3 papers
stat.ML2025
High-dimensional Analysis of Synthetic Data Selection
Parham Rezaei, Filip Kovacevic, Francesco Locatello +1
Despite the progress in the development of generative models, their usefulness in creating synthetic data that improve prediction performance of classifiers has been put into quest…
stat.ML2025
Learning Pareto manifolds in high dimensions: How can regularization help?
Tobias Wegel, Filip Kovačević, Alexandru Ţifrea +1
Simultaneously addressing multiple objectives is becoming increasingly important in modern machine learning. At the same time, data is often high-dimensional and costly to label. F…
stat.ML2025
Spectral Estimators for Multi-Index Models: Precise Asymptotics and Optimal Weak Recovery
Filip Kovačević, Yihan Zhang, Marco Mondelli
Multi-index models provide a popular framework to investigate the learnability of functions with low-dimensional structure and, also due to their connections with neural networks,…