130 citations · 304 across the 10 of their papers we have counts for
10 papers
Optimal Robust Linear Regression in Nearly Linear Time
Yeshwanth Cherapanamjeri, Efe Aras, Nilesh Tripuraneni +3
We study the problem of high-dimensional robust linear regression where a learner is given access to samples from the generative model (with $X…
An Efficient Sampling Algorithm for Non-smooth Composite Potentials
Wenlong Mou, Nicolas Flammarion, Martin J. Wainwright +1
We consider the problem of sampling from a density of the form , where is a smooth and strongly convex func…
Improved Bounds for Discretization of Langevin Diffusions: Near-Optimal Rates without Convexity
Wenlong Mou, Nicolas Flammarion, Martin J. Wainwright +1
We present an improved analysis of the Euler-Maruyama discretization of the Langevin diffusion. Our analysis does not require global contractivity, and yields polynomial dependence…
Escaping from saddle points on Riemannian manifolds
Yue Sun, Nicolas Flammarion, Maryam Fazel
We consider minimizing a nonconvex, smooth function on a Riemannian manifold . We show that a perturbed version of Riemannian gradient descent algorithm converges…
Fast Mean Estimation with Sub-Gaussian Rates
Yeshwanth Cherapanamjeri, Nicolas Flammarion, Peter L. Bartlett
We propose an estimator for the mean of a random vector in that can be computed in time for i.i.d.~samples and that has error bounds matching the s…
Is There an Analog of Nesterov Acceleration for MCMC?
Yi-An Ma, Niladri Chatterji, Xiang Cheng +3
We formulate gradient-based Markov chain Monte Carlo (MCMC) sampling as optimization on the space of probability measures, with Kullback-Leibler (KL) divergence as the objective fu…
Sampling Can Be Faster Than Optimization
Yi-An Ma, Yuansi Chen, Chi Jin +2
Optimization algorithms and Monte Carlo sampling algorithms have provided the computational foundations for the rapid growth in applications of statistical machine learning in rece…
Gen-Oja: A Two-time-scale approach for Streaming CCA
Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion +2
In this paper, we study the problems of principal Generalized Eigenvector computation and Canonical Correlation Analysis in the stochastic setting. We propose a simple and efficien…
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo
Niladri S. Chatterji, Nicolas Flammarion, Yi-An Ma +2
We provide convergence guarantees in Wasserstein distance for a variety of variance-reduction methods: SAGA Langevin diffusion, SVRG Langevin diffusion and control-variate underdam…
Averaging Stochastic Gradient Descent on Riemannian Manifolds
Nilesh Tripuraneni, Nicolas Flammarion, Francis Bach +1
We consider the minimization of a function defined on a Riemannian manifold accessible only through unbiased estimates of its gradients. We develop a geometric framew…