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OPT2020: Optimization for Machine Learning
Courtney Paquette · Mark Schmidt · Sebastian Stich · Quanquan Gu · Martin Takac

Fri Dec 11 03:15 AM -- 04:30 PM (PST) @ None
Event URL: https://opt-ml.org/index.html »

Optimization lies at the heart of many machine learning algorithms and enjoys great interest in our community. Indeed, this intimate relation of optimization with ML is the key motivation for the OPT series of workshops.

Looking back over the past decade, a strong trend is apparent: The intersection of OPT and ML has grown to the point that now cutting-edge advances in optimization often arise from the ML community. The distinctive feature of optimization within ML is its departure from textbook approaches, in particular, its focus on a different set of goals driven by "big-data, nonconvexity, and high-dimensions," where both theory and implementation are crucial.

We wish to use OPT 2020 as a platform to foster discussion, discovery, and dissemination of the state-of-the-art in optimization as relevant to machine learning. And well beyond that: as a platform to identify new directions and challenges that will drive future research, and continue to build the OPT+ML joint research community.

Invited Speakers
Volkan Cevher (EPFL)
Michael Friedlander (UBC)
Donald Goldfarb (Columbia)
Andreas Krause (ETH, Zurich)
Suvrit Sra (MIT)
Rachel Ward (UT Austin)
Ashia Wilson (MSR)
Tong Zhang (HKUST)

Please join us in gather.town for all breaks and poster sessions (Click "Open Link" on any break or poster session).

To see all submitted paper and posters, go to the "opt-ml website" at the top of the page.

Use RocketChat or Zoom link (top of page) if you want to ask the speaker a direct question during the Live Q&A and Contributed Talks.

Author Information

Courtney Paquette (Lehigh University)
Mark Schmidt (University of British Columbia)
Sebastian Stich (EPFL)

Dr. [Sebastian U. Stich](https://sstich.ch/) is a faculty at the CISPA Helmholtz Center for Information Security. Research interests: - *methods for machine learning and statistics*—at the interface of theory and practice - *collaborative learning* (distributed, federated and decentralized methods) - *optimization for machine learning* (adaptive stochastic methods and generalization performance)

Quanquan Gu (UCLA)
Martin Takac (Lehigh University)

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