Interacting particle systems as stochastic social dynamics, Bernoulli, vol.19, issue.4, pp.1122-1149, 2013. ,
A lecture on the averaging process, Probability Surveys, vol.9, pp.90-102, 2012. ,
Eigenvalues and spectral dimension of random geometric graphs in thermodynamic regime, International Conference on Complex Networks and Their Applications, pp.965-975, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02397383
Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n), Advances in Neural Information Processing Systems, pp.773-781, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00831977
Nonparametric estimation of a function from noiseless observations at random points, Journal of Multivariate Analysis, vol.160, pp.93-104, 2017. ,
Accelerated gossip in networks of given dimension using Jacobi polynomial iterations, SIAM Journal on Mathematics of Data Science, vol.2, issue.1, pp.24-47, 2020. ,
URL : https://hal.archives-ouvertes.fr/hal-01797016
The tradeoffs of large scale learning, Advances in Neural Information Processing Systems, vol.20, pp.161-168, 2008. ,
On-line learning for very large data sets, Applied Stochastic Models in Business and Industry, vol.21, issue.2, pp.137-151, 2005. ,
Optimal rates for the regularized least-squares algorithm, Foundations of Computational Mathematics, vol.7, issue.3, pp.331-368, 2007. ,
On the linear convergence of the stochastic gradient method with constant step-size, Optimization Letters, vol.13, issue.5, pp.1177-1187, 2019. ,
, Spectral Graph Theory. Number 92 in CBMS Regional Conference Series in Mathematics, 1997.
Nonparametric stochastic approximation with large step-sizes, The Annals of Statistics, vol.44, issue.4, pp.1363-1399, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01053831
Harder, better, faster, stronger convergence rates for least-squares regression, The Journal of Machine Learning Research, vol.18, issue.1, pp.3520-3570, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01275431
Sobolev norm learning rates for regularized least-squares algorithm, 2017. ,
A distribution-free theory of nonparametric regression, 2006. ,
Kernel methods in machine learning, The Annals of Statistics, pp.1171-1220, 2008. ,
Kernel truncated randomized ridge regression: Optimal rates and low noise acceleration, Advances in Neural Information Processing Systems, pp.15332-15341, 2019. ,
Optimal global rates of convergence for interpolation problems with random design, Statistics & Probability Letters, vol.83, issue.8, pp.1871-1879, 2013. ,
Optimal convergence for distributed learning with stochastic gradient methods and spectral-regularization algorithms, 2018. ,
The power of interpolation: Understanding the effectiveness of SGD in modern over-parametrized learning, Proceedings of the 35th International Conference on Machine Learning, pp.3325-3334, 2018. ,
Isoperimetry and heat kernel decay on percolation clusters. The Annals of Probability, vol.32, pp.100-128, 2004. ,
Beating SGD saturation with tail-averaging and minibatching, Advances in Neural Information Processing Systems, pp.12568-12577, 2019. ,
Constrained consensus and optimization in multi-agent networks, IEEE Transactions on Automatic Control, vol.55, issue.4, pp.922-938, 2010. ,
Statistical optimality of stochastic gradient descent on hard learning problems through multiple passes, Advances in Neural Information Processing Systems, pp.8114-8124, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01799116
Learning with incremental iterative regularization, Advances in Neural Information Processing Systems, pp.1630-1638, 2015. ,
Fast convergence of stochastic gradient descent under a strong growth condition, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00855113
, Gossip algorithms. Foundations and Trends® in Networking, vol.3, pp.1-125, 2009.
Online learning as stochastic approximation of regularization paths: Optimality and almost-sure convergence, IEEE Transactions on Information Theory, vol.60, issue.9, pp.5716-5735, 2014. ,
Introduction to Nonparametric Estimation, 2008. ,
Fast and faster convergence of sgd for over-parameterized models and an accelerated perceptron, Proceedings of Machine Learning Research, pp.1195-1204, 2019. ,
Spline Models for Observational Data, Society for Industrial and Applied Mathematics, 1990. ,
Scattered Data Approximation, Cambridge Monographs on Applied and Computational Mathematics, 2004. ,
Online gradient descent learning algorithms, Foundations of Computational Mathematics, vol.8, issue.5, pp.561-596, 2008. ,