Python code for the GP-RC algorithm presented in "Genetic Programming with Rademacher Complexity for Symbolic Regression" (CEC-2019). Paper Link: https://ieeexplore.ieee.org/document/8790341
Lecture notes taken in the Quantitative Foundations of Artificial Intelligence class in Fall 2023, taught by Prof. Dr. Ludger Overbeck at Justus Liebig University Giessen.
An implementation of Giuseppe Paleologo's Rademacher Antiserum, designed to assess strategy performance consistency through Rademacher complexity and RAS-adjusted Sharpe Ratios. This code evaluates strategy robustness by applying Rademacher random vectors for anti-overfitting analysis.
Codes and experiments for the paper "Learning Vector-valued Functions with Local Rademacher Complexity". Preprint.
Codes and experiments for "Multi-Class Learning using Unlabeled Samples: Theory and Algorithm", published in IJCAI 2019
Repository dealing with key concepts in Machine Learning
Codes and experiments for "Multi-Class Learning: From Theory to Algorithm", published in NeurIPS 2018
Code for the paper "Bavarian: Betweenness Centrality Approximation with Variance-Aware Rademacher Averages", by Chloe Wohlgemuth, Cyrus Cousins, and Matteo Riondato, appearing in ACM KDD'21 and ACM TKDD'23
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