Louis, Ard A.

8 publications

ICLR 2026 Closed-Form $\ell_r$ Norm Scaling with Data for Overparameterized Linear Regression and Diagonal Linear Networks Under $\ell_p$ Bias Shuofeng Zhang, Ard A. Louis

ICLR 2026 Decoupling Dynamical Richness from Representation Learning: Towards Practical Measurement Yoonsoo Nam, Nayara Fonseca, Seok Hyeong Lee, Chris Mingard, Niclas Alexander Göring, Ouns El Harzli, Abdurrahman Hadi Erturk, Soufiane Hayou, Ard A. Louis

NeurIPSW 2024 Algorithmic Stability of Minimum-Norm Interpolating Deep Neural Networks Ouns El Harzli, Yoonsoo Nam, Ilja Kuzborskij, Bernardo Cuenca Grau, Ard A. Louis

ICMLW 2024 An Exactly Solvable Model for Emergence and Scaling Laws Yoonsoo Nam, Nayara Fonseca, Seok Hyeong Lee, Chris Mingard, Ard A. Louis

NeurIPS 2024 An Exactly Solvable Model for Emergence and Scaling Laws in the Multitask Sparse Parity Problem Yoonsoo Nam, Nayara Fonseca, Seok Hyeong Lee, Chris Mingard, Ard A. Louis

AAAI 2024 Double-Descent Curves in Neural Networks: A New Perspective Using Gaussian Processes Ouns El Harzli, Bernardo Cuenca Grau, Guillermo Valle Pérez, Ard A. Louis

JMLR 2021 Is SGD a Bayesian Sampler? Well, Almost Chris Mingard, Guillermo Valle-Pérez, Joar Skalse, Ard A. Louis

ICLR 2019 Deep Learning Generalizes Because the Parameter-Function mAP Is Biased Towards Simple Functions Guillermo Valle-Perez, Chico Q. Camargo, Ard A. Louis