[Colloquium] REMINDER: 3/30 Talks at TTIC: Tuo Zhao, Johns Hopkins University

[Mary Marre]

When:     Wednesday, March 30th at 11:00 am

Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526

Who:       Tuo Zhao, Johns Hopkins University


Title:       Compute Faster and Learn Better: Machine Learning via
Nonconvex Model-based Optimization

Abstract: Nonconvex optimization naturally arises in many machine learning
problems (e.g. sparse learning, matrix factorization, and tensor
decomposition). Machine learning researchers exploit various nonconvex
formulations to gain modeling flexibility, estimation robustness,
adaptivity, and computational scalability. Although classical computational
complexity theory has shown that solving nonconvex optimization is
generally NP-hard in the worst case, practitioners have proposed numerous
heuristic optimization algorithms, which achieve outstanding empirical
performance in real-world applications.

To bridge this gap between practice and theory, we propose a new generation
of model-based optimization algorithms and theory, which incorporate the
statistical thinking into modern optimization. Particularly, when designing
practical computational algorithms, we take the underlying statistical
models into consideration (e.g. sparsity, low rankness). Our novel
algorithms exploit hidden geometric structures behind many nonconvex
optimization problems, and can obtain global optima with the desired
statistics properties in polynomial time with high probability.


Host: Nathan Srebro, nati at ttic.edu


Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 504*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*

On Tue, Mar 29, 2016 at 1:26 PM, Mary Marre <mmarre at ttic.edu> wrote:

> When:     Wednesday, March 30th at 11:00 am
>
> Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526
>
> Who:       Tuo Zhao, Johns Hopkins University
>
>
> Title:       Compute Faster and Learn Better: Machine Learning via
> Nonconvex Model-based Optimization
>
> Abstract: Nonconvex optimization naturally arises in many machine
> learning problems (e.g. sparse learning, matrix factorization, and tensor
> decomposition). Machine learning researchers exploit various nonconvex
> formulations to gain modeling flexibility, estimation robustness,
> adaptivity, and computational scalability. Although classical computational
> complexity theory has shown that solving nonconvex optimization is
> generally NP-hard in the worst case, practitioners have proposed numerous
> heuristic optimization algorithms, which achieve outstanding empirical
> performance in real-world applications.
>
> To bridge this gap between practice and theory, we propose a new
> generation of model-based optimization algorithms and theory, which
> incorporate the statistical thinking into modern optimization.
> Particularly, when designing practical computational algorithms, we take
> the underlying statistical models into consideration (e.g. sparsity, low
> rankness). Our novel algorithms exploit hidden geometric structures behind
> many nonconvex optimization problems, and can obtain global optima with the
> desired statistics properties in polynomial time with high probability.
>
>
> Host: Nathan Srebro, nati at ttic.edu
>
>
> Mary C. Marre
> Administrative Assistant
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 504*
> *Chicago, IL  60637*
> *p:(773) 834-1757 <%28773%29%20834-1757>*
> *f: (773) 357-6970 <%28773%29%20357-6970>*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
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