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Found 6 papers, 0 papers with code
Data-Driven Reachability analysis and Support set Estimation with Christoffel Functions
In addition to applying classical Vapnik-Chervonenkis (VC) dimension bound arguments, we apply the PAC-Bayes theorem by leveraging a formal connection between kernelized empirical inverse Christoffel functions and Gaussian process regression models.
Data-Driven Reachability Analysis with Christoffel Functions
We present an algorithm for data-driven reachability analysis that estimates finite-horizon forward reachable sets for general nonlinear systems using level sets of a certain class of polynomials known as Christoffel functions.
Fairness with Overlapping Groups; a Probabilistic Perspective
In algorithmically fair prediction problems, a standard goal is to ensure the equality of fairness metrics across multiple overlapping groups simultaneously.
Fairness with Overlapping Groups
In algorithmically fair prediction problems, a standard goal is to ensure the equality of fairness metrics across multiple overlapping groups simultaneously.
On the Consistency of Top-k Surrogate Losses
Our analysis continues by showing previously proposed hinge-like top-$k$ surrogate losses are not top-$k$ calibrated and suggests no convex hinge loss is top-$k$ calibrated.
Kernel-based Outlier Detection using the Inverse Christoffel Function
Outlier detection methods have become increasingly relevant in recent years due to increased security concerns and because of its vast application to different fields.
