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1
Conference
Generalization Analysis of Machine Learning Algorithms via the Worst-Case Data-Generating Probability Measure

Authors: Zou, Xinying, Perlaza, Samir, M., Esnaola, Iñaki, Altman, Eitan

Contributors: Network Engineering and Operations (NEO ), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Department of Electrical and Computer Engineering Princeton (ECE), Princeton University, Laboratoire de Géométrie Algébrique et Applications à la Théorie de l'Information (GAATI), Université de la Polynésie Française (UPF), Department of Automatic Control and Systems Engineering Sheffield (ACSE), University of Sheffield Sheffield, Laboratoire Informatique d'Avignon (LIA), Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI, Laboratory of Information, Network and Communication Sciences (LINCS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Mines-Télécom Paris (IMT)-Sorbonne Université (SU), Inria Exploratory Action IDEM - Information and Decision Making, ANR-21-CE25-0013,PARFAIT,Planification et apprentissage pour AI-Edge Computing (PARFAIT)(2021)

Superior Title: AAAI 2024 - Conference on Artificial Intelligence ; https://inria.hal.science/hal-04353957 ; AAAI 2024 - Conference on Artificial Intelligence, Feb 2024, Vancouver, Canada

Subject Terms: Supervised Machine Learning, Worst-Case, Generalization Gap, Relative Entropy, Gibbs Algorithm, Sensitivity, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [MATH.MATH-IT]Mathematics [math]/Information Theory [math.IT], [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH]

Subject Geographic: Vancouver

Time: Vancouver, Canada

Relation: hal-04353957; https://inria.hal.science/hal-04353957; https://inria.hal.science/hal-04353957/document; https://inria.hal.science/hal-04353957/file/AAAI24camera_Hal.pdf

Availability: https://inria.hal.science/hal-04353957
https://inria.hal.science/hal-04353957/document
https://inria.hal.science/hal-04353957/file/AAAI24camera_Hal.pdf


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2
Academic Journal
The Worst-Case Data-Generating Probability Measure in Statistical Learning

Authors: Zou, Xinying, Perlaza, Samir, M, Esnaola, Iñaki, Altman, Eitan, Poor, H. Vincent

Contributors: Network Engineering and Operations (NEO ), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Department of Electrical and Computer Engineering Princeton (ECE), Princeton University, Laboratoire de Géométrie Algébrique et Applications à la Théorie de l'Information (GAATI), Université de la Polynésie Française (UPF), Department of Automatic Control and Systems Engineering Sheffield (ACSE), University of Sheffield Sheffield, Laboratoire Informatique d'Avignon (LIA), Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI, Inria Exploratory Action -- Information and Decision Making (IDEM)U.S National Science Foundation under Grant ECCS-2335876C3.ai Digital Transformation InstitutePrinceton Language + Intelligence, ANR-21-CE25-0013,PARFAIT,Planification et apprentissage pour AI-Edge Computing (PARFAIT)(2021)

Superior Title: EISSN: 2641-8770 ; IEEE Journal on Selected Areas in Information Theory ; https://inria.hal.science/hal-04442591 ; IEEE Journal on Selected Areas in Information Theory, In press, ⟨10.1109/JSAIT.2024.3383281⟩

Subject Terms: Supervised Machine Learning, Worst-Case, Generalization Error, Relative Entropy, Gibbs Algorithm, Sensitivity, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]

Relation: hal-04442591; https://inria.hal.science/hal-04442591; https://inria.hal.science/hal-04442591v2/document; https://inria.hal.science/hal-04442591v2/file/JSAIT_camera.pdf

Availability: https://doi.org/10.1109/JSAIT.2024.3383281
https://inria.hal.science/hal-04442591
https://inria.hal.science/hal-04442591v2/document
https://inria.hal.science/hal-04442591v2/file/JSAIT_camera.pdf

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3
Report
An Exact Characterization of the Generalization Error of Machine Learning Algorithms

Authors: Zou, Xinying, Perlaza, Samir, M., Esnaola, Iñaki, Altman, Eitan, Poor, H. Vincent

Contributors: Network Engineering and Operations (NEO ), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Department of Electrical and Computer Engineering Princeton (ECE), Princeton University, Laboratoire de Géométrie Algébrique et Applications à la Théorie de l'Information (GAATI), Université de la Polynésie Française (UPF), Department of Automatic Control and Systems Engineering Sheffield (ACSE), University of Sheffield Sheffield, Laboratoire Informatique d'Avignon (LIA), Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI, INRIA Exploratory Action IDEM, ANR-21-CE25-0013,PARFAIT,Planification et apprentissage pour AI-Edge Computing (PARFAIT)(2021), European Project: 872172,H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions,TESTBED2(2020)

Superior Title: https://inria.hal.science/hal-04426500 ; 2024.

Subject Terms: Generalization Error, Worst-Case, [INFO.INFO-IT]Computer Science [cs]/Information Theory [cs.IT], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]

Relation: info:eu-repo/grantAgreement//872172/EU/Testing and Evaluating Sophisticated information and communication Technologies for enaBling scalablE smart griD Deployment/TESTBED2; hal-04426500; https://inria.hal.science/hal-04426500; https://inria.hal.science/hal-04426500/document; https://inria.hal.science/hal-04426500/file/An%20Exact%20Characterization%20of%20the%20Generalization%20Error%20of%20Machine%20Learning%20Algorithms.pdf

Availability: https://inria.hal.science/hal-04426500
https://inria.hal.science/hal-04426500/document
https://inria.hal.science/hal-04426500/file/An%20Exact%20Characterization%20of%20the%20Generalization%20Error%20of%20Machine%20Learning%20Algorithms.pdf


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4
Report
An Exact Characterization of the Generalization Error of Machine Learning Algorithms ; Une caractérisation exacte de l'erreur de généralisation pour des algorithmes d'apprentissage automatique

Authors: Zou, Xinying, Perlaza, Samir, M., Esnaola, Iñaki, Altman, Eitan, Poor, H. Vincent

Contributors: Network Engineering and Operations (NEO ), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Department of Electrical and Computer Engineering Princeton (ECE), Princeton University, Laboratoire de Géométrie Algébrique et Applications à la Théorie de l'Information (GAATI), Université de la Polynésie Française (UPF), Department of Automatic Control and Systems Engineering Sheffield (ACSE), University of Sheffield Sheffield, Laboratoire Informatique d'Avignon (LIA), Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI, Inria Exploratory Action -- Information and Decision Making (IDEM)U.S National Science Foundation under Grant ECCS-2335876C3.ai Digital Transformation InstitutePrinceton Language + Intelligence, INRIA, ANR-21-CE25-0013,PARFAIT,Planification et apprentissage pour AI-Edge Computing (PARFAIT)(2021), European Project: 872172,H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions,TESTBED2(2020)

Superior Title: https://inria.hal.science/hal-04442566 ; RR-9539, INRIA. 2024, pp.67.

Subject Terms: Statistical Machine Learning, Gibbs Algorithm, Generalization Error, [INFO.INFO-IT]Computer Science [cs]/Information Theory [cs.IT], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]

Relation: info:eu-repo/grantAgreement//872172/EU/Testing and Evaluating Sophisticated information and communication Technologies for enaBling scalablE smart griD Deployment/TESTBED2; Report N°: RR-9539; hal-04442566; https://inria.hal.science/hal-04442566; https://inria.hal.science/hal-04442566v2/document; https://inria.hal.science/hal-04442566v2/file/V2-RR9539.pdf

Availability: https://inria.hal.science/hal-04442566
https://inria.hal.science/hal-04442566v2/document
https://inria.hal.science/hal-04442566v2/file/V2-RR9539.pdf


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5
Report
The Worst-Case Data-Generating Probability Measure ; La mesure de probabilité génératrice de données dans le pire des cas

Authors: Zou, Xinying, Perlaza, Samir, M., Esnaola, Iñaki, Altman, Eitan

Contributors: Network Engineering and Operations (NEO ), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Department of Electrical and Computer Engineering Princeton (ECE), Princeton University, Laboratoire de Géométrie Algébrique et Applications à la Théorie de l'Information (GAATI), Université de la Polynésie Française (UPF), University of Sheffield Sheffield, Laboratoire Informatique d'Avignon (LIA), Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI, Laboratory of Information, Network and Communication Sciences (LINCS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Mines-Télécom Paris (IMT)-Sorbonne Université (SU), Inria Exploratory Action -- Information and Decision Making (IDEM), INRIA, ANR-21-CE25-0013,PARFAIT,Planification et apprentissage pour AI-Edge Computing (PARFAIT)(2021)

Superior Title: https://inria.hal.science/hal-04181971 ; RR-9515, INRIA. 2023.

Subject Terms: Supervised Machine Learning, Worst-Case, Generalization Gap, Relative Entropy, Gibbs Algorithm, Sensitivity, Apprentissage automatique supervisé, pire cas, généralisation, entropie relative, algorithme de Gibbs, sensibilité, [MATH.MATH-IT]Mathematics [math]/Information Theory [math.IT], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]

Relation: Report N°: RR-9515; hal-04181971; https://inria.hal.science/hal-04181971; https://inria.hal.science/hal-04181971v2/document; https://inria.hal.science/hal-04181971v2/file/RR-9515.pdf

Availability: https://inria.hal.science/hal-04181971
https://inria.hal.science/hal-04181971v2/document
https://inria.hal.science/hal-04181971v2/file/RR-9515.pdf


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6
Report
The Worst-Case Data-Generating Probability Measure ; La mesure de probabilité génératrice de données dans le pire des cas

Authors: Zou, Xinying, Perlaza, Samir, M., Esnaola, Iñaki, Altman, Eitan

Contributors: Network Engineering and Operations (NEO ), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Department of Electrical and Computer Engineering Princeton (ECE), Princeton University, Laboratoire de Géométrie Algébrique et Applications à la Théorie de l'Information (GAATI), Université de la Polynésie Française (UPF), University of Sheffield Sheffield, Laboratoire Informatique d'Avignon (LIA), Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI, Laboratory of Information, Network and Communication Sciences (LINCS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Mines-Télécom Paris (IMT)-Sorbonne Université (SU), Inria Exploratory Action -- Information and Decision Making (IDEM), INRIA, ANR-21-CE25-0013,PARFAIT,Planification et apprentissage pour AI-Edge Computing (PARFAIT)(2021)

Superior Title: https://inria.hal.science/hal-04181971 ; RR-9515, INRIA. 2023, pp.29.

Subject Terms: Supervised Machine Learning, Worst-Case, Generalization Gap, Relative Entropy, Gibbs Algorithm, Sensitivity, Apprentissage automatique supervisé, pire cas, généralisation, entropie relative, algorithme de Gibbs, sensibilité, [MATH.MATH-IT]Mathematics [math]/Information Theory [math.IT], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]

Relation: Report N°: RR-9515; hal-04181971; https://inria.hal.science/hal-04181971; https://inria.hal.science/hal-04181971/document; https://inria.hal.science/hal-04181971/file/INRIA-RR9515.pdf

Availability: https://inria.hal.science/hal-04181971
https://inria.hal.science/hal-04181971/document
https://inria.hal.science/hal-04181971/file/INRIA-RR9515.pdf


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7
Dissertation/ Thesis
The social welfare of the farmer victim of accidents ; La protection sociale de l'agriculteur victime d'accidents

Authors: Meftah, Leïla

Contributors: Laboratoire Biens, Normes, Contrats (LBNC), Avignon Université (AU), Université d'Avignon, Franck Petit, Thierry Tauran

Superior Title: https://theses.hal.science/tel-02179002 ; Droit. Université d'Avignon, 2018. Français. ⟨NNT : 2018AVIG2065⟩.

Subject Terms: Social protection, Armers, Agricultural employees, Non agricultural employees, Accident at work, Road accident, Unforgivable fault, Income benefit, Work accident pension, Disparities of victims, Common-law accident, Daily allowances, Incapacity, Compensation, Compulsory insurance accident, Income deduction, Wage deduction, Generalisation full compensation, Protection sociale, Agriculteurs, Salariés agricoles, Non-salariés agricoles, Accident du travail, Accident du trajet, Réparation forfaitaire, Faute inexcusable, Revenu de remplacement, Rente accident du travail, Inégalités des victimes, Accident de droit commun

Relation: NNT: 2018AVIG2065; tel-02179002; https://theses.hal.science/tel-02179002; https://theses.hal.science/tel-02179002/document; https://theses.hal.science/tel-02179002/file/pdf2star-1562754458-THESE-FINIE2.pdf

Availability: https://theses.hal.science/tel-02179002
https://theses.hal.science/tel-02179002/document
https://theses.hal.science/tel-02179002/file/pdf2star-1562754458-THESE-FINIE2.pdf


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