Publications related to The role of perspective functions in convexity, polyconvexity, rank-one convexity and separate convexity

Stable parameterization of continuous and piecewise-linear functions

Michaël Unser, Alexis Marie Frederic Goujon, Joaquim Gonçalves Garcia Barreto Campos

Rectified-linear-unit (ReLU) neural networks, which play a prominent role in deep learning, generate continuous and piecewise-linear (CPWL) functions. While they provide a powerful parametric representation, the mapping between the parameter and function s ...

2023

Stochastic homogenization of degenerate integral functionals with linear growth

Matthias Ruf

We study the limit behaviour of sequences of non-convex, vectorial, random integral functionals, defined on W1,1, whose integrands are ergodic and satisfy degenerate linear growth conditions. The latter involve suitable random, scale-dependent weight-funct ...

2023

Localizing Unsynchronized Sensors With Unknown Sources

Ivan Dokmanic, Dalia Salem Hassan Fahmy El Badawy

We propose a method for sensor array self-localization using a set of sources at unknown locations. The sources produce signals whose times of arrival are registered at the sensors. We look at the general case where neither the emission times of the source ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

DIVIDE-AND-CONQUER METHODS FOR FUNCTIONS OF MATRICES WITH BANDED OR HIERARCHICAL LOW-RANK STRUCTURE\ast

Daniel Kressner, Stefano Massei, Alice Cortinovis

This work is concerned with approximating matrix functions for banded matrices, hierarchically semiseparable matrices, and related structures. We develop a new divide-and-conquer method based on (rational) Krylov subspace methods for performing low-rank up ...

SIAM PUBLICATIONS2022

Stochastic distributed learning with gradient quantization and double-variance reduction

Sebastian Urban Stich, Konstantin Mishchenko

We consider distributed optimization over several devices, each sending incremental model updates to a central server. This setting is considered, for instance, in federated learning. Various schemes have been designed to compress the model updates in orde ...

TAYLOR & FRANCIS LTD2022

Finite Littlestone Dimension Implies Finite Information Complexity

Michael Christoph Gastpar, Aditya Pradeep, Ido Nachum

We prove that every online learnable class of functions of Littlestone dimension d admits a learning algorithm with finite information complexity. Towards this end, we use the notion of a globally stable algorithm. Generally, the information complexity of ...

2022

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

Volkan Cevher, Ahmet Alacaoglu

In this paper, we analyze the recently proposed stochastic primal-dual hybrid gradient (SPDHG) algorithm and provide new theoretical results. In particular, we prove almost sure convergence of the iterates to a solution with convexity and linear convergenc ...

SIAM PUBLICATIONS2022

Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization

Volkan Cevher, Kimon Antonakopoulos, Efstratios Panteleimon Skoulakis, Leello Tadesse Dadi, Ali Kavis

We propose an adaptive variance-reduction method, called AdaSpider, for minimization of L-smooth, non-convex functions with a finite-sum structure. In essence, AdaSpider combines an AdaGrad-inspired [Duchi et al., 2011, McMahan & Streeter, 2010], but a fai ...

2022

A first-order primal-dual method with adaptivity to local smoothness

Volkan Cevher, Maria-Luiza Vladarean

We consider the problem of finding a saddle point for the convex-concave objective

\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)

, where

f

is a convex function with locally Lipschitz gradient and

g

is convex and possibly non-smooth. We propose an ...

2021

Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates

Martin Jaggi, Sebastian Urban Stich, Amirkeivan Mohtashami

It has been experimentally observed that the efficiency of distributed training with stochastic gradient (SGD) depends decisively on the batch size and—in asynchronous implementations—on the gradient staleness. Especially, it has been observed that the spe ...

2021

The role of perspective functions in convexity, polyconvexity, rank-one convexity and separate convexity

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Stable parameterization of continuous and piecewise-linear functions

Stochastic homogenization of degenerate integral functionals with linear growth

Localizing Unsynchronized Sensors With Unknown Sources

DIVIDE-AND-CONQUER METHODS FOR FUNCTIONS OF MATRICES WITH BANDED OR HIERARCHICAL LOW-RANK STRUCTURE\ast

Stochastic distributed learning with gradient quantization and double-variance reduction

Finite Littlestone Dimension Implies Finite Information Complexity

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization

A first-order primal-dual method with adaptivity to local smoothness

Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates

Stochastic homogenization of degenerate integral functionals with linear growth

DIVIDE-AND-CONQUER METHODS FOR FUNCTIONS OF MATRICES WITH BANDED OR HIERARCHICAL LOW-RANK STRUCTURE\ast

Localizing Unsynchronized Sensors With Unknown Sources

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

Stochastic distributed learning with gradient quantization and double-variance reduction

A first-order primal-dual method with adaptivity to local smoothness

Stable parameterization of continuous and piecewise-linear functions

Finite Littlestone Dimension Implies Finite Information Complexity

Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization

Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates