Các bài viết có thể truy cập công khai - Francis BachTìm hiểu thêm
Không có ở bất kỳ nơi nào: 1
Optimal Estimation of Smooth Transport Maps with Kernel SoS
A Vacher, B Muzellec, F Bach, FX Vialard, A Rudi
SIAM Journal on Mathematics of Data Science 6 (2), 311-342, 2024
Các cơ quan ủy nhiệm: European Commission, Agence Nationale de la Recherche
Có tại một số nơi: 137
SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives
A Defazio, F Bach, S Lacoste-Julien
Advances in neural information processing systems 27, 1646-1654, 2014
Các cơ quan ủy nhiệm: European Commission
Minimizing finite sums with the stochastic average gradient
M Schmidt, N Le Roux, F Bach
Mathematical Programming 162, 83-112, 2017
Các cơ quan ủy nhiệm: Natural Sciences and Engineering Research Council of Canada, European Commission
On Lazy Training in Differentiable Programming
L Chizat, E Oyallon, F Bach
Advances in Neural Information Processing Systems 32, 2937-2947, 2019
Các cơ quan ủy nhiệm: European Commission
Breaking the curse of dimensionality with convex neural networks
F Bach
Journal of Machine Learning Research 18 (19), 1-53, 2017
Các cơ quan ủy nhiệm: European Commission
On the global convergence of gradient descent for over-parameterized models using optimal transport
L Chizat, F Bach
Advances in neural information processing systems 31, 3036-3046, 2018
Các cơ quan ủy nhiệm: European Commission
Learning with submodular functions: A convex optimization perspective
F Bach
Foundations and Trends® in Machine Learning 6 (2-3), 145-373, 2013
Các cơ quan ủy nhiệm: European Commission
Sparse modeling for image and vision processing
J Mairal, F Bach, J Ponce
Foundations and Trends® in Computer Graphics and Vision 8 (2-3), 85-283, 2014
Các cơ quan ủy nhiệm: European Commission
Stochastic optimization for large-scale optimal transport
A Genevay, M Cuturi, G Peyré, F Bach
Advances in neural information processing systems, 3440-3448, 2016
Các cơ quan ủy nhiệm: European Commission
Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance
J Weed, F Bach
Bernoulli 25 (4A), 2620-2648, 2019
Các cơ quan ủy nhiệm: US National Science Foundation
Implicit bias of gradient descent for wide two-layer neural networks trained with the logistic loss
L Chizat, F Bach
Conference on Learning Theory, 1305-1338, 2020
Các cơ quan ủy nhiệm: European Commission, Agence Nationale de la Recherche
Fast and faster convergence of SGD for over-parameterized models and an accelerated perceptron
S Vaswani, F Bach, M Schmidt
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
Các cơ quan ủy nhiệm: European Commission
On the equivalence between kernel quadrature rules and random feature expansions
F Bach
Journal of Machine Learning Research 18 (21), 1-38, 2017
Các cơ quan ủy nhiệm: European Commission
Learning with differentiable pertubed optimizers
Q Berthet, M Blondel, O Teboul, M Cuturi, JP Vert, F Bach
Advances in neural information processing systems 33, 9508-9519, 2020
Các cơ quan ủy nhiệm: European Commission, Agence Nationale de la Recherche
Nonparametric stochastic approximation with large step-sizes
A Dieuleveut, F Bach
The Annals of Statistics 44 (4), 1363-1399, 2016
Các cơ quan ủy nhiệm: European Commission
Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression
F Bach
The Journal of Machine Learning Research 15 (1), 595-627, 2014
Các cơ quan ủy nhiệm: European Commission
Bridging the gap between constant step size stochastic gradient descent and markov chains
A Dieuleveut, A Durmus, F Bach
Annals of Statistics 48 (3), 1348-1382, 2020
Các cơ quan ủy nhiệm: European Commission
Implicit regularization of discrete gradient dynamics in linear neural networks
G Gidel, F Bach, S Lacoste-Julien
Advances in Neural Information Processing Systems, 3202-3211, 2019
Các cơ quan ủy nhiệm: Natural Sciences and Engineering Research Council of Canada
Low-rank optimization with trace norm penalty
B Mishra, G Meyer, F Bach, R Sepulchre
SIAM Journal on Optimization 23 (4), 2124-2149, 2013
Các cơ quan ủy nhiệm: National Fund for Scientific Research, Belgium
From averaging to acceleration, there is only a step-size
N Flammarion, F Bach
Conference on Learning Theory, 658-695, 2015
Các cơ quan ủy nhiệm: European Commission
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