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Your search keyword '"Laroche, Romain"' showing total 7 results
Start Over Author "Laroche, Romain" Topic [info]computer science [cs]
7 results on '"Laroche, Romain"'

1. Massive Multi-Player Multi-Armed Bandits for IoT Networks: An Application on LoRa Networks

Author

Dakdouk, Hiba, Feraud, Raphaël, Varsier, Nadège, Maillé, Patrick, Laroche, Romain, Orange Labs, Département Systèmes Réseaux, Cybersécurité et Droit du numérique (IMT Atlantique - SRCD), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Dependability Interoperability and perfOrmance aNalYsiS Of networkS (DIONYSOS), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES (IRISA-D2), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Microsoft Research, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique)

Subjects
[INFO]Computer Science [cs]
Abstract

More and more manufacturers, as part of the transition toward Industry 4.0, are using Internet of Things (IoT) networks for more efficient production. The wide and extensive expansion of IoT devices and the variety of applications generate different challenges, mainly in terms of reliability and energy efficiency. In this paper, we propose an approach to optimize the performance of IoT networks by making the IoT devices intelligent using machine learning techniques. We formulate the optimization problem as a massive multi-player multi-armed bandit and introduce two novel policies: Decreasing-Order-Reward-Greedy (DORG) focuses on the number of successful transmissions, while Decreasing-Order-Fair-Greedy (DOFG) also guarantees some measure of fairness between the devices. We then present an efficient way to manage the trade-off between energy consumption and packet losses in Long-Range (LoRa) networks using our algorithms, by which LoRa nodes adjust their emission parameters (Spreading Factor and transmitting power). We implement our algorithms on a LoRa network simulator and show that such learning techniques largely outperform the Adaptive Data Rate (ADR) algorithm currently implemented in LoRa devices, in terms of both energy consumption and packet losses.

Published
2022

2. Collaborative Exploration and Exploitation in massively Multi-Player Bandits

Author

Dakdouk, Hiba, Féraud, Raphaël, Laroche, Romain, Varsier, Nadège, Maillé, Patrick, Orange Labs, Microsoft Research, Département Systèmes Réseaux, Cybersécurité et Droit du numérique (IMT Atlantique - SRCD), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Dependability Interoperability and perfOrmance aNalYsiS Of networkS (DIONYSOS), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES (IRISA-D2), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES (IRISA-D2), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Inria Rennes – Bretagne Atlantique, and Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
[INFO]Computer Science [cs]
Abstract

In this paper, we propose an approach to optimize the performance of Internet of Things (IoT) networks. We formulate the optimization problem as a massive multi-player multi-armed bandit problem, where the devices are the players and the radio channels are the arms, with collisions possibly preventing message reception. For handling a realistic IoT network, we do not assume that sensing information is available (i.e. that the collision are observed) or that the number of players is smaller than the number of arms. As the optimization problem is intractable, we propose two greedy policies: the first one focusing on the number of successful communications, while the second one also takes into account fairness between players. In order to implement an approximation of the targeted policies, we propose an explore-then-exploit approach, and establish a regret lower bound in Ω T 2/3 log T N + K 3/2. For estimating the mean reward of arms, we propose a decentralized exploration algorithm with controlled information exchanges between players. Then we state that the regret of the estimated target policy is optimal with respect to the time horizon T. Finally, we provide some experimental evidences that the proposed algorithms outperform several baselines.

Published
2021

3. Safe transfer learning for dialogue applications

Author

Carrara, Nicolas, Laroche, Romain, Bouraoui, Jean-Léon, Urvoy, Tanguy, Pietquin, Olivier, Orange Labs [Lannion], France Télécom, Sequential Learning (SEQUEL), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Maluuba, Google Inc, and Research at Google

Subjects
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO]Computer Science [cs], Transfer Learning, Dialogue, Safety, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; In this paper, we formulate the hypothesis that the first dialogues with a new user should be handle in a very conservative way, for two reasons : avoid user dropout; gather more successful dialogues to speedup the learning of the asymptotic strategy. To this extend, we propose to transfer a safe strategy to initiate the first dialogues.

Published
2018

4. Task Completion Transfer Learning for Reward Inference

Author

El Asri, Layla, Laroche, Romain, Pietquin, Olivier, Georgia Tech Lorraine [Metz], Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Ecole Supérieure d'Electricité - SUPELEC (FRANCE)-Georgia Institute of Technology [Atlanta]-CentraleSupélec-Ecole Nationale Supérieure des Arts et Metiers Metz-Centre National de la Recherche Scientifique (CNRS), Orange Labs [Issy les Moulineaux], France Télécom, Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), Ecole Nationale Supérieure des Arts et Metiers Metz-Georgia Institute of Technology [Atlanta]-Ecole Supérieure d'Electricité - SUPELEC (FRANCE)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC), and Pietquin, Olivier

Subjects
[SPI]Engineering Sciences [physics], [SPI] Engineering Sciences [physics], [INFO]Computer Science [cs], [INFO] Computer Science [cs]
Abstract

International audience; Reinforcement learning-based spoken dialogue systems aimto compute an optimal strategy for dialogue managementfrom interactions with users. They compare their differentmanagement strategies on the basis of a numerical rewardfunction. Reward inference consists of learning a rewardfunction from dialogues scored by users. A major issue for rewardinference algorithms is that important parameters influenceuser evaluations and cannot be computed online. This isthe case of task completion. This paper introduces Task CompletionTransfer Learning (TCTL): a method to exploit theexact knowledge of task completion on a corpus of dialoguesscored by users in order to optimise online learning. Comparedto previously proposed reward inference techniques,TCTL returns a reward function enhanced with the possibilityto manage the online non-observability of task completion.A reward function is learnt with TCTL on dialogues with arestaurant seeking system. It is shown that the reward functionreturned by TCTL is a better estimator of dialogue performancethan the one returned by reward inference.

Published
2014

5. NASTIA : Negotiating Appointment Setting Interface

Author

El Asri, Layla, Lemonnier, Rémi, Laroche, Romain, Khouzaimi, Hatim, Pietquin, Olivier, Georgia Tech Lorraine [Metz], Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Ecole Supérieure d'Electricité - SUPELEC (FRANCE)-Georgia Institute of Technology [Atlanta]-CentraleSupélec-Ecole Nationale Supérieure des Arts et Metiers Metz-Centre National de la Recherche Scientifique (CNRS), Orange Labs [Issy les Moulineaux], France Télécom, Laboratoire Informatique d'Avignon (LIA), Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI, Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), Pietquin, Olivier, Ecole Nationale Supérieure des Arts et Metiers Metz-Georgia Institute of Technology [Atlanta]-Ecole Supérieure d'Electricité - SUPELEC (FRANCE)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), and Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)

Subjects
[SPI]Engineering Sciences [physics], [SPI] Engineering Sciences [physics], [INFO]Computer Science [cs], [INFO] Computer Science [cs]
Abstract

International audience; This paper describes a French Spoken Dialogue System (SDS) named NASTIA (Negotiating Appointment SeTting InterfAce).Appointment scheduling is a hybrid task halfway between slot-filling and negotiation. NASTIA implements three different negotiationstrategies. These strategies were tested on 1734 dialogues with 385 users who interacted at most 5 times with the SDS and gave a ratingon a scale of 1 to 10 for each dialogue. Previous appointment scheduling systems were evaluated with the same experimental protocol.NASTIA is different from these systems in that it can adapt its strategy during the dialogue. The highest system task completion ratewith these systems was 81% whereas NASTIA had an 88% average and its best performing strategy even reached 92%. This strategyalso significantly outperformed previous systems in terms of overall user rating with an average of 8.28 against 7.40. The experimentalso enabled highlighting global recommendations for building spoken dialogue systems.

Published
2014

6. DINASTI : Dialogues with a Negotiating Appointment Setting Interface

Author

El Asri, Layla, Laroche, Romain, Pietquin, Olivier, Georgia Tech Lorraine [Metz], Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Ecole Supérieure d'Electricité - SUPELEC (FRANCE)-Georgia Institute of Technology [Atlanta]-CentraleSupélec-Ecole Nationale Supérieure des Arts et Metiers Metz-Centre National de la Recherche Scientifique (CNRS), Orange Labs [Issy les Moulineaux], France Télécom, Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), Pietquin, Olivier, Ecole Nationale Supérieure des Arts et Metiers Metz-Georgia Institute of Technology [Atlanta]-Ecole Supérieure d'Electricité - SUPELEC (FRANCE)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), and Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)

Subjects
[SPI]Engineering Sciences [physics], [SPI] Engineering Sciences [physics], [INFO]Computer Science [cs], [INFO] Computer Science [cs]
Abstract

International audience; This paper describes the DINASTI (DIalogues with a Negotiating Appointment SeTting Interface) corpus, which is composed of 1734dialogues with the French spoken dialogue system NASTIA (Negotiating Appointment SeTting InterfAce). NASTIA is a reinforcementlearning-based system. The DINASTI corpus was collected while the system was following a uniform policy. Each entry of the corpusis a system-user exchange annotated with 120 automatically computable features.The corpus contains a total of 21587 entries, with 385testers. Each tester performed at most five scenario-based interactions with NASTIA. The dialogues last an average of 10.82 dialogueturns, with 4.45 reinforcement learning decisions. The testers filled an evaluation questionnaire after each dialogue. The questionnaireincludes three questions to measure task completion. In addition, it comprises 7 Likert-scaled items evaluating several aspects of theinteraction, a numerical overall evaluation on a scale of 1 to 10, and a free text entry. Answers to this questionnaire are provided withDINASTI. This corpus is meant for research on reinforcement learning modelling for dialogue management.

Published
2014

7. Uncertainty Management in Dialogue Systems

Author

Laroche, Romain, Bouchon-Meunier, Bernadette, Bretier, P., Machine Learning and Information Retrieval (MALIRE), Laboratoire d'Informatique de Paris 6 (LIP6), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), and Publications, Lip6

Subjects
[INFO]Computer Science [cs], [INFO] Computer Science [cs]
Abstract

International audience; Dialogue systems find more and more applications but the scientific improvements face difficulties to be transferred into the dialog technologies. France Telecom R&D is designing a new architecture based on uncertainty management in dialogue. Dialogue constraints lead to define a Logical Framework for Probabilistic Reasoning (LFPR) which results are close to the ones of the Evidence Theory and the Assumption-based Truth Maintenance System (ATMS). The paper eventually compares it to the Theory of Hints of Kohlas and Monney.

Published
2008

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