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[5th EPFL CIS - RIKEN AIP Joint Seminar] Talks by Martin Jaggi, EPFL CIS

Wed, 17 Nov 2021 18:00 - 19:00 JST
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-Time Zone:JST -The seats are available on a first-come-first-served basis. -When the seats are fully booked, we may stop accepting applications. -Simultaneous interpretation will not be available.

Description

EPFL CIS and RIKEN AIP started a seminar, titled “EPFL CIS - RIKEN AIP Joint Seminar series" from October, 2021.

EPFL is located in Switzerland and is one of the most vibrant and cosmopolitan science and technology institutions. EPFL has both a Swiss and international vocation and focuses on three missions: teaching, research and innovation.

The Center for Intelligent Systems (CIS) at EPFL, a joint initiative of the schools ENAC, IC, SB, STI and SV seeks to advance research and practice in the strategic field of intelligent systems.

RIKEN is Japan's largest comprehensive research institution renowned for high-quality research in a diverse range of scientific disciplines.

RIKEN Center for Advanced Intelligence Project (AIP) houses more than 40 research teams ranging from fundamentals of machine learning and optimization, applications in medicine, materials, and disaster, to analysis of ethics and social impact of artificial intelligence.


【The 5th Seminar】


Date and Time: November 17th 6:00pm – 7:00pm(JST)
10:00am-11:00pm(CEST)
Venue:Zoom webinar

Language: English

Speaker: Martin Jaggi, EPFL CIS

Title: Learning with Strange Gradients

Abstract:
Gradient methods form the foundation of current machine learning. A vast literature covers the use of stochastic gradients being simple unbiased estimators of the full gradient of our objective. In this talk, we discuss four applications motivated from practical machine learning, where this key assumption is violated, and show new ways to cope with gradients which are only loosely related to the original objective. We demonstrate that algorithms with rigorous convergence guarantees can still be obtained in such settings, for
1) federated learning on heterogeneous data,
2) personalized collaborative learning,
3) masked training of neural networks with partial gradients,
4) learning with malicious participants, in the sense of Byzantine robust training.

Bio:
Martin Jaggi is a Tenure Track Assistant Professor at EPFL, heading the Machine Learning and Optimization Laboratory. Before that, he was a post-doctoral researcher at ETH Zurich, at the Simons Institute in Berkeley, and at École Polytechnique in Paris. He has earned his PhD in Machine Learning and Optimization from ETH Zurich in 2011, and a MSc in Mathematics also from ETH Zurich. He is a Fellow of ELLIS, and a co-founder of EPFL's Applied Machine Learning Days.


All participants are required to agree with the AIP Seminar Series Code of Conduct.
Please see the URL below.
https://aip.riken.jp/event-list/termsofparticipation/?lang=en

RIKEN AIP will expect adherence to this code throughout the event. We expect cooperation from all participants to help ensure a safe environment for everybody.


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