Seminars

Every week, the OptML group gathers to update each other about their recent work and discuss various problems. In these one hour meetings, one student or faculty member gives a presentation, either to describe what they have learned in the past week or discuss the recent challenges they have been facing. The presentation is interactive with all members of the group participating, asking questions, and offering ideas. The group also occasionally invites researchers from industry or academia to present.

2017

11/03/2017 Mohammadreza Nazari OptML student
Policy gradients in application to reinforcement learning
11/17/2017 Albert Berahas visitor
A Multi-Batch L-BFGS Method for Machine Learning
11/10/2017 Lam Nguyen OptML student
When do stochastic gradient algorithms work well for training deep neural networks
11/03/2017 Francesco Orabona visitor
Coin Betting for Backprop without Learning Rates and More
10/20/2017 Chenxin Ma OptML student
Introduction to natural gradient descent II
10/13/2017 Rui Shi OptML student
Different measures on convergence results
10/06/2017 Chenxin Ma OptML student
Introduction to natural gradient descent I
09/22/2017 Courtney Paquette visitor
Acceleration for Gradient-Based Non-Convex Optimization
09/15/2017 Xi He OptML student
TensorFlow tutorial II
09/08/2017 Xi He OptML student
TensorFlow tutorial I
05/02/2017 Matt Menickelly OptML student
On graduated optimization for stochastic non-convex problems
04/18/2017 Majid Jahani OptML student
Finite-sum Composition Optimization via Variance Reduced
Gradient Descent
04/11/2017 Mertcan Yetkin OptML student
SVM for solving optimal control
04/04/2017 Liyuan Cao OptML student
k-Components: A Method for Analyzing Data in Radial Shape
03/28/2017 Mohammadreza Samadi OptML student
Gradient Descent with Momentum
03/21/2017 Jie Liu OptML student
SARAH: part II
03/07/2017 Lam Nguyen OptML student
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient
02/28/2017 Hiva Ghanbari OptML student
A Globally Convergent Inexact Newton Method for Systems of Monotone Equations
02/21/2017 Chenxin Ma OptML student
Underestimated Sequence Part II
02/14/2017 Xi He OptML student
Sub-Sampled Exact and Inexact Newton Methods
02/07/2017 Wei Guo OptML student
Barzilai-Borwein Step Size for Stochastic Gradient Descent paper
02/01/2017 Majid Jahani OptML student
Underestimated Sequence
11/29/2016 Matt Menickelly OptML student
Sparsity-Constrained Gaussian Graphical Models for Anomaly Detection
11/22/2016 Jie Liu OptML student
Fast stochastic methods for nonsmooth nonconvex optimization paper
11/01/2016 Mohammadreza Samadi OptML student
Proof technique in non-convex optimization
10/25/2016 Rui Shi OptML student
Robust Stochastic Approximation Approach to Stochastic Programming
10/18/2016 N/A OptML student
Discussions with Mark Schmidt
10/04/2016 Kürşat Kemikli OptML student
A limited memory quasi-Newton trust-region method for box constrained optimizatio
09/27/2016 Hiva Ghanbari OptML student
A Universal Catalyst for First-Order Optimization paper
09/20/2016 Chenxin Ma OptML student
An optimal first order method based on optimal quadratic averaging paper
09/13/2016 Wei Guo OptML student
Handling Nonpositive Curvature in a
Limited Memory Steepest Descent Method
08/30/2016 Xi He OptML student
Second order methods in training deep neural network
05/05/2016 Martin Takáč OptML faculty
Theano And GPU computing in Python: Code
03/31/2016 Mohammadreza Samadi OptML student
Efficient Trust Region Subproblem Solvers
03/24/2016 Chenxin Ma OptML student
Second Order Stochastic Optimization in Linear Time
03/10/2016 Matt Menickelly OptML student
Random Sampling in Stochastic Optimization
03/03/2016 Xi He OptML student
Intro to Deep Neural Networks
02/25/2016 Milad Siami OptML guest
System Performance Measures for Noisy Consensus Networks
02/18/2016 Jiawei Zhang OptML guest
Deep Neural Network Learning Artistic Style
02/11/2016 Milad Siami OptML guest
System Performance Measures for Noisy Consensus Networks
02/04/2016 Jie Liu OptML student
Hybrid Optimization Methods in AC Optimal Power Flow
01/28/2016 Frank E. Curtis OptML faculty
Theory of Stochastic Gradient Methods

2015

12/02/2015 Hiva Ghanbari OptML student
12/02/2015 Mohammadreza Samadi OptML student
11/25/2015 Xi He OptML student
11/11/2015 Francesco Orabona Yahoo! Labs
10/28/2015 Chenxin Ma OptML student
10/21/2015 Jeffrey Larson Argonne National Laboratory
10/21/2015 Matt Menickelly OptML student
10/14/2015 Wei Guo OptML student
10/07/2015 Jie Liu OptML student
09/30/2015 Hiva Ghanbari OptML student
09/30/2015 Mohammadreza Samadi OptML student
09/16/2015 Rui Shi OptML student
09/16/2015 Matt Menickelly OptML student
09/09/2015 Xi He OptML student
09/09/2015 Chenxin Ma OptML student
09/02/2015 Wei Guo OptML student
09/02/2015 Jie Liu OptML student
08/26/2015 Hiva Ghanbari OptML student
08/26/2015 Mohammadreza Samadi OptML student
08/19/2015 Xi He OptML student