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ORCIDFrancis Bach
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2020 – today
- 2024
[j1]Francis Bach:
High-Dimensional Analysis of Double Descent for Linear Regression with Random Projections. SIAM J. Math. Data Sci. 6(1): 26-50 (2024)- [c4]Vivien A Cabannnes, Francis Bach:
The Galerkin method beats Graph-Based Approaches for Spectral Algorithms. AISTATS 2024: 451-459 - [c3]Simon Martin, Francis Bach, Giulio Biroli:
On the Impact of Overparameterization on the Training of a Shallow Neural Network in High Dimensions. AISTATS 2024: 3655-3663 - [c2]Nathan Doumèche, Francis Bach, Gérard Biau, Claire Boyer:
Physics-informed machine learning as a kernel method. COLT 2024: 1399-1450 - [i6]Nathan Doumèche, Francis Bach, Claire Boyer, Gérard Biau:
Physics-informed machine learning as a kernel method. CoRR abs/2402.07514 (2024) - [i5]Bertille Follain, Francis Bach:
Enhanced Feature Learning via Regularisation: Integrating Neural Networks and Kernel Methods. CoRR abs/2407.17280 (2024) - [i4]Clémentine Chazal, Anna Korba, Francis Bach:
Statistical and Geometrical properties of regularized Kernel Kullback-Leibler divergence. CoRR abs/2408.16543 (2024) - [i3]Nathan Doumèche, Francis Bach, Gérard Biau, Claire Boyer:
Physics-informed kernel learning. CoRR abs/2409.13786 (2024) - 2023
- [c1]Antônio H. Ribeiro, Dave Zachariah, Francis Bach, Thomas B. Schön:
Regularization properties of adversarially-trained linear regression. NeurIPS 2023 - [i2]Bertille Follain, Umut Simsekli, Francis Bach:
Nonparametric Linear Feature Learning in Regression Through Regularisation. CoRR abs/2307.12754 (2023) - [i1]Antônio H. Ribeiro, Dave Zachariah, Francis Bach, Thomas B. Schön:
Regularization properties of adversarially-trained linear regression. CoRR abs/2310.10807 (2023)
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