Algorithms in high-dimensional non-convex landscapes | Vidéo | Carmin.tv

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Algorithms in high-dimensional non-convex landscapes

De Lenka Zdeborova

Apparaît dans la collection : AofA: Probabilistic, Combinatorial and Asymptotic Methods for the Analysis of Algorithms / AofA: méthodes probabilistes, combinatoires et asymptotiques pour l analyse d algorithmes

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.

Informations sur la vidéo

Date de captation 26/06/2019
Date de publication 26/07/2019
Institut CIRM
Licence CC BY NC ND
Langue Anglais
Audience Chercheurs
Réalisateur(s) Guillaume Hennenfent
Format MP4

Données de citation

DOI 10.24350/CIRM.V.19540503
Citer cette vidéo Zdeborova, Lenka (26/06/2019). Algorithms in high-dimensional non-convex landscapes. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19540503
URL https://dx.doi.org/10.24350/CIRM.V.19540503

Domaine(s)

Bibliographie

DIA, Mohamad, MACRIS, Nicolas, KRZAKALA, Florent, et al. Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula. In : Advances in Neural Information Processing Systems. 2016. p. 424-432 - http://papers.nips.cc/paper/6379-mutual-information-for-symmetric-rank-one-matrix-estimation-a-proof-of-the-replica-formula
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

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