[Colloquium] REMINDER: IDEAL Seminar 4/13: Mingrui Liu, Iowa State

[Alicia McClarin]

*When:*     Monday April 13th at 3:00pm

*Where:*
https://northwestern.zoom.us/j/501586978?pwd=RHlHd3pLUDRXb3ZwU2NjUjdxMmNGZz09;
password: ideal2020

*Who: *      Mingrui Liu, Iowa State

*Title: *      Min-max Optimization in Large-scale Machine Learning:
Provable Algorithms with Fast Non-asymptotic Convergence

*Abstract:* Min-max optimization receives increasing attention in machine
learning, in settings such as stochastic AUC maximization and Generative
Adversarial Nets (GANs). However, the de facto optimization algorithms
people usually use for solving these min-max games in practice either
suffer from slow convergence rate or lack theoretical guarantees. A natural
question is proposed---how to design provable algorithms with fast
non-asymptotic convergence for machine learning problems with min-max
formulation?

To answer this question, I will focus on the following four concrete and
fundamental questions:

1. In stochastic AUC maximization with linear model, is it possible to
design a provably faster algorithm than the traditional the stochastic
primal-dual gradient method?

2. In stochastic AUC maximization with deep neural network, how to design a
novel algorithm with state-of-the-art convergence rate?

3. Although adaptive gradient methods with alternate update empirically
work well in training GANs, it requires expensive tuning efforts, lacks
theoretical convergence guarantees and might diverge in practice. Is it
possible to design adaptive gradient algorithms for training GANs with
provably faster convergence than its non-adaptive counterpart?

4. Decentralized parallel algorithms are robust to network bandwidth and
latency compared with its centralized counterpart. Is it possible to train
GANs in decentralized distributed manner with provable guarantees?

In this talk, I will present my recent work to provide all positive answers
to these questions.

*Bio:       *Mingrui Liu is currently a 4th year Ph.D. candidate in the
Department of Computer Science at the University of Iowa, under the
supervision of Prof. Tianbao Yang. He has board interests in machine
learning and has focused on several research topics, including large-scale
optimization in machine learning and learning theory. His recent research
focus is to design provably efficient algorithms for nonconvex min-max
problems in machine learning.

www.ideal.northwestern.edu

-- 
*Alicia McClarin*
*Toyota Technological Institute at Chicago*
*6045 S. Kenwood Ave., **Office 504*
*Chicago, IL 60637*
*773-834-3321*
*www.ttic.edu* <http://www.ttic.edu/>
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