[Colloquium] REMINDER: 8/31 Thesis Defense: Heejin Choi, TTIC

[Mary Marre via Colloquium]

 *Thesis Defense: Heejin Choi, TTIC*



*When:*      Friday, August 31st at *11:00am*

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

*Who:        *Heejin Choi, TTIC


*Title:        *Efficient Structured Surrogate Loss and Regularization in
Structured Prediction


*Abstract:*
In this dissertation, we focus on several important problems in structured
prediction. In structured prediction, the label has a rich intrinsic
substructure, and the loss varies with respect to predicted label and the
true label pair. Structured SVM is an extension of binary SVM to adapt to
such structured tasks.

In the first part of the dissertation, we study the surrogate losses and
its efficient methods. To minimize the empirical risk, a surrogate loss
which upper bounds the loss, is used as a proxy to minimize the actual
loss. Since the objective function is written in terms of the surrogate
loss, the choice of the surrogate loss is important since the performance
depends on it. Another issue regarding the surrogate loss is the efficiency
of the argmax label inference for the surrogate loss. The efficiency is
necessary for the optimization since it is often the most time-consuming
step. We present a new class of surrogate losses named a bi-criteria
surrogate loss, which is a generalization of the popular surrogate losses.
We first investigate an efficient method for a slack rescaling formulation
as a starting point utilizing decomposability of the model. Then, we extend
the algorithm for the bi-criteria surrogate loss, which is very efficient
and shows performance improvement.

In the second part of the dissertation, another important issue of
regularization is studied. Specifically, we investigate a problem of
regularization in hierarchical classification when a structural imbalance
exists in the label structure. We present a method to normalize the
structure, as well as a new norm, namely shared Frobenius norm. It is
suitable for hierarchical classification that adapts to the data in
addition to the label structure.



*Thesis advisor:* Nathan Srebro <nati at ttic.edu>





Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 517*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*

On Thu, Aug 30, 2018 at 1:54 PM, Mary Marre <mmarre at ttic.edu> wrote:

> *Thesis Defense: Heejin Choi, TTIC*
>
>
>
> *When:*      Friday, August 31st at *11:00am*
>
> *Where:    * TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>
> *Who:        *Heejin Choi, TTIC
>
>
> *Title:        *Efficient Structured Surrogate Loss and Regularization in
> Structured Prediction
>
>
> *Abstract:*
> In this dissertation, we focus on several important problems in structured
> prediction. In structured prediction, the label has a rich intrinsic
> substructure, and the loss varies with respect to predicted label and the
> true label pair. Structured SVM is an extension of binary SVM to adapt to
> such structured tasks.
>
> In the first part of the dissertation, we study the surrogate losses and
> its efficient methods. To minimize the empirical risk, a surrogate loss
> which upper bounds the loss, is used as a proxy to minimize the actual
> loss. Since the objective function is written in terms of the surrogate
> loss, the choice of the surrogate loss is important since the performance
> depends on it. Another issue regarding the surrogate loss is the efficiency
> of the argmax label inference for the surrogate loss. The efficiency is
> necessary for the optimization since it is often the most time-consuming
> step. We present a new class of surrogate losses named a bi-criteria
> surrogate loss, which is a generalization of the popular surrogate losses.
> We first investigate an efficient method for a slack rescaling formulation
> as a starting point utilizing decomposability of the model. Then, we extend
> the algorithm for the bi-criteria surrogate loss, which is very efficient
> and shows performance improvement.
>
> In the second part of the dissertation, another important issue of
> regularization is studied. Specifically, we investigate a problem of
> regularization in hierarchical classification when a structural imbalance
> exists in the label structure. We present a method to normalize the
> structure, as well as a new norm, namely shared Frobenius norm. It is
> suitable for hierarchical classification that adapts to the data in
> addition to the label structure.
>
>
>
> *Thesis advisor:* Nathan Srebro <nati at ttic.edu>
>
>
>
>
> Mary C. Marre
> Administrative Assistant
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 523*
> *Chicago, IL  60637*
> *p:(773) 834-1757*
> *f: (773) 357-6970*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
> On Tue, Aug 28, 2018 at 4:17 PM, Mary Marre <mmarre at ttic.edu> wrote:
>
>> *Thesis Defense: Heejin Choi, TTIC*
>>
>>
>>
>> *When:*      Friday, August 31st at *11:00am*
>>
>> *Where:    * TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>>
>> *Who:        *Heejin Choi, TTIC
>>
>>
>> *Title:        *Efficient Structured Surrogate Loss and Regularization
>> in Structured Prediction
>>
>>
>> *Abstract:*
>> In this dissertation, we focus on several important problems in
>> structured prediction. In
>> structured prediction, the label has a rich intrinsic substructure, and
>> the loss varies with
>> respect to predicted label and the true label pair. Structured SVM is an
>> extension of binary
>> SVM to adapt to such structured tasks.
>>
>> In the first part of the dissertation, we study the surrogate losses and
>> its efficient meth-
>> ods. To minimize the empirical risk, a surrogate loss which upper bounds
>> the loss, is used as a proxy to minimize the actual loss. Since the
>> objective function is written in terms of the surrogate loss, the choice of
>> the surrogate loss is important since the performance depends on it.
>> Another issue regarding the surrogate loss is the efficiency of the argmax
>> label inference for the surrogate loss. The efficiency is necessary for the
>> optimization since it is often the most time-consuming step. We present a
>> new class of surrogate losses named a bi-criteria surrogate loss, which is
>> a generalization of the popular surrogate losses. We first investigate an
>> efficient method for a slack rescaling formulation as a starting point
>> utilizing decomposability of the model. Then, we extend the algorithm for
>> the bi-criteria surrogate loss, which is very efficient and shows
>> performance improvement.
>>
>> In the second part of the dissertation, another important issue of
>> regularization is studied. Specifically, we investigate a problem of
>> regularization in hierarchical classification when a structural imbalance
>> exists in the label structure. We present a method to normalize the
>> structure, as well as a new norm, namely shared Frobenius norm. It is
>> suitable for hierarchical classification that adapts to the data in
>> addition to the label structure.
>>
>>
>>
>> *Thesis advisor:* Nathan Srebro <nati at ttic.edu>
>>
>>
>>
>> Mary C. Marre
>> Administrative Assistant
>> *Toyota Technological Institute*
>> *6045 S. Kenwood Avenue*
>> *Room 523*
>> *Chicago, IL  60637*
>> *p:(773) 834-1757*
>> *f: (773) 357-6970*
>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>
>
>
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