4 papers
Towards a holistic understanding of Selection Bias for Causal Effect Identification
Yiwen Qiu, Filip KovaÄeviÄ, Shimeng Huang +2
Selection bias is pervasive in observational studies. For example, large scale biobanks data can exhibit ``healthy volunteer bias'' when respondents are healthier and of higher soc…
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…
Spectral Estimators for Multi-Index Models: Precise Asymptotics and Optimal Weak Recovery
Filip KovaÄeviÄ, 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,…
Learning Pareto manifolds in high dimensions: How can regularization help?
Tobias Wegel, Filip KovaÄeviÄ, Alexandru Å¢ifrea +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…