[Colloquium] REMINDER: 10/2 TTIC Colloquium: Ilias Diakonikolas, University of Southern California

[Mary Marre via Colloquium]

When:     Monday, October 2nd at 11:00 a.m.

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

Who:       Ilias Diakonikolas, University of Southern California


Title: Computational Efficiency and High-Dimensional Robust Statistics

Abstract: Fitting a model to a collection of observations is a prototypical
problem in machine learning. Since any model is only approximately valid,
an estimator that is useful in practice must also be robust in the presence
of model misspecification. It turns out that there is a striking tension
between robustness and computational efficiency. In even the most basic
high-dimensional settings, such as robustly computing the mean and
covariance, until recently the only known estimators were either hard to
compute or could only tolerate a negligible fraction of errors.

In this talk, we will survey the recent progress in algorithmic
high-dimensional robust statistics. We will describe the first robust and
computationally efficient algorithms for several fundamental estimation
problems. We will also present practical applications to exploratory data
analysis. Finally, we will touch upon computational and statistical limits
of robust estimation.

The talk will be based on the following papers:

https://arxiv.org/abs/1604.06443 (FOCS 2016)

https://arxiv.org/pdf/1703.00893.pdf (ICML 2017)

https://arxiv.org/abs/1611.03473 (FOCS 2017)

********************************************
Bio: Ilias Diakonikolas is an Assistant Professor and Andrew and Erna
Viterbi Early Career Chair in the Department of Computer Science at USC. He
obtained a Diploma in electrical and computer engineering from the National
Technical University of Athens and a Ph.D. in computer science from
Columbia University where he was advised by Mihalis Yannakakis.

Before moving to USC, he was a faculty member at the University of
Edinburgh, and prior to that he was the Simons postdoctoral fellow in
theoretical computer science at the University of California, Berkeley. His
research is on the algorithmic foundations of massive data sets, in
particular on designing efficient algorithms for fundamental problems in
machine learning. He is a recipient of a Sloan Fellowship, an NSF Career
Award, a Google Faculty Research Award, a Marie Curie Fellowship, the IBM
Research Pat Goldberg Best Paper Award, and an honorable mention in the
George Nicholson competition from the INFORMS society.


Host: Julia Chuzhoy <cjulia at ttic.edu>


For more information on the colloquium series or to subscribe to the
mailing list, please see http://www.ttic.edu/colloquium.php


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 Sun, Oct 1, 2017 at 7:30 PM, Mary Marre <mmarre at ttic.edu> wrote:

> When:     Monday, October 2nd at 11:00 a.m.
>
> Where:    TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>
> Who:       Ilias Diakonikolas, University of Southern California
>
>
> Title: Computational Efficiency and High-Dimensional Robust Statistics
>
> Abstract: Fitting a model to a collection of observations is a
> prototypical problem in machine learning. Since any model is only
> approximately valid, an estimator that is useful in practice must also be
> robust in the presence of model misspecification. It turns out that there
> is a striking tension between robustness and computational efficiency. In
> even the most basic high-dimensional settings, such as robustly computing
> the mean and covariance, until recently the only known estimators were
> either hard to compute or could only tolerate a negligible fraction of
> errors.
>
> In this talk, we will survey the recent progress in algorithmic
> high-dimensional robust statistics. We will describe the first robust and
> computationally efficient algorithms for several fundamental estimation
> problems. We will also present practical applications to exploratory data
> analysis. Finally, we will touch upon computational and statistical limits
> of robust estimation.
>
> The talk will be based on the following papers:
>
> https://arxiv.org/abs/1604.06443 (FOCS 2016)
>
> https://arxiv.org/pdf/1703.00893.pdf (ICML 2017)
>
> https://arxiv.org/abs/1611.03473 (FOCS 2017)
>
> ********************************************
> Bio: Ilias Diakonikolas is an Assistant Professor and Andrew and Erna
> Viterbi Early Career Chair in the Department of Computer Science at USC. He
> obtained a Diploma in electrical and computer engineering from the National
> Technical University of Athens and a Ph.D. in computer science from
> Columbia University where he was advised by Mihalis Yannakakis.
>
> Before moving to USC, he was a faculty member at the University of
> Edinburgh, and prior to that he was the Simons postdoctoral fellow in
> theoretical computer science at the University of California, Berkeley. His
> research is on the algorithmic foundations of massive data sets, in
> particular on designing efficient algorithms for fundamental problems in
> machine learning. He is a recipient of a Sloan Fellowship, an NSF Career
> Award, a Google Faculty Research Award, a Marie Curie Fellowship, the IBM
> Research Pat Goldberg Best Paper Award, and an honorable mention in the
> George Nicholson competition from the INFORMS society.
>
>
> Host: Julia Chuzhoy <cjulia at ttic.edu>
>
>
> For more information on the colloquium series or to subscribe to the
> mailing list, please see http://www.ttic.edu/colloquium.php
>
>
> Mary C. Marre
> Administrative Assistant
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 504*
> *Chicago, IL  60637*
> *p:(773) 834-1757 <(773)%20834-1757>*
> *f: (773) 357-6970 <(773)%20357-6970>*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
> On Tue, Sep 26, 2017 at 1:09 PM, Mary Marre <mmarre at ttic.edu> wrote:
>
>> When:     Monday, October 2nd at 11:00 a.m.
>>
>> Where:    TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>>
>> Who:       Ilias Diakonikolas, University of Southern California
>>
>>
>> Title: Computational Efficiency and High-Dimensional Robust Statistics
>>
>> Abstract: Fitting a model to a collection of observations is a
>> prototypical problem in machine learning. Since any model is only
>> approximately valid, an estimator that is useful in practice must also be
>> robust in the presence of model misspecification. It turns out that there
>> is a striking tension between robustness and computational efficiency. In
>> even the most basic high-dimensional settings, such as robustly computing
>> the mean and covariance, until recently the only known estimators were
>> either hard to compute or could only tolerate a negligible fraction of
>> errors.
>>
>> In this talk, we will survey the recent progress in algorithmic
>> high-dimensional robust statistics. We will describe the first robust and
>> computationally efficient algorithms for several fundamental estimation
>> problems. We will also present practical applications to exploratory data
>> analysis. Finally, we will touch upon computational and statistical limits
>> of robust estimation.
>>
>> The talk will be based on the following papers:
>>
>> https://arxiv.org/abs/1604.06443 (FOCS 2016)
>>
>> https://arxiv.org/pdf/1703.00893.pdf (ICML 2017)
>>
>> https://arxiv.org/abs/1611.03473 (FOCS 2017)
>>
>> ********************************************
>> Bio: Ilias Diakonikolas is an Assistant Professor and Andrew and Erna
>> Viterbi Early Career Chair in the Department of Computer Science at USC. He
>> obtained a Diploma in electrical and computer engineering from the National
>> Technical University of Athens and a Ph.D. in computer science from
>> Columbia University where he was advised by Mihalis Yannakakis.
>>
>> Before moving to USC, he was a faculty member at the University of
>> Edinburgh, and prior to that he was the Simons postdoctoral fellow in
>> theoretical computer science at the University of California, Berkeley. His
>> research is on the algorithmic foundations of massive data sets, in
>> particular on designing efficient algorithms for fundamental problems in
>> machine learning. He is a recipient of a Sloan Fellowship, an NSF Career
>> Award, a Google Faculty Research Award, a Marie Curie Fellowship, the IBM
>> Research Pat Goldberg Best Paper Award, and an honorable mention in the
>> George Nicholson competition from the INFORMS society.
>>
>>
>> Host: Julia Chuzhoy <cjulia at ttic.edu>
>>
>>
>> For more information on the colloquium series or to subscribe to the
>> mailing list, please see http://www.ttic.edu/colloquium.php
>>
>>
>> Mary C. Marre
>> Administrative Assistant
>> *Toyota Technological Institute*
>> *6045 S. Kenwood Avenue*
>> *Room 504*
>> *Chicago, IL  60637*
>> *p:(773) 834-1757 <(773)%20834-1757>*
>> *f: (773) 357-6970 <(773)%20357-6970>*
>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>
>
>
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