SCIENTIFIC PROGRAMS AND ACTIVITIES

October 23, 2017

THE FIELDS INSTITUTE FOR RESEARCH IN MATHEMATICAL SCIENCES

Thematic Program on Statistical Inference, Learning, and Models for Big Data
January to June, 2015


January 26 – 30, 2015
Workshop on Big Data and Statistical Machine Learning
Organizing Committee
Ruslan Salakhutdinov (Chair)
Dale Schuurmans
Yoshua Bengio
Hugh Chipman
Bin Yu

Program

The aim of this workshop is to bring together researchers working on various large-scale deep learning as well as hierarchical models to discuss a number of important challenges, including the ability to perform transfer learning as well as the best strategies to learn these systems on large scale problems. These problems are "large" in terms of input dimensionality (in the order of millions), number of training samples (in the order of 100 millions or more) and number of categories (in the order of several tens of thousands).

Tentative Schedule

Monday January 26

8:30-9:15

Coffee and Registration

9:15-9:30

Ruslan Salakhutdinov: Welcome

9:30-10:30

Yoshua Bengio, Université de Montréal
Exploring alternatives to Boltzmann machine

10:30-11:00

Coffee

11:00-12:00

John Langford, Microsoft Research
Learning to explore

12:00-2:00

Lunch
2:00-3:00
Hau-tieng Wu, University of Toronto
Structure massive data by graph connection Laplacian and its application
3:00-3:30

Tea

3:30-4:30

Roger Grosse, University of Toronto
Scaling up natural gradient by factorizing Fisher information
4:30
Cash Bar Reception
Tuesday January 27

9:30-10:30

Brendan Frey, University of Toronto
The infinite genome project: Using statistical induction to understand the genome and improve human health

10:30-11:00

Coffee break

11:00-12:00

Daniel Roy, University of Toronto
Mondrian Forests: Efficient Online Random Forests

12:00-2:00

Lunch break

2:00-3:00

Raquel Urtasun, University of Toronto

3:00-3:30

Tea break
Wednesday January 28

9:30-10:30

Samy Bengio, Google Inc
The Battle Against the Long Tail

10:30-11:00

Coffee break

11:00-12:00

Richard Zemel, University of Toronto
Learning Rich But Fair Representations

12:00-1:00

Lunch break

2:00-3:00

David Blei, Princeton University
Probabilistic Topic Models and User Behavior

3:00-3:30

Tea break
3:30-4:30
Yura Burda, Fields Institute
Raising the Reliability of Estimates of Generative Performance of MRFs
Thursday January 29

9:30-10:30

Joelle Pineau, McGill University
Practical kernel-based reinforcement learning

10:30-11:00

Coffee break

11:00-12:00

Cynthia Rudin, MIT CSAIL and Sloan School of Management
Thoughts on Interpretable Machine Learning

12:00-2:00

Lunch

2:00-3:00

Radford Neal, University of Toronto
Learning to Randomize and Remember in Partially-Observed Environments

3:00-3:30

Tea break
Friday January 30

9:30-10:30

Alexander Schwing, The Fields Institute
Deep Learning meets Structured Prediction

10:30-11:00

Coffee break

11:00-12:00

Ruslan Salakhutdinov:Closing remarks.

12:00-2:00

Lunch

 

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