Publications associées à Strong dual space

Efficient Continual Finite-Sum Minimization

Volkan Cevher, Efstratios Panteleimon Skoulakis, Leello Tadesse Dadi

Given a sequence of functions

f_1,\ldots,f_n

with

f_i:\mathcal{D}\mapsto \mathbb{R}

, finite-sum minimization seeks a point

{x}^\star \in \mathcal{D}

minimizing

\sum_{j=1}^nf_j(x)/n

. In this work, we propose a key twist into the finite-sum minimizat ...

2024

Editing Tetrasubstituted Carbon: Dual C–O Bond Functionalization of Tertiary Alcohols Enabled by Palladium-Based Dyotropic Rearrangement

Qian Wang, Jieping Zhu, Baochao Yang, Tristan Corentin Pierre Delcaillau

Many elegant asymmetric syntheses of enantioenriched tertiary alcohols have been developed, and both the transition metal-catalyzed and the radical-based peripheral functionalization of tertiary alcohols have attracted intensive research interest in recent ...

2024

The statistical complexity of early-stopped mirror descent

Tomas Vaskevicius, Varun Kanade

Recently there has been a surge of interest in understanding implicit regularization properties of iterative gradient-based optimization algorithms. In this paper, we study the statistical guarantees on the excess risk achieved by early-stopped unconstrain ...

Oxford Univ Press2023

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

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, Amirkeivan Mohtashami, Sebastian Urban Stich

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 ...

MICROTOME PUBLISHING2021

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

Vincent Gramoli

There is an increasing interest in a fast-growing machine learning technique called Federated Learning (FL), in which the model training is distributed over mobile user equipment (UEs), exploiting UEs' local computation and training data. Despite its advan ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads

Martin Jaggi, Sebastian Urban Stich, Hossein Shokri Ghadikolaei

In distributed optimization, parameter updates from the gradient computing node devices have to be aggregated in every iteration on the orchestrating server. When these updates are sent over an arbitrary commodity network, bandwidth and latency can be limi ...

MICROTOME PUBLISHING2021

A Multi-Agent Primal-Dual Strategy for Composite Optimization over Distributed Features

Ali H. Sayed, Sulaiman A S A E Alghunaim, Ming Yan

This work studies multi-agent sharing optimization problems with the objective function being the sum of smooth local functions plus a convex (possibly non-smooth) function coupling all agents. This scenario arises in many machine learning and engineering ...

IEEE2020

Stochastic Three-Composite Convex Minimization with a Linear Operator

Volkan Cevher

We develop a primal-dual convex minimization framework to solve a class of stochastic convex three-composite problem with a linear operator. We consider the cases where the problem is both convex and strongly convex and analyze the convergence of the propo ...

JMLR: W&CP2018

Rich cell-type-specific network topology in neocortical microcircuitry

Henry Markram, Eilif Benjamin Muller, Srikanth Ramaswamy, Michael Reimann, Idan Segev, Eyal Gal, Michael London

Uncovering structural regularities and architectural topologies of cortical circuitry is vital for understanding neural computations. Recently, an experimentally constrained algorithm generated a dense network reconstruction of a similar to 0.3-mm(3) volum ...

Nature Publishing Group2017

Converse Bounds for Noisy Group Testing with Arbitrary Measurement Matrices

Volkan Cevher, Jonathan Mark Scarlett

We consider the group testing problem, in which one seeks to identify a subset of defective items within a larger set of items based on a number of noisy tests. While matching achievability and converse bounds are known in several cases of interest for i.i ...

Ieee2016

A Note on the Expressiveness of BIP

Simon Bliudze, Eduard Baranov

We extend our previous algebraic formalisation of the notion of component-based framework in order to formally define two forms, strong and weak, of the notion of full expressiveness. Our earlier result shows that the BIP (Behaviour-Interaction-Priority) f ...

Open Publ Assoc2016

Polymer Brush Guided Formation of Conformal, Plasmonic Nanoparticle-Based Electrodes for Microwire Solar Cells

Harm-Anton Klok, Anna Fontcuberta i Morral, Caroline Sugnaux, Anna Dalmau Mallorquí

This report explores the use of sacrificial thin polymer films prepared by surface-initiated polymerization as a template for the fabrication of highly conformal metal nanoparticle solar cell electrodes. As a first proof-of-principle, the use of this metho ...

Wiley-Blackwell2015

A codimension two stable manifold of near soliton equivariant wave maps

Joachim Krieger, Daniel Tataru

We consider finite-energy equivariant solutions for the wave map problem from ℝ2+1 to S2 which are close to the soliton family. We prove asymptotic orbital stability for a codimension-two class of initial data which is small with respect to a stronger topo ...

Mathematical Science Publ2013

Convex Generalizations of Total Variation Based on the Structure Tensor with Applications to Inverse Problems

Michaël Unser, Stamatios Lefkimmiatis

We introduce a generic convex energy functional that is suitable for both grayscale and vector-valued images. Our functional is based on the eigenvalues of the structure tensor, therefore it penalizes image variation at every point by taking into account t ...

Springer2013

Widgets and Spaces : Personal & Contextual Portability and Plasticity with OpenSocial

Evgeny Bogdanov

Social media platforms are created and exploited for various activities carried out individually or collaboratively and relying on different resources and tools. Social media platforms are inherently contextual; the context being defined as a specific acti ...

EPFL2013

Extrapolation et synthèse

Christian Fiorillo

Let G a locally compact group, H a closed subgroup and 1 < p < ∞. It's well-known that the restriction of the functions from G to H is a surjective linear contraction from Ap(G) onto Ap(H). We prove, when H is amenable, that every element in Ap(H) with com ...

EPFL2007

Minkowski sums of polytopes : combinatorics and computation

Christophe Weibel

Minkowski sums are a very simple geometrical operation, with applications in many different fields. In particular, Minkowski sums of polytopes have shown to be of interest to both industry and the academic world. This thesis presents a study of these sums, ...

EPFL2007

On Interpolation and Resampling of Discrete Data

Pouya Dehghani Tafti

This letter introduces a new representation of discrete signals based on the mathematical notions of functionals and continuous dual spaces. A new and more general sampling theorem is also suggested. Next, the problems of interpolating and resampling discr ...

IEEE Institute of Electrical and Electronics Engineers2006