1 code implementation • 6 Jul 2020 • Pierre Humbert, Laurent Oudre, Nivolas Vayatis, Julien Audiffren
Recently, there has been growing interest in the analysis of spectrograms of ElectroEncephaloGram (EEG), particularly to study the neural correlates of (un)-consciousness during General Anesthesia (GA).
no code implementations • 5 Sep 2019 • Laura Rettig, Julien Audiffren, Philippe Cudré-Mauroux
We address the problem of tuning word embeddings for specific use cases and domains.
no code implementations • 9 Aug 2019 • Pierre Humbert, Julien Audiffren, Laurent Oudre, Nicolas Vayatis
This paper introduces a new multivariate convolutional sparse coding based on tensor algebra with a general model enforcing both element-wise sparsity and low-rankness of the activations tensors.
no code implementations • ICLR 2018 • Thomas Moreau, Julien Audiffren
One of the main challenges of deep learning methods is the choice of an appropriate training strategy.
no code implementations • NeurIPS 2017 • Julien Audiffren, Liva Ralaivola
We propose an algorithm, UnchainedBandits, that efficiently finds the set of optimal arms, or Pareto front, of any poset even when pairs of comparable arms cannot be a priori distinguished from pairs of incomparable arms, with a set of minimal assumptions.
1 code implementation • 14 Nov 2016 • Thomas Moreau, Julien Audiffren
One of the main challenges of deep learning methods is the choice of an appropriate training strategy.
no code implementations • 8 Feb 2016 • Julien Audiffren, Ralaivola Liva
We adress the problem of dueling bandits defined on partially ordered sets, or posets.
no code implementations • NeurIPS 2015 • Julien Audiffren, Liva Ralaivola
We study the restless bandit problem where arms are associated with stationary $\varphi$-mixing processes and where rewards are therefore dependent: the question that arises from this setting is that of carefully recovering some independence by `ignoring' the values of some rewards.
no code implementations • 28 Oct 2015 • Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu, Alain Rakotomamonjy, Julien Audiffren
In this paper we consider the problems of supervised classification and regression in the case where attributes and labels are functions: a data is represented by a set of functions, and the label is also a function.
no code implementations • 23 Jun 2014 • Julien Audiffren, Liva Ralaivola
To do so, we provide a UCB strategy together with a general regret analysis for the case where the size of the independence blocks (the ignored rewards) is fixed and we go a step beyond by providing an algorithm that is able to compute the size of the independence blocks from the data.
no code implementations • 10 Jun 2014 • Julien Audiffren, Hachem Kadri
The purpose of this paper is to introduce a concept of equivalence between machine learning algorithms.
no code implementations • 1 Nov 2013 • Julien Audiffren, Hachem Kadri
We consider the problem of learning a vector-valued function f in an online learning setting.
no code implementations • 9 Oct 2013 • Julien Audiffren, Hachem Kadri
Regularization is used to find a solution that both fits the data and is sufficiently smooth, and thereby is very effective for designing and refining learning algorithms.
no code implementations • 17 Jun 2013 • Julien Audiffren, Hachem Kadri
We show that multi-task kernel regression algorithms are uniformly stable in the general case of infinite-dimensional output spaces.