Conference on Data Science

November 12 - 15, 2019, The Fields Institute

Location: Fields Institute, Stewart Library

Description

The purpose of this conference is to bring together researchers in data science from the statistics and computer science perspectives to stimulate discussion, interaction, and cross-pollination of ideas.

Schedule

Tuesday, November 12th, 2019
09:30 to 10:00	Coffee break
10:00 to 10:30	On Clustering and Data Visualization Forrest Paton, McMaster University
10:30 to 11:00	Relaxing the Identically Distributed Assumption in Gaussian Co-Clustering for High Dimensional Data Michael Gallaugher, McMaster University
11:00 to 11:30	Maximum A-posteriori Estimation for the Gaussian Mixture Model via Mixed Integer Nonlinear Programming Patrick Flaherty, University of Massachusetts, Amherst
11:30 to 12:00	Fractionally supervised classification: a model-based clustering approach using the weighted likelihood Irene Vrbik, University of British Columbia
12:00 to 13:30	Lunch
13:30 to 14:00	Combinatorial Pure Exploration of Multi-armed Bandits with Full-bandit Feedback Yuko Kuroki, The University of Tokyo
14:00 to 14:30	Symmetric Loss and Its Applications to Learning under Label Noise Nontawat Charoenphakdee, The University of Tokyo
14:30 to 15:00	Machine Learning from Weak Supervision: Towards Accurate Classification with Low Labeling Costs Masashi Sugiyama, RIKEN and The University of Tokyo
15:00 to 15:30	Coffee break
15:30 to 16:00	Finite mixture modeling of directed weighted multilayer networks Volodymyr Melnykov, University of Alabama
16:00 to 16:30	Using Subset Log-Likelihoods to Trim Outliers in Gaussian Mixture Models Paul McNicholas, McMaster University

Wednesday, November 13th, 2019
09:30 to 10:00	Coffee break
10:00 to 10:30	Comparing EM to a greedy search algorithm to optimize ICL for mixture models Arthur White, Trinity College Dublin, the University of Dublin
10:30 to 11:00	Learning Causality and Learning with Causality: A Road to Intelligence Kun Zhang, Carnegie Mellon University
11:00 to 11:30	Tensor Variate Mixture Models Applied to Accelerometer Data in Children Peter Tait, McMaster University
11:30 to 12:00	Mixtures of Dirichlet-Multinomial Regression Models for Microbiome Data Sanjeena Dang, Binghamton University
12:00 to 13:30	Lunch
13:30 to 14:00	Bayesian infinite mixtures of multivariate normal-inverse Gaussian distributions for clus- tering of skewed data Yuan Fang, Binghamton University
14:00 to 14:30	Your classifier is secretly an energy based model and you should treat it like one Kevin Swersky, Google
14:30 to 15:00	Neural Execution Engines Milad Hashemi, Google
15:00 to 15:30	Coffee break
15:30 to 16:00	Learning to Fix Programming Errors with Graph2Diff Neural Networks Daniel Tarlow, Google
16:00 to 16:30	Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift Jasper Snoek, Google
16:30 to 17:00	Scalable and Efficient Comparison-based Search without Features Matthias Grossglauser, École Polytechnique Fédérale de Lausanne

Thursday, November 14th, 2019
09:30 to 10:00	Coffee break
10:00 to 10:30	Neural Stochastic Differential Equations for Sparsely-sampled Time Series David Duvenaud, University of Toronto
10:30 to 11:00	Learning Algorithms from Bayesian Principles Mohammad Emtiyaz Khan, RIKEN Center for AI project
11:00 to 11:30	A probabilistic framework for fusing genetic and metabolic networks with applications to Alzheimer’s disease Rachael Hageman Blair, State University of New York at Buffalo
11:30 to 12:00	A family of mixture models for biclustering Wangshu Tu, Binghamton University
12:00 to 13:30	Lunch
13:30 to 14:00	Minimized Aggregated Wasserstein Barycenter for Gaussian Mixture Models Lynn Lin, The Pennsylvania State University
14:00 to 14:30	An Inferential Framework for Individualized Minimal Clinically Importance Diﬀerence with a Linear Structure Zehua Zhou, State University of New York at Buffalo
14:30 to 15:00	Clustering of Multivariate Biological Data in the Multi-omics Era Anjali Silva, Princess Margaret Cancer Center, University Health Network, Vector Institute, University of Toronto
15:00 to 15:30	Coffee break
15:30 to 16:00	Automate the Diagnosis of Obstructive Sleep Apnea Using Convolutional Neural Networks (CNN) Based on Polysomnographs (PSGs) Sunny Wang, Wilfrid Laurier University
16:00 to 16:30	Merging K-means solutions for clustering large datasets Semhar Michael, South Dakota State University
16:30 to 19:00	Reception at Prenup Pub

Friday, November 15th, 2019
09:30 to 10:00	Coffee break
10:00 to 11:00	Differentiable Ranking and Sorting using Optimal Transport Marco Cuturi, Google Brain
11:00 to 12:00	Optimal transportation between unequal dimensions Robert McCann, University of Toronto

Registered Participants

Name	Affiliation
Zahid Abul-Basher	University of Toronto
Marcio Cardoso	MLC Consultng
Nontawat Charoenphakdee	The University of Tokyo
Sanjeena Dang	Binghamton University
Antoine Deza	McMaster University
Mehdy Dousty	University of Toronto
David Duvenaud	University of Toronto
Michal Edelstein	Technion
Yuan Fang	Binghamton University
Patrick Flaherty	University of Massachusetts, Amherst
Michael Gallaugher	McMaster University
Matthias Grossglauser	École Polytechnique Fédérale de Lausanne
Rachael Hageman Blair	State University of New York at Buffalo
Ramin Hamedi	cmorgroup
Milad Hashemi	Google
Abdulkadir Hussein	University of Windsor
Sebastian Jaimungal	University of Toronto
Daniel Jordán
Mohammad Emtiyaz Khan	RIKEN Center for AI project
Yuko Kuroki	The University of Tokyo
Lynn Lin	The Pennsylvania State University
Brian McCrindle	McMaster University
Paul McNicholas	McMaster University
Venkatesh Medabalimi
Volodymyr Melnykov	University of Alabama
Peter Miasniko	University of Toronto
Semhar Michael	South Dakota State University
Riyad Parvez	Ukko Agro
Forrest Paton	McMaster University
Omid Rezania	York University
Carolina Riascos	University of Toronto
Reza Samavi	McMaster University
Anjali Silva	Princess Margaret Cancer Center, University Health Network, Vector Institute, University of Toronto
Diego Silva	University of Toronto
Jasper Snoek	Google
Masashi Sugiyama	RIKEN and The University of Tokyo
Kevin Swersky	Google
Peter Tait	McMaster University
Daniel Tarlow	Google
Wangshu Tu	Binghamton University
Irene Vrbik	University of British Columbia
Sunny Wang	Wilfrid Laurier University
Arthur White	Trinity College Dublin, the University of Dublin
Dexiang Wu	Beihang University
Jason Zeng	Pratt and Whitney Canada
Kun Zhang	Carnegie Mellon University
Zehua Zhou	State University of New York at Buffalo

Organizing Committee

Sanjeena Dang - Binghamton University

Mohammad Emtiyaz Khan - RIKEN Center for AI project

Tanzy Love - University of Rochester

Paul McNicholas - McMaster University

Volodymyr Melnykov - University of Alabama

Masashi Sugiyama - RIKEN and The University of Tokyo

Fields Contact: Brittany Camp

The Fields Institute for
Research in Mathematical Sciences

Conference on Data Science

Description

Schedule

Registered Participants

Organizing Committee

Quick Links

Scheduled as part of

People and Contacts

Calendar and Events