Nicolas Flammarion
Nicolas Flammarion
Verified email at berkeley.edu - Homepage
TitleCited byYear
From averaging to acceleration, there is only a step-size
N Flammarion, F Bach
Conference on Learning Theory, 658-695, 2015
662015
Harder, better, faster, stronger convergence rates for least-squares regression
A Dieuleveut, N Flammarion, F Bach
The Journal of Machine Learning Research 18 (1), 3520-3570, 2017
402017
Optimal rates of statistical seriation
N Flammarion, C Mao, P Rigollet
Bernoulli 25 (1), 623-653, 2019
232019
On the theory of variance reduction for stochastic gradient Monte Carlo
NS Chatterji, N Flammarion, YA Ma, PL Bartlett, MI Jordan
arXiv preprint arXiv:1802.05431, 2018
202018
Averaging stochastic gradient descent on Riemannian manifolds
N Tripuraneni, N Flammarion, F Bach, MI Jordan
arXiv preprint arXiv:1802.09128, 2018
92018
Stochastic Composite Least-Squares Regression with convergence rate O (1/n)
N Flammarion, F Bach
arXiv preprint arXiv:1702.06429, 2017
92017
Robust discriminative clustering with sparse regularizers
N Flammarion, B Palaniappan, F Bach
The Journal of Machine Learning Research 18 (1), 2764-2813, 2017
72017
Sampling can be faster than optimization
YA Ma, Y Chen, C Jin, N Flammarion, MI Jordan
arXiv preprint arXiv:1811.08413, 2018
42018
Is There an Analog of Nesterov Acceleration for MCMC?
YA Ma, N Chatterji, X Cheng, N Flammarion, P Bartlett, MI Jordan
arXiv preprint arXiv:1902.00996, 2019
22019
Fast Mean Estimation with Sub-Gaussian Rates
Y Cherapanamjeri, N Flammarion, PL Bartlett
arXiv preprint arXiv:1902.01998, 2019
2019
Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation
K Bhatia, A Pacchiano, N Flammarion, PL Bartlett, MI Jordan
Advances in Neural Information Processing Systems, 7016-7025, 2018
2018
Stochastic approximation and least-squares regression, with applications to machine learning
N Flammarion
Ecole normale supérieure-ENS PARIS, 2017
2017
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Articles 1–12