Variational characterizations of divergence: Donsker-Varadhan

Speaker: 

Kathryn Dover

Institution: 

UC Irvine

Time: 

Tuesday, January 28, 2020 - 2:00pm to 2:50pm

Location: 

RH 510R

This week, we will discuss Section 3.3 of the lecture notes of Wu and Polyanski:

http://people.lids.mit.edu/yp/homepage/papers.html

Working Group in Information Theory is a self-educational project in the department. Techniques based on information theory have become essential in high-dimensional probability, theoretical computer science and statistical learning theory. On the other hand, information theory is not taught systematically. The goal of this group is to close this gap.

Geometric interpretation of mutual information

Speaker: 

Sonky Ung

Institution: 

UC Irvine

Time: 

Tuesday, January 21, 2020 - 2:00pm to 2:50pm

Location: 

RH 510R

This week, we will discuss Section 3.2 of the lecture notes of Wu and Polyanski:

http://people.lids.mit.edu/yp/homepage/papers.html

Working Group in Information Theory is a self-educational project in the department. Techniques based on information theory have become essential in high-dimensional probability, theoretical computer science and statistical learning theory. On the other hand, information theory is not taught systematically. The goal of this group is to close this gap.

Sufficient statistics and data-processing

Speaker: 

Amirhossein Taghvaei

Institution: 

UC Irvine

Time: 

Tuesday, January 14, 2020 - 2:00pm to 2:50pm

Location: 

RH 510R

This week, we will discuss Section 2.5 and Section 3.1 of the lecture notes of Wu and Polyanski:

http://people.lids.mit.edu/yp/homepage/papers.html

Working Group in Information Theory is a self-educational project in the department. Techniques based on information theory have become essential in high-dimensional probability, theoretical computer science and statistical learning theory. On the other hand, information theory is not taught systematically. The goal of this group is to close this gap.

Mutual Information

Speaker: 

Kevin Bui

Institution: 

UCI

Time: 

Tuesday, December 3, 2019 - 2:00pm to 3:00pm

Location: 

RH 510R

This week, we will continue to discuss Section 2.3 of the lecture notes of Wu and Polyanski:

http://people.lids.mit.edu/yp/homepage/papers.html

Working Group in Information Theory is a self-educational project in the department. Techniques based on information theory have become essential in high-dimensional probability, theoretical computer science and statistical learning theory. On the other hand, information theory is not taught systematically. The goal of this group is to close this gap.

Conditional Divergence

Speaker: 

Liam Hardiman

Institution: 

UCI

Time: 

Tuesday, November 19, 2019 - 2:00pm to 3:00pm

Location: 

RH 510R

This week, we will finish our discussion of Section 2.2 of the lecture notes of Wu and Polyanski: 
http://people.lids.mit.edu/yp/homepage/papers.html

Working Group in Information Theory is a self-educational project in the department. Techniques based on information theory have become essential in high-dimensional probability, theoretical computer science and statistical learning theory. On the other hand, information theory is not taught systematically. The goal of this group is to close this gap.

Conditional Divergence

Speaker: 

Liam Hardiman

Institution: 

UCI

Time: 

Tuesday, November 12, 2019 - 2:00pm to 3:00pm

Location: 

RH 510R

This week, we will continue to discuss Section 2.2 of the lecture notes of Wu and Polyanski: 
http://people.lids.mit.edu/yp/homepage/papers.html

Working Group in Information Theory is a self-educational project in the department. Techniques based on information theory have become essential in high-dimensional probability, theoretical computer science and statistical learning theory. On the other hand, information theory is not taught systematically. The goal of this group is to close this gap.

Conditional Divergence

Speaker: 

Liam Hardiman

Institution: 

UC Irvine

Time: 

Tuesday, November 5, 2019 - 2:00pm to 3:00pm

Location: 

RH 510R

This week, we will continue to discuss Section 2.1-2.2 of the lecture notes of Wu and Polyanski: 
http://people.lids.mit.edu/yp/homepage/papers.html

Working Group in Information Theory is a self-educational project in the department. Techniques based on information theory have become essential in high-dimensional probability, theoretical computer science and statistical learning theory. On the other hand, information theory is not taught systematically. The goal of this group is to close this gap.

Pinsker’s Inequality and Differential Entropy

Speaker: 

John Peca-Medlin

Institution: 

UCI

Time: 

Tuesday, October 29, 2019 - 2:00pm

Location: 

RH 510R

This week, we will continue to discuss Section 1.6 and 1.7 of the lecture notes of Wu and Polyanski: 
http://people.lids.mit.edu/yp/homepage/papers.html

Working Group in Information Theory is a self-educational project in the department. Techniques based on information theory have become essential in high-dimensional probability, theoretical computer science and statistical learning theory. On the other hand, information theory is not taught systematically. The goal of this group is to close this gap.

Information divergence and differential entropy

Speaker: 

John Peca-Medlin

Institution: 

UCI

Time: 

Tuesday, October 22, 2019 - 2:00pm to 3:00pm

Location: 

510R

This week, we will discuss Section 1.6 and 1.7 of the lecture notes of Wu and Polyanski: 
http://people.lids.mit.edu/yp/homepage/papers.html

Working Group in Information Theory is a self-educational project in the department. Techniques based on information theory have become essential in high-dimensional probability, theoretical computer science and statistical learning theory. On the other hand, information theory is not taught systematically. The goal of this group is to close this gap.

 Submodularity of Entropy, Han's Inequality, and Shearer’s Lemma

Speaker: 

Kathryn Dover

Institution: 

UCI

Time: 

Tuesday, October 15, 2019 - 2:00pm to 3:00pm

Location: 

510R

Working Group in Information Theory is a self-educational project in the department. Techniques based on information theory have become essential in high-dimensional probability, theoretical computer science and statistical learning theory. On the other hand, information theory is not taught systematically. The goal of this group is to close this gap.

This week, we will discuss Section 1.4 and 1.5 of the lecture notes of Wu and Polyanski: 
http://people.lids.mit.edu/yp/homepage/papers.html

 

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