Introduction

The Machine Learning Theory Reading Group (MLTRG) meets regularly (usually weekly) to talk about recent research. In terms 1 and 2, we focus on a particular topic. In the summer, we discuss a variety of papers with less cohesiveness.

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Reading group schedules


Winter 2017 Every Wednesday in room ICICS 304 from 1-3pm
Date Presenter Topic
Jan. 11 Chris Rademacher complexity
Jan. 18 Devon and Raunak VC Dimension
Jan. 25 Bader On the mathematical foundations of learning
Feb. 1 Bader On the mathematical foundations of learning, continued
Fall 2016 (Deep learning) Every Monday in room ICICS 304 from 3:30-5:30pm
Date Presenter Topic
Sept. 12 Chris Learning neural networks
Sept. 19 Jason The loss surfaces of multilayer networks (spin glass)
Sept. 26 Abbas Benefits of depth in neural networks
Oct. 3 Nick Sketching and neural networks
Oct. 17 Reza Convexified convolutional neural networks
Oct. 24 Ricky What regularized auto-encoders learn from the data generating distribution
Oct. 31 Chris Beating the perils of non-convexity: guaranteed training of neural networks using tensor methods
Nov. 7 Saif Universal approximation theorem (Cybenko, Hornik et al.)
Nov. 14 Issam AdaNet
Nov. 21 Julie Finding approximate local minima for nonconvex optimization in linear time
Nov. 28 Free discussion Talk about ideas, interesting open questions, directions for further research, etc.
Summer 2016 Every Tuesday in room ICICS 304 from 1-3pm
Date Presenter Topic
May 10 Reza Hazan's Book, Chapter 1: Introduction and Chapter 2: Convergence analysis in convex optimization
May 17 Chris Hazan's Book, Chapter 3: First order algorithms for online convex optimization
May 24 Sharan Hazan's Book, Chapter 4: Second order methods
June 7 Abbas Hazan's Book, Chapter 5: Regularization
June 14 Abbas Hazan's Book, Chapter 5: Regularization continued
June 21 Sharan Hazan's Book, Chapter 6: Bandit convex optimization
June 28 Sharan Hazan's Book, Chapter 6: Bandit convex optimization, continued
July 5 Chris Streaming SVRG
July 12 Abbas Local Superlinearly-convergent method for finite sums, ICML'16
July 19 Mark Katyusha: The First Direct Acceleration of Stochastic Gradient Methods
Winter 2016 Every Monday in room ICICS 304 from 3-5pm
Date Presenter Topic
Feb 13 Abbas and Chris Moitra's Book, Chapter 2: Non-negative Matrix Factorization
Feb 29 Sharan and Reza Moitra's Book, Chapter 3: Tensor Methods
Feb 29 Abbas and Yaniv Moitra's Book, Chapter 4: Sparse Recovery
March 7 Chris Moitra's Book, Chapter 5: Dictionary Learning
March 14 Sharan and Reza Moitra's Book, Chapter 6: Mixtures of Gaussians
March 21 Abbas and Chris Moitra's Book, Chapter 7: Matrix completion
Apr 4 Abbas Iterative methods for matrix completion, based on a STOC'13 and a COLT'15 paper
Apr 11 Nick Circuits of the Mind and Cortical Learning, COLT'15
Apr 18 Sharan Some theory of Deep Learning, based on an ICLR'16 paper
Apr 25 Mark Overview of stochastic gradient methods

Some resources

List of deep learning papers (Google Docs)
Awesome deep learning resources
Deep Learning book, Goodfellow, Bengio, and Courville, 2016.

UBC Machine Learning Reading Group

Moitra's book.
Elad Hazan's book
List of papers (Google Docs)
Course page of Machine Learning Theory in Georgia Tech, with an extensive bibliography