Statistical Learning and Data Mining
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Fall 2014
All meetings will be held every
other Tuesday between 11:10 am - 12:30 pm in CH212.
We will start with Optimization for Statistics as one of the themes of Fall 2014.
Presentation and discussion schedule will be posted as we go along.
- September 9, 2014
Speaker: Andrew Landgraf
Title: Data Science for Social Good
- September 23, 2014
Speakers: Vince Vu, Yoon Lee, and Yunzhang Zhu
Title: Selected Examples of Optimization from Lange et al. (2014)
Reading groups
Newton: Qian Qian, Abhijoy Saha, Shanshan Tu, Weiyi Xie, Jiaqi (Jessie) Zaetz*, Sebastian Kurtek MM: Andrew Landgraf*, Yi Lu, Huong (Sophie) Nguyen, Ran Wei, Yuan Zhang, Yoon Lee Lagrange: Liubo Li*, Yinghao Sun, Zhifei Yan, Vince Vu, Yunzhang Zhu - October 7, 2014
Weiyi Xie & Jessie Zaetz: Quasi-Newton Methods Yi Lu: Tricks of the Trade from A Tutorial on MM Algorithms
Liubo Li: Introduction to ADMM algorithm
- October 21, 2014
Title: Comparing Optimization Techniques in the NBA Team Strength Example from A Tutorial on MM Algorithms
Discussants: Sophie Nguyen, Qian Qian, Shanshan Tu, and Zhifei Yan
Data - November 4, 2014
No Meeting
- November 18, 2014
Jessie Zaetz: Conjugate Gradient Method Andrew Landgraf: Convergence of the MM Algorithm from Section 12.4 of Optimization
Liubo Li: ADMM for the Lasso Problem from Chapter 6 of Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers - December 2, 2014
Abhijoy Saha: Non Linear Conjugate Gradient Method Yi Lu & Sophie Nguyen: MM Algorithm for Image Debluring from Adaptative Total Variation Image Deblurring: A Majorization-Minimization Approach
Zhifei Yan: Dynamically Updating the Penalty Parameter (ρ) in ADMM