: TTIC 41000
: Karl Stratos
: M 3-4:20pm
: TTIC Room 526
: M 4:30-5pm
This special topics course will examine techniques that use eigenvalues/eigenvectors of a matrix (and more generally, any linear algebraic tools) to solve or understand problems in modern machine learning.
The course will be accompanied by lectures on technical materials required to understand and derive spectral techniques.
As part of the course, students will take turn to present papers on recent research in this area.
Audience and prerequisites.
- Achieving an understanding of the foundational concepts and tools used in modern spectral methods
- Obtaining an ability to accurately evaluate new works in this area at conferences
- Finding new research projects that persist beyond this course
The expected audience is student researchers in machine learning who are interested in learning about and doing novel research on this topic.
The main prerequisite is general mathematical maturity and familiarity with basic notions in linear algebra, statistics, and optimization.
The course will be pass-fail. Students who actively engage in the class will receive a pass.
A significant part of this evaluation will also be the technical quality of the student presentation.
||Introduction, Vector Space Review
|Oct 3 (Wed)
||Hilbert Space, Matrix Decomposition (Dudeja and Hsu, 2018)
||Spectral Learning of HMMs (Hsu et al., 2008; Foster et al., 2012; Balle et al., 2014)
||Canonical Correlation Analysis (Golub and Zha, 1992; Andrew et al., 2013)
Some notes on deep CCA
Comparing matrix ranges
||Paper Discussion (Wang et al., 2015b)
||Paper Discussion (Chapter 2 of Peyre and Cuturi)
||Paper Discussion (Chapter 4 of Peyre and Cuturi; Peyre et al., 2016; Alvarez-Melis and Jaakkola, 2018; Zhang et al., 2017)
||Paper Discussion (Von Luxburg, 2007; Belkin and Niyogi, 2002)
||Paper Discussion (Sedoc et al., 2017; Halko et al., 2011)
||Paper Discussion (Goodfellow et al., 2014; Arjovsky et al., 2017; Arora et al., 2018; Miyato et al., 2018)
||Paper Discussion (Anandkumar et al., 2014; Sedghi et al., 2016)
Please expand recursively for a more complete list.
|Analysis of CCA
- A spectral algorithm for learning mixture models (Vempala and Wang, 2004)
- On Spectral Learning of Mixtures of Distributions (Achlioptas and McSherry, 2005)
- A Spectral Algorithm for Learning Hidden Markov Models (Hsu et al., 2008)
- Toward Learning Gaussian Mixtures with Arbitrary Separation (Belkin and Sinha, 2010)
- Spectral dimensionality reduction for HMMs (Foster et al., 2012)
- Learning mixtures of spherical Gaussians: moment methods and spectral decompositions (Hsu and Kakade, 2012)
- Hilbert Space Embeddings of Predictive State Representations (Boots et al., 2013)
- Generalized Topic Modeling (Blum and Haghtalab, 2016)
- Multitask Spectral Learning of Weighted Automata (Rabusseau et al., 2017)
- Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning (Rabusseau et al., 2018)
- Learning Single-Index Models in Gaussian Space (Dudeja and Hsu, 2018)