Arnaud LEGRAND – POLARIS

Articles posted by Arnaud LEGRAND

DATAMOVE/POLARIS BBQ 2019

Arnaud LEGRAND 2019/06/14 2019/06/14News

Super barbecue avec toutes les familles, les nouveaux, les anciens, …

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POLARIS Bootcamp (May 2019)

Arnaud LEGRAND 2019/05/24 2019/06/14News

Internal use: the pad where we took notes on the organization. Les photos Long talks:

Arnaud Legrand: Introduction to Bayesian sampling (sources)
Bary Pradelski: Preferences, Utilities and Identity Economics
Nicolas Gast: Bandits and online …
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News
- DATAMOVE/POLARIS BBQ 2019 2019/06/14
- POLARIS Bootcamp (May 2019) 2019/05/24
- slides of Andras Gyorgy 2016/01/15
- kickoff meeting on Feb. 11 2016/01/13
Next seminars
- 2:00 pm – 3:00 pm, January 23, 2020 – Can random matrices change the future of machine learning? by Romain Couillet (Gipsa)
- 2:30 pm – 4:00 pm, January 30, 2020 – HDR Nicolas Gast
- 2:00 pm – 3:00 pm, February 6, 2020 – keynote LIG

January 2020
M	T	W	T	F	S	S
		1	2	3	4	5
6	7	8	9 Keynote: Radu Horaud January 9, 2020 2:00 pm – 3:00 pm Read more	10	11	12
13	14	15	16 Capacity of a LoRaWAN cell, by Martin Heusse (Drakkar) January 16, 2020 2:00 pm – 3:00 pm Bâtiment IMAG (406) Saint-Martin-d'Hères, 38400 France Map We propose a model to estimate the packet delivery rate in a LoRaWAN cell, when all nodes have the same traffic generation process and may use repetitions. The model predicts the transmission success rate for any cell range and node density, with similar traffic from all nodes. We find that the transmission success depends on striking a balance between the adverse effects of attenuation and collisions; in small cells, it is highly dependent on the suitable allocation of spreading factors, whereas using packet repetitions is more effective in large cells. Read more	17	18	19
20	21	22	23 Can random matrices change the future of machine learning? by Romain Couillet (Gipsa) January 23, 2020 2:00 pm – 3:00 pm Romain COUILLET (professor at CentraleSupélec, University ParisSaclay; IDEX GSTATS Chair & MIAI LargeDATA Chair, University Grenoble-Alpes) Title: Can random matrices change the future of machine learning? Abstract: Many standard machine learning algorithms and intuitions are known to misbehave, if not dramatically collapse, when operated on large dimensional data. In this talk, we will show that large dimensional statistics, and particularly random matrix theory, not only can elucidate this behavior but provides a new set of tools to understand and (sometimes drastically) improve machine learning algorithms. Besides, we will show that our various theoretical findings are provably applicable to very realistic and not-so-large dimensional data. Read more	24	25	26
27	28	29	30 HDR Nicolas Gast January 30, 2020 2:30 pm – 4:00 pm Bâtiment IMAG (amphitheater) Saint-Martin-d'Hères, 38400 France Map TBA Read more	31

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