Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks. - Blake Bordelon; Abdulkadir Canatar; Cengiz Pehlevan (ICML, 2020)

Statistical Mechanics of Generalization in Kernel Regression. - Abdulkadir Canatar; Blake Bordelon; Cengiz Pehlevan (CoRR, 2020)

Out-of-Distribution Generalization in Kernel Regression. - Abdulkadir Canatar; Blake Bordelon; Cengiz Pehlevan (NeurIPS, 2021)

Asymptotics of representation learning in finite Bayesian neural networks. - Jacob A. Zavatone-Veth; Abdulkadir Canatar; Benjamin S. Ruben; Cengiz Pehlevan (NeurIPS, 2021)

Asymptotics of representation learning in finite Bayesian neural networks. - Jacob A. Zavatone-Veth; Abdulkadir Canatar; Cengiz Pehlevan (CoRR, 2021)

A Kernel Analysis of Feature Learning in Deep Neural Networks. - Abdulkadir Canatar; Cengiz Pehlevan (Allerton, 2022)

A Spectral Theory of Neural Prediction and Alignment. - Abdulkadir Canatar; Jenelle Feather; Albert J. Wakhloo; SueYeon Chung (NeurIPS, 2023)

Bandwidth Enables Generalization in Quantum Kernel Models. - Abdulkadir Canatar; Evan Peters; Cengiz Pehlevan; Stefan M. Wild; Ruslan Shaydulin (Trans. Mach. Learn. Res., 2023)

Spectral Analysis of Representational Similarity with Limited Neurons. - Hyunmo Kang; Abdulkadir Canatar; SueYeon Chung (CoRR, 2025)

Estimating Dimensionality of Neural Representations from Finite Samples. - Chanwoo Chun; Abdulkadir Canatar; SueYeon Chung; Daniel D. Lee (CoRR, 2025)