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Publications associées (200)

An accelerated communication-efficient primal-dual optimization framework for structured machine learning

Distributed optimization algorithms are essential for training machine learning models on very large-scale datasets. However, they often suffer from communication bottlenecks. Confronting this issue, a communication-efficient primal-dual coordinate ascent ...

2019

Safe Convex Learning under Uncertain Constraints

Maryam Kamgarpour, Andreas Krause, Ilnura Usmanova

We address the problem of minimizing a convex smooth function f(x) over a compact polyhedral set D given a stochastic zeroth-order constraint feedback model. This problem arises in safety-critical machine learning applications, such as personalized medicin ...

PMLR2019

Convex Optimization using Sparsified Stochastic Gradient Descent with Memory

Jean-Baptiste Francis Marie Juliette Cordonnier

The interest for distributed stochastic optimization has raised to train complex Machine Learning models with more data on distributed systems. Increasing the computation power speeds up the training but it faces a communication bottleneck between workers ...

2018

Mean-Field methods for Structured Deep-Learning in Computer Vision

Pierre Bruno Baqué

In recent years, Machine Learning based Computer Vision techniques made impressive progress. These algorithms proved particularly efficient for image classification or detection of isolated objects. From a probabilistic perspective, these methods can predi ...

EPFL2018

On the linear convergence of the stochastic gradient method with constant step-size

Volkan Cevher, Cong Bang Vu

The strong growth condition (SGC) is known to be a sufficient condition for linear convergence of the stochastic gradient method using a constant step-size γ (SGM-CS). In this paper, we provide a necessary condition, for the linear convergence of SGM-CS, t ...

2018

Learning Control

Sylvain Calinon

This chapter presents an overview of learning approaches for the acquisition of controllers and movement skills in humanoid robots. The term learning control refers to the process of acquiring a control strategy to achieve a task. While the definition is i ...

Springer2018

Accelerated Sensorimotor Learning of Compliant Movement Primitives

Auke Ijspeert, Tadej Petric, Luca Colasanto

Autonomous trajectory generation through generalization requires a database of motion, which can be difficult and time consuming to obtain. In this paper, we propose a method for autonomous expansion of a database for the generation of compliant and accura ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2018

Nonlinear PD plus sliding mode control with application to a parallel delta robot

Mohamed Bouri

In this paper, a hybrid nonlinear proportional-derivative-sliding mode controller (NPD-SMC) is developed for the trajectory tracking of robot manipulators. The proposed controller combines the advantage of the easy implementation of NPD control and the rob ...

SLOVAK UNIV TECHNOLOGY2018

Let’s be honest: An optimal no-regret framework for zero-sum games

Volkan Cevher, Ya-Ping Hsieh, Mehmet Fatih Sahin, Ehsan Asadi Kangarshahi

We revisit the problem of solving two-player zero- sum games in the decentralized setting. We pro- pose a simple algorithmic framework that simulta- neously achieves the best rates for honest regret as well as adversarial regret, and in addition resolves t ...

2018

Iterative Learning of Speech Recognition Models for Air Traffic Control

Petr Motlicek, Mittul Singh

Automatic Speech Recognition (ASR) has recently proved to be a useful tool to reduce the workload of air traffic controllers leading to significant gains in operational efficiency. Air Traffic Control (ATC) systems in operation rooms around the world gener ...

2018

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