A dynamic gradient approach to Pareto optimization with nonsmooth convex objective functions

In a general Hilbert framework, we consider continuous gradient-like dynamical systems for constrained multiobjective optimization involving non-smooth convex objective functions. Our approach is in the line of a previous work where was considered the case of convex di erentiable objective functions. Based on the Yosida regularization of the subdi erential operators involved in the system, we obtain the existence of strong global trajectories. We prove a descent property for each objective function, and the convergence of trajectories to weak Pareto minima. This approach provides a dynamical endogenous weighting of the objective functions. Applications are given to cooperative games, inverse problems, and numerical multiobjective optimization

Essays on the macroeconomics of labor market and firm dynamics

Cette thèse contribue à la compréhension des frictions sur le marché de travail et comment ces frictions affectent les agrégats macroéconomiques comme le chômage et la productivité. Elle jette également un regard critique sur les politiques environnementales telles que la taxe carbone et le financement vert. Le premier chapitre examine comment les contrats de non-competition signés entre employeurs et employés affectent le chômage, la productivité et le bien-être des agents dans l'économie. Ces contrats stipulent que l'employé travaillant sous ceux-ci ne doit en aucun cas travailler pour un employeur concurrent; et ce pour une période déterminée allant de un à deux ans après séparation avec son premier employeur. Ce type de contrat est récurrent aux Etats-Unis et affecte au moins un employé sur cinq dans ce pays. Les résultats des analyses montrent qu'une forte incidence effective de ces contrats peut non seulement comprimer les salaires mais générer du chômage. Ceci est essentiellement dû au fait que certaines personnes ayant signé ce contrat ont du mal à se trouver un nouvel emploi après s'être séparées de leur premier travail. L'article propose de baisser la durée des restrictions d'emploi de ces contrats dans le but d'amoindrir leur effets sur les travailleurs. Cependant, il est à noter que ces contrats sont en partie bénéfiques du fait de l'incitation pour les employeurs de former les employés sur le marché du travail, augmentant la productivité totale. Parlant de contrats d'emploi, le deuxième chapitre évalue les implications de la coexistence de contrats dits temporaires (contrat à durée déterminée) et permanents (contrat à durée indéterminée) sur le flux des travailleurs entre chômage, emploi et non-participation au marché du travail durant le cycle de vie des agents. Cette analyse revêt une importance particulière du fait des effets de ces flux de travailleurs sur l'emploi agrégé et les salaires durant le cycle de vie des agents. Il en ressort que les transitions des individus d'un emploi permanent au chômage sont le plus important facteur expliquant l'emploi agrégé durant le cycle de vie des agents. Toute politique visant à augmenter l'emploi devrait cibler ce flux de travailleurs. Par ailleurs, la transition des individus d'un emploi temporaire vers le chômage se révèle être significatif dans l'explication du faible emploi des jeunes dans les pays européens comme la France, surtout pour ceux ayant un niveau d'éducation élevé. l'article va plus loin en construisant un model qui explique les profils de transitions observés durant le cycle de vie des agents et analyse comment les effets associés aux réformes de protection de l'emploi dans les pays européens sont distribués entre les travailleurs selon leur niveau d'éducation et âge. Enfin, le troisième chapitre jette un regard critique sur les politiques environnementales comme la taxe sur les émissions générées par les unités de production et le financement vert. L'article montre qu'en dépit de leur efficacité dans la réduction des émissions, ces politiques peuvent impacter négativement l'allocation des ressources comme le capital entre les firmes, réduisant la productivité agrégée. Ceci provient du fait que certaines entreprises très productives mais financièrement contraintes peuvent avoir des difficultés à investir dans la technologie de réduction de leurs émissions carbone alors que d'autres moins productives que les premières mais très riches, investissent plus facilement. Le poids du fardeau fiscal lié aux emissions force les premières à quitter le marché réduisant la productivité. Ceci suggère que d'autres politiques comme celle de subventions vertes sont importantes pour réduire ces potentielles distortions.This thesis contributes to understanding labor market frictions and how these frictions impact macroeconomic aggregates such as unemployment and productivity. It also critically examines environmental policies such as carbon taxes and green financing. The first chapter examines how non-compete contracts signed between employers and employees affect unemployment, productivity, and welfare in the economy. These contracts stipulate that the employee, while under contract, cannot work for a competing employer for a specified period, typically ranging from one to two years after separation from their initial employer. This type of contract is widespread in the United States and affects at least one in five employees in the country. Results show that a high enforceable incidence of these contracts can compress wages and generate unemployment. This is primarily due to the fact that some individuals who have signed such contracts face difficulties in finding new employment after separating from their initial job. The article proposes reducing the duration of the post-employment restrictions of these contracts to mitigate their effects on workers. However, it is worth noting that these contracts partially benefit employers by incentivizing them to invest in employee training, thereby increasing overall productivity. Speaking of employment contracts, the second chapter evaluates the implications of the coexistence of temporary contracts (fixed-term contracts) and permanent contracts (indefinite-term contracts) on worker flows between unemployment, employment, and labor force non-participation over the life-cycle. This analysis is particularly important due to the effects of these flows on aggregate employment and wages over the life-cycle. It is found that transitions of individuals from permanent employment to unemployment are the most significant factor explaining aggregate employment over the life-cycle. Any policy aimed at increasing employment should target this flow of workers. Moreover, the transition of individuals from temporary employment to unemployment is significant in explaining the low employment of young individuals in European countries like France, especially for those with higher levels of education. The article goes further by constructing a model that explains the observed transition profiles during agents' life-cycle and analyzes how the effects linked to employment protection reforms in European countries are distributed among workers based on their level of education and age. Finally, the third chapter provides a critical assessment of environmental policies such as emissions taxes on production units and green financing. The article shows that despite their effectiveness in reducing emissions, these policies can negatively impact resource allocation, such as capital, among firms, thus reducing aggregate productivity. This is because some highly productive but seriously financially constrained firms may struggle to invest in emission reduction technology, while less productive but wealthy entrepreneurs invest more easily. The burden of emissions-related fiscal measures forces the former to exit the market, thereby reducing productivity. This suggests that other policies, such as green subsidies, are important to mitigate these potential distortions

Training Discriminative Models to Evaluate Generative Ones

Generative models are known to be difficult to assess. Recent works, especially on generative adversarial networks (GANs), produce good visual samples of varied categories of images. However, the validation of their quality is still difficult to define and there is no existing agreement on the best evaluation process. This paper aims at making a step toward an objective evaluation process for generative models. It presents a new method to assess a trained generative model by evaluating the test accuracy of a classifier trained with generated data. The test set is composed of real images. Therefore, The classifier accuracy is used as a proxy to evaluate if the generative model fit the true data distribution. By comparing results with different generated datasets we are able to classify and compare generative models. The motivation of this approach is also to evaluate if generative models can help discriminative neural networks to learn, i.e., measure if training on generated data is able to make a model successful at testing on real settings. Our experiments compare different generators from the Variational Auto-Encoders (VAE) and Generative Adversarial Network (GAN) frameworks on MNIST and fashion MNIST datasets. Our results show that none of the generative models is able to replace completely true data to train a discriminative model. But they also show that the initial GAN and WGAN are the best choices to generate on MNIST database (Modified National Institute of Standards and Technology database) and fashion MNIST database

State Representation Learning for Control: An Overview

Representation learning algorithms are designed to learn abstract features that characterize data. State representation learning (SRL) focuses on a particular kind of representation learning where learned features are in low dimension, evolve through time, and are influenced by actions of an agent. The representation is learned to capture the variation in the environment generated by the agent's actions; this kind of representation is particularly suitable for robotics and control scenarios. In particular, the low dimension characteristic of the representation helps to overcome the curse of dimensionality, provides easier interpretation and utilization by humans and can help improve performance and speed in policy learning algorithms such as reinforcement learning. This survey aims at covering the state-of-the-art on state representation learning in the most recent years. It reviews different SRL methods that involve interaction with the environment, their implementations and their applications in robotics control tasks (simulated or real). In particular, it highlights how generic learning objectives are differently exploited in the reviewed algorithms. Finally, it discusses evaluation methods to assess the representation learned and summarizes current and future lines of research

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Generative Models from the perspective of Continual Learning

Which generative model is the most suitablefor Continual Learning? This paper aims at evaluating andcomparing generative models on disjoint sequential imagegeneration tasks. We investigate how several models learn andforget, considering various strategies: rehearsal, regularization,generative replay and fine-tuning. We used two quantitativemetrics to estimate the generation quality and memory ability.We experiment with sequential tasks on three commonly usedbenchmarks for Continual Learning (MNIST, Fashion MNISTand CIFAR10). We found that among all models, the originalGAN performs best and among Continual Learning strategies,generative replay outperforms all other methods. Even ifwe found satisfactory combinations on MNIST and FashionMNIST, training generative models sequentially on CIFAR10is particularly instable, and remains a challenge. Our code isavailable online

Exploration Strategies for Incremental Learning of Object-Based Visual Saliency

International audienceSearching for objects in an indoor environment can be drastically improved if a task-specific visual saliency is available. We describe a method to learn such an object-based visual saliency in an intrinsically motivated way using an environment exploration mechanism. We first define saliency in a geometrical manner and use this definition to discover salient elements given an attentive but costly observation of the environment. These elements are used to train a fast classifier that predicts salient objects given large-scale visual features. In order to get a better and faster learning, we use intrinsic motivation to drive our observation selection, based on uncertainty and novelty detection. Our approach has been tested on RGB-D images, is real-time, and outperforms several state-of-the-art methods in the case of indoor object detection

Apprentissage incrémental de la saillance visuelle pour des applications robotique

National audienceNous proposons une méthode d'apprentissage incrémental de la saillance visuelle par un mécanisme d'exploration de l'environnement. Partant d'une définition géométrique de la saillance des objets, notre système observe de façon attentive et ciblée son environnement, jusqu'à découvrir des éléments saillants. Un classifieur permet alors d'apprendre les caractéristiques visuelles correspondantes afin de pouvoir ensuite prédire rapidement les positions des objets sans analyse géométrique. Notre approche a été testée sur des images RGBD, fonctionne en temps réel et dépasse plusieurs méthodes de l'état de l'art sur le contexte particulier de la détection d'objets en intérieur

On the Use of Intrinsic Motivation for Visual Saliency Learning

International audienceThe use of intrinsic motivation for the task of learning sensori-motor properties has received a lot of attention over the last few years, but only little work has been provided toward using intrinsic motivation for the task of learning visual signals. In this paper, we propose to apply the main ideas of the Intelligent Adaptive Curiosity (IAC) for the task of visual saliency learning. We here present RL-IAC, an adapted version of IAC that uses reinforcement learning to deal with time consuming displacements while actively learning saliency based on local learning progress. We also introduce the use of a backward evaluation to deal with a learner that is shared between several regions. We demonstrate the good performance of RL-IAC compared to other exploration techniques, and we discuss the performance of other intrinsic motivation sources instead of learning progress in our problem