English
Algorithms in high-dimensional non-convex landscapes
Multi angle
Auteurs : Zdeborova, Lenka (Auteur de la conférence)
CIRM (Editeur )
62P35 - Applications to physics
68T05 - Learning and adaptive systems
68W25 - Approximation algorithms
CIRM (Editeur )
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Résumé :
Analysis of algorithms in noisy high-dimensional probabilistic problems poses many current challenges. In a subclass of these problems the corresponding challenges can be overcome with the help of a method coming from statistical mechanics. I will review some of the related recent work together with progress on rigorous justification of the corresponding results.
Mots-Clés : machine learning; optimization; gradiant descent; statistical physics
Codes MSC : Mots-Clés : machine learning; optimization; gradiant descent; statistical physics
62P35 - Applications to physics
68T05 - Learning and adaptive systems
68W25 - Approximation algorithms
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Informations sur la Vidéo
Réalisateur : Hennenfent, GuillaumeLangue : Anglais Date de Publication : 26/07/2019 Date de Captation : 26/06/2019 Sous Collection : Research talks Catégorie arXiv : Machine Learning ; Condensed Matter ; Statistics Theory ; Disordered Systems and Neural Networks Domaine(s) : Informatique ; Théorie du Contrôle & Optimisation ; Géométrie ; Probabilités & Statistiques Format : MP4 (.mp4) - HD Durée : 00:56:41 Audience : Chercheurs Download : https://videos.cirm-math.fr/2019-06-26_Zderborova.mp4 
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Informations sur la Rencontre
Nom de la Rencontre : AofA: Probabilistic, Combinatorial and Asymptotic Methods for the Analysis of Algorithms / AofA: méthodes probabilistes, combinatoires et asymptotiques pour l analyse d algorithmesOrganisateurs de la Rencontre : Bassino, Frédérique ; Martínez, Conrado ; Salvy, Bruno Dates : 24/06/2019 - 28/06/2019 Année de la rencontre : 2019 URL de la Rencontre : https://conferences.cirm-math.fr/1940.html
Données de citation
DOI : 10.24350/CIRM.V.19540503Citer cette vidéo: Zdeborova, Lenka (2019). Algorithms in high-dimensional non-convex landscapes. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19540503 URI : http://dx.doi.org/10.24350/CIRM.V.19540503 |
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Bibliographie
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- LESIEUR, Thibault, KRZAKALA, Florent, et ZDEBOROVÁ, Lenka. Constrained low-rank matrix estimation: Phase transitions, approximate message passing and applications. Journal of Statistical Mechanics: Theory and Experiment, 2017, vol. 2017, no 7, p. 073403. - https://doi.org/10.1088/1742-5468/aa7284
- LESIEUR, Thibault, MIOLANE, Léo, LELARGE, Marc, et al. Statistical and computational phase transitions in spiked tensor estimation. In : 2017 IEEE International Symposium on Information Theory (ISIT). IEEE, 2017. p. 511-515. - https://doi.org/10.1109/ISIT.2017.8006580
- MANNELLI, Stefano Sarao, BIROLI, Giulio, CAMMAROTA, Chiara, et al. Marvels and pitfalls of the langevin algorithm in noisy high-dimensional inference. arXiv preprint arXiv:1812.09066, 2018. - https://arxiv.org/abs/1812.09066
- MANNELLI, Stefano Sarao, KRZAKALA, Florent, URBANI, Pierfrancesco, et al. Passed & Spurious: analysing descent algorithms and local minima in spiked matrix-tensor model. arXiv preprint arXiv:1902.00139, 2019. - https://arxiv.org/abs/1902.00139
