A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression. - Tin Sum Cheng; Aurélien Lucchi; Anastasis Kratsios; Ivan Dokmanic; David Belius (NeurIPS, 2023)

Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum. - Tin Sum Cheng; Aurélien Lucchi; Anastasis Kratsios; David Belius (ICML, 2024)

A Comprehensive Analysis on the Learning Curve in Kernel Ridge Regression. - Tin Sum Cheng; Aurélien Lucchi; Anastasis Kratsios; David Belius (NeurIPS, 2024)

Unpacking Softmax: How Temperature Drives Representation Collapse, Compression, and Generalization. - Wojciech Masarczyk; Mateusz Ostaszewski; Tin Sum Cheng; Tomasz Trzcinski; Aurélien Lucchi; Razvan Pascanu (CoRR, 2025)

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters. - Anastasis Kratsios; Tin Sum Cheng; Aurélien Lucchi; Haitz Sáez de Ocáriz Borde (CoRR, 2025)