works on

From the 1 of 123 linked papers with an AI index.

activity
20062026
most citedSupervised Dictionary Learning

735 citations · 3k across the 69 of their papers we have counts for

collaborators
Showing 2017Show all

9 papers · 1 filter

math.OC2017

Structure-Adaptive, Variance-Reduced, and Accelerated Stochastic Optimization

Junqi Tang, Francis Bach, Mohammad Golbabaee +1

In this work we explore the fundamental structure-adaptiveness of state of the art randomized first order algorithms on regularized empirical risk minimization tasks, where the sol…

cs.LG20173 cited

AdaBatch: Efficient Gradient Aggregation Rules for Sequential and Parallel Stochastic Gradient Methods

Alexandre Défossez, Francis Bach

We study a new aggregation operator for gradients coming from a mini-batch for stochastic gradient (SG) methods that allows a significant speed-up in the case of sparse optimizatio…

math.OC20177 cited

Convex optimization over intersection of simple sets: improved convergence rate guarantees via an exact penalty approach

Achintya Kundu, Francis Bach, Chiranjib Bhattacharyya

We consider the problem of minimizing a convex function over the intersection of finitely many simple sets which are easy to project onto. This is an important problem arising in v…

cs.LG201724 cited

A Generic Approach for Escaping Saddle points

Sashank J Reddi, Manzil Zaheer, Suvrit Sra +4

A central challenge to using first-order methods for optimizing nonconvex problems is the presence of saddle points. First-order methods often get stuck at saddle points, greatly d…

cs.LG20176 cited

Efficient Algorithms for Non-convex Isotonic Regression through Submodular Optimization

Francis Bach

We consider the minimization of submodular functions subject to ordering constraints. We show that this optimization problem can be cast as a convex optimization problem on a space…

math.PR2017

Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance

Jonathan Weed, Francis Bach

The Wasserstein distance between two probability measures on a metric space is a measure of closeness with applications in statistics, probability, and machine learning. In this wo…