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

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Algorithms in high-dimensional non-convex landscapes

By Lenka Zdeborova

Appears in 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.

Information about the video

Date of recording 26/06/2019
Date of publication 26/07/2019
Institution CIRM
Licence CC BY NC ND
Language English
Audience Researchers
Director(s) Guillaume Hennenfent
Format MP4

Citation data

DOI 10.24350/CIRM.V.19540503
Cite this video 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

Domain(s)

Bibliography

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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

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