induced by a neural network type embedding of the data. Specifically, we provid…