Research

Publications

NIPS 2018

Accepted Papers

Neural Guided Constraint Logic Programming for Program Synthesis
By Lisa Zhang · Gregory Rosenblatt · Ethan Fetaya · Renjie Liao · William Byrd · Matthew Might · Raquel Urtasun · Richard Zemel

 

Learning Latent Subspaces in Variational Autoencoders
By Jack Klys · Jake Snell · Richard Zemel

 

Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer
By David Madras · Toni Pitassi · Richard Zemel

 

Reversible Recurrent Neural Networks
By Matthew MacKay · Paul Vicol · Jimmy Ba · Roger Grosse

 

On the Convergence and Robustness of Training GANs with Regularized Optimal Transport
By Maziar Sanjabi · Jimmy Ba · Meisam Razaviyayn · Jason Lee

 

Neural Ordinary Differential Equations
By Tian Qi Chen · Yulia Rubanova · Jesse Bettencourt · David Duvenaud

 

Isolating Sources of Disentanglement in Variational Autoencoders
By Tian Qi Chen · Xuechen Li · Roger Grosse · David Duvenaud

 

Iterative Value-Aware Model Learning
By Amir-massoud Farahmand

 

A Neural Compositional Paradigm for Image Captioning
By Bo Dai · Sanja Fidler · Dahua Lin

 

Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks
By Abdullah Rashwan · Agastya Kalra · Pascal Poupart · Prashant Doshi · Georgios Trimponias · Wei-Shou Hsu

 

Monte-Carlo Tree Search for Constrained POMDPs
By Jongmin Lee · Geon-hyeong Kim · Pascal Poupart · Kee-Eung Kim

 

Unsupervised Video Object Segmentation for Deep Reinforcement Learning
By Vikash Goel · Jameson Weng · Pascal Poupart

 

Deep Homogeneous Mixture Models: Representation, Separation, and Approximation
By Priyank Jaini · Pascal Poupart · Yaoliang Yu

 

Data-dependent PAC-Bayes priors via differential privacy
By Gintare Karolina Dziugaite · Daniel Roy

 

Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes
By Hassan Ashtiani, Shai Ben-David, Nick Harvey, Chris Liaw, Abbas Mehrabian, Yaniv Plan

Workshops

Machine Learning for Health (ML4H): Moving beyond supervised learning in healthcare
Andrew Beam · Tristan Naumann · Marzyeh Ghassemi · Matthew McDermott · Madalina Fiterau · Irene Y Chen · Brett Beaulieu-Jones · Michael Hughes · Farah Shamout · Corey Chivers · Jaz Kandola · Alexandre Yahi · Samuel G Finlayson · Bruno Jedynak · Peter Schulam · Natalia Antropova · Jason Fries · Adrian Dalca

ICML 2018

Accepted Papers and Oral

Neural Relational Inference for Interacting Systems
By Thomas Kipf · Ethan Fetaya · Kuan-Chieh Wang · Max Welling · Richard Zemel

 

Distilling the Posterior in Bayesian Neural Networks
By Kuan-Chieh Wang · Paul Vicol · James Lucas · Li Gu · Roger Grosse · Richard Zemel

 

Reviving and Improving Recurrent Back-Propagation
By Renjie Liao · Yuwen Xiong · Ethan Fetaya · Lisa Zhang · KiJung Yoon · Zachary S Pitkow · Raquel Urtasun · Richard Zemel

 

Learning Adversarially Fair and Transferable Representations
By David Madras · Elliot Creager · Toniann Pitassi · Richard Zemel

 

Inference Suboptimality in Variational Autoencoders
By Chris Cremer · Xuechen Li · David Duvenaud

 

Noisy Natural Gradient as Variational Inference
By Guodong Zhang · Shengyang Sun · David Duvenaud · Roger Grosse

 

Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control
By Yangchen Pan · Amir-massoud Farahmand · Martha White · Saleh Nabi · Piyush Grover · Daniel Nikovski

 

Differentiable Compositional Kernel Learning for Gaussian Processes
By Shengyang Sun · Guodong Zhang · Chaoqi Wang · Wenyuan Zeng · Jiaman Li · Roger Grosse

 

Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors
By Gintare Karolina Dziugaite · Daniel Roy

 

Learning to Reweight Examples for Robust Deep Learning
By Mengye Ren · Wenyuan Zeng · Bin Yang · Raquel Urtasun

 

Nature: International Journal of Science

Published Letter

Observation of topological phenomena in a programmable lattice of 1,800 qubits

By Andrew D. King, Juan Carrasquilla, Jack Raymond, Isil Ozfidan, Evgeny Andriyash, Andrew Berkley, Mauricio Reis,  Trevor Lanting, Richard Harris, Fabio Altomare, Kelly Boothby, Paul I. Bunyk, Colin Enderud, Alexandre Fréchette,  Emile Hoskinson, Nicolas Ladizinsky, Travis Oh, Gabriel Poulin-Lamarre, Christopher Rich, Yuki Sato,  Anatoly Yu. Smirnov, Loren J. Swenson, Mark H. Volkmann1, Jed Whittaker1, Jason Yao1, Eric Ladizinsky, Mark W. Johnson, Jeremy Hilton & Mohammad H. Amin

CVPR 2018

Spotlight

Now You Shake Me: Towards Automatic 4D Cinema
By Yuhao Zhou, Makarand Tapaswi and Sanja Fidler
Spotlight 3330

 

MovieGraphs: Towards Understanding Human-Centric Situations from Videos
By Paul Vicol, Makarand Tapaswi, Lluís Castrejón and Sanja Fidler
Spotlight 3308

Accepted Posters

Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++
By David Acuna, Huan Ling, Amlan Kar and Sanja Fidler
Poster 3409

 

Learning to Act Properly: Predicting and Explaining Affordances from Images
By Ching-Yao Chuang, Jiaman Li, Antonio Torralba and Sanja Fidler
Poster 631

 

SurfConv: Bridging 3D and 2D Convolution for RGBD Images
By Hang Chu, Wei-Chiu Ma, Kaustav Kundu, Raquel Urtasun and Sanja Fidler
Poster 711

 

A Face to Face Neural Conversation Model
By Hang Chu and Sanja Fidler
Poster 710

 

Now You Shake Me: Towards Automatic 4D Cinema
By Yuhao Zhou, Makarand Tapaswi and Sanja Fidler
Poster 3330

 

VirtualHome: Simulating Household Activities via Programs
By Xavier Puig, Kevin Ra, Marko Boben, Jiaman Li, Tingwu Wang, Sanja Fidler and Antonio Torralba
Poster 3399

 

MovieGraphs: Towards Understanding Human-Centric Situations from Videos
By Paul Vicol, Makarand Tapaswi, Lluís Castrejón and Sanja Fidler
Poster 3308

 

Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points
By Fabien Baradel, Christian Wolf, Julien Mille, and Graham Taylor
Poster 182

Oral

VirtualHome: Simulating Household Activities via Programs
By Xavier Puig, Kevin Ra, Marko Boben, Jiaman Li, Tingwu Wang, Sanja Fidler and Antonio Torralba
Oral 3399

ICLR 2018

Accepted Papers

Kronecker-factored Curvature Approximations for Recurrent Neural Networks

By James Martens, Jimmy Ba, Matt Johnson

 

Quantitatively Evaluating GANs With Divergences Proposed for Training

By Daniel Jiwoong Im, Alllan He Ma, Graham W. Taylor, Kristin Branson

 

Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches

By Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse

 

Attacking Binarized Neural Networks

By Angus Galloway, Graham W. Taylor, Medhat Moussa

 

Meta-Learning for Semi-Supervised Few-Shot Classification

By Mengye Ren, Sachin Ravi, Eleni Triantafillou, Jake Snell, Kevin Swersky, Josh B. Tenenbaum, Hugo Larochelle, Richard S. Zemel

 

Backpropagation through the Void: Optimizing control variates for black-box gradient estimation

By Will Grathwohl, Dami Choi, Yuhuai Wu, Geoff Roeder, David Duvenaud

 

Understanding Short-Horizon Bias in Stochastic Meta-Optimization

By Yuhuai Wu, Mengye Ren, Renjie Liao, Roger Grosse

 

NerveNet: Learning Structured Policy with Graph Neural Networks

By Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler

Workshop Papers

Predict Responsibly: Increasing Fairness by Learning to Defer

By David Madras, Toniann Pitassi, Richard Zemel

 

Isolating Sources of Disentanglement in Variational Autoencoders

By Tian Qi Chen, Xuechen Li, Roger Grosse, David Duvenaud

 

Graph Partition Neural Networks for Semi-Supervised Classification

By Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander Gaunt, Raquel Urtasun, Richard S. Zemel

 

Gradient-based Optimization of Neural Network Architecture

By Will Grathwohl, Elliot Creager, Seyed Ghasemipour, Richard Zemel

 

Reconstructing evolutionary trajectories of mutations in cancer

By Yulia Rubanova, Ruian Shi, Roujia Li, Jeff Wintersinger, Amit Deshwar, Nil Sahin, Quaid Morris

 

Inference in probabilistic graphical models by Graph Neural Networks

By KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard Zemel, Xaq Pitkow

 

Leveraging Constraint Logic Programming for Neural Guided Program Synthesis

By Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William Byrd, Raquel Urtasun, Richard Zemel

Talks

VECTOR INSTITUTE MACHINE LEARNING ADVANCES AND APPLICATIONS SEMINAR

2017-18

Presented at Fields Institute

Barak Pearlmutter, Hamilton Institute, Maynooth
Near-Criticality and Pathology in the Central Nervous System
Abstract

Suchi Saria, Johns Hopkins University
Individualizing Healthcare with Machine Learning (Video)
Abstract

Peter Grünwald, Leiden University
Learning the learning rate: how to repair Bayes when the model is wrong (Video)
Abstract

Emma Brunskill, Stanford University
Reinforcement Learning When Experience is Expensive (Video)
Abstract

Josh Tenenbaum, Massachusetts Institute of Technology
Building machines that learn and think like humans (Video)
Abstract

Chris Maddison, University of Oxford
Relaxed Gradient Estimators (Video)
Abstract

Yair Weiss, Hebrew University
Learning the Statistics of Full Images (Video)
Abstract

Christopher Manning, Stanford University
Towards a better model for neural network reasoning (Video)
Abstract

Vlad Mnih, DeepMind
Efficient Multi-Task Deep Reinforcement Learning (Video)
Abstract

Stephen Friend, Sage Bionetworks
Exploring fundamental unknowns that prevent us from using our devices to navigate between disease and health (Video)
Abstract

Ilya Sutskever, OpenAI
Meta Learning and Self Play (Video)
Abstract

Yee Whye Teh, University of Oxford
DisTraL: Distill and Transfer for Deep Multitask Reinforcement Learning (Video)
Abstract

Guang Wei Yu (Layer 6), Mohammad Islam (Wattpad), Putra anggala (Shopify), and Javier Moreno (Rubikloud)
Recommendation Systems (Video)
Abstract

Jennifer Listgarten, Microsoft Research
From Genetics to CRISPR Gene Editing with Machine Learning (Video)
Abstract

Chris Williams, Univeristy of Edinburgh
Artificial Intelligence for Data Analytics (Video)
Abstract

Jon Shlens, Google Brain
Learning representations of the visual world
Abstract

Geoffrey Hinton, University of Toronto
What is wrong with convolutional neural nets? (Video)
Abstract

2016-2017

Presented at Fields Institute

David Sontag, Massachusetts Institute of Technology
How is Machine Learning Going to Change Health Care? (Video)
Abstract

Alex Graves, DeepMind
Frontiers in Recurrent Neural Network Research
Abstract

Max Welling, University of Amsterdam
Generalizing convolutions for Deep Learning (Video)
Abstract

Ernest Earon, Precision Hawk
Airborne Intelligence: Why birds are so good at what they do (Video)
Abstract

James Bergstra, Kindred
From Teleoperation to AGI (Video)
Abstract

Ryan Geriepy, ClearPath Robotics
3 Steps To Applying Machine Learning To Fleets Of Self-Driving Vehicles (Video)
Abstract

Shane Gu, Cambridge University
Sample-Efficient Deep Reinforcement Learning for Robotics (Video)
Abstract

David Blei, Columbia University
Probabilistic Topic Models and User Behavior (Video)
Abstract

Fernanda Viegas, Martin Wattenberg and Daniel Smilkov, Google
Visualization for machine learning–and human learning, too (Video)
Abstract

Roger Melko, University of Waterloo
Machine Learning Quantum Physics (Video)
Abstract

Pieter Abbeel, University of California, Berkeley
Deep Reinforcement Learning for Robotics (Video)
Abstract

Roger Grosse, University of Toronto
Optimizing neural networks using structured probabilistic models (Video)
Abstract

Graham Taylor, University of Guelph
Dataset Augmentation in Feature Space (Video)
Abstract

Rob Fergus, New York University / Facebook
Memory and Communication in Neural Networks (Video)
Abstract

Hugo Larochelle, Google Brain

Autoregressive Generative Models with Deep Learning (Video)

Abstract

Michael Schull, Institute for Clinical Evaluative Sciences
ICES: Linking data and discovery, and building a health data science agenda (Video)
Abstract

Alexandre Le Bouthillier, Imagia
Imagia: Artificial Intelligence for medical image analysis (Video)
Abstract

Brendan Frey, Deep Genomics
Deep Genomics: Changing the course of genomic medicine (Video)
Abstract

Joshua Landy, Figure 1
Figure 1: The computer will see you now (Video)
Abstract

Ofer Shai, Meta Inc.
Meta: Unlocking the world’s scientific and technical insights (Video)
Abstract

Roger Grosse, David Duvenaud and Sanja Fidler,, University of Toronto
Meet the New Faculty: Roger Grosse, David Duvenaud & Sanja Fidler (Video)
Abstract

Andrew McCallum, University of Massachusetts
Universal Schema for Representation and Reasoning from Natural Language (Video)
Abstract

Richard Sutton, University of Alberta
The Future of Artificial Intelligence Belongs to Search and Learning (Video)
Abstract

Geoffrey Hinton, University of Toronto
Using Fast Weights to Store Temporary Memories (Video)
Abstract

Amir Ban, Tel-Aviv University
When Should an Expert Make a Prediction? (Video)
Abstract

VECTOR FRIDAY ML SEMINAR

2018

Gillian Hadfield, University of Toronto
AI Alignment and Human Normativity (Video)
Introduction
Vector Institute

Scott Sanner, University of Toronto
Autoencoders for Collaborative Filtering (Video)
Introduction
Vector Institute

ETHICS OF AI IN CONTEXT SERIES

2018

Presented at Centre of Ethics, University of Toronto

Richard Zemel, University of Toronto
Ensuring Fair and Responsible Automated Decisions (Video)
Centre of Ethics, University of Toronto

Frank Rudzicz, University of Toronto, University Health Network, Winterlight Labs
The Future of Automated Healthcare (Video)
Centre of Ethics, University of Toronto

Faculty

Faculty Affiliates

Yuhong Guo Suzanna Becker Neil Bruce Aleksandar Nikolov
Andreas Moshovos Blake Richards Gennady Pekhimenko Gillian Hadfield
Radford Neal Suzanne Stevenson Yang Xu Dana Kulic
Jimmy Lin Kate Larson Roger Melko Yaoliang Yu
Olga Veksler James Elder Jim Reilly John Connolly
Ranil Sonnadara Reza Samavi Thomas Doyle Konstantinos Derpanis
Alex Mihailidis Angela Schoellig Anne Martel Ashton Anderson
Babak Taati Benjamin Haibe-Kains Gerald Penn Graeme Hirst
Jason Anderson Kyros Kultulakos Michael Brudno Paul Boutros
Scott Sanner Sheila McIlrath Sven Dickinson Toniann Pitassi
Vaughn Betz Michael Hoffman Ali Ghodsi Jesse Hoey
Shai Ben-David Jody Culham Mark Daley Robert Mercer
Marcus Brubaker Yuri Boykov Ajay Agrawal Albert Yoon
Anthony Niblett Avi Goldfarb Benjamin Alarie Peter Wittek
Jonathan Kelly Maura Grossman Hui Jiang Hamid Reza Tizhoosh
Ruth Urner Tom Chau Laura Rosella

Postdocs

Naulet Zacharie Faiza Khan Khattak Ethan Fetaya Marc Law
Makarand Tapasawi Jörn-Henrik Jacobsen Bradly Stadie

Graduate Students and Student Interns

Chunhao Chang Mingjie Mai George Adam Ladislav Rampasek
Shems Saleh Sana Tokenaboni Daniel Hidru Lauren Erdman
Aziz Mezlini Angeline Yashodara Alexander Edmonds Ekansh Sharma
Christopher Srinivasa Haosui Duanmu Alex Gao Jeffrey Negrea
Jun Yang Yangbo Tang Jonathan Lorraine Geoffrey Roeder
Bowen Xu Tian Qi Chen Eric Langlois Chris Cremer
Will Grathwohl Guodong Zhang Jesse Bettencourt Matthew MacKay
Micha Livne Ali Punjani Fartash Faghri Farzaneh Mahdisoltani
Serena Jeblee Bai Li Mohamed Abdalla Akshay Budhkar
Demetres Kostas Daniyal Liaqat Fizza Ahmad Sheikh Stefania Raimondo
Arvid Fydenlund Ekaterina Kudashkina Thorsteinn Jonsson Colin Brennan
Vithursan Thangarasa Bradley Kennedy Alaaeldin Ali Shamak Dutta
Adam Balint Brendan Duke Angus Galloway Eu Wern Teh
Devinder Kumar Terrance Devries Michal Lisicki Boris Knyazev
Harris Chan Kaitlin Laverty Gurnit Atwal Kun Nie
Yulia Rubanova Alexander Sasse Nil Sahin Jeffrey Wintersinger
Simon Eng Amit Deshwar Kevin Ha Andrew Toulis
Ioan Andrei Barsan Chenxue Zhang Renjie Liao Eleni Triantafillou
Marc-Etienne Brunet David Madras Kamyar Seyed Ghasemipour Elliot Creager
Mengye Ren Jake Snell Kuan-Chieh Wang James Lucas
William Saunders Shengyang Sun Yuhuai  Wu Paul Adrian Vicol
Atef Chaudhury Amlan Kar Seung Wook Kim Jiaman  Li
Tingwu Wang Tingke Shen Wenzheng Chen Chaoqi  Wang
Hang Chu Kaustav Kundu Maria Shugrina Ricardo Salmon
Abdullah Rashwan Paulo Pachecho Priyank Jaini Joseph Scott
Jeffrey Rudd Guojun Zhang Ankit Vahdera Pan Pan Cheng
Kira Selby Rong Tian Tse Safwan Hossain John Chen
Haochen Zhang Soren Sabet Sarvestany Sheldon Huang Huan Ling
Gintare Karolina Dziugaite Rachid  Riad Kiarash Jamali Aidan Gomez
Sheng Jia Mufan Li YueLan Qin Ariel Herbert-Voss
Philippe Casgrain Nathan Killoran Oren Kraus Marissa Weis
Michael Leung Hannes Bretshneider Alice Gao
Romina Abachi Amir Hosein Khas Ahmadi Yuhao Zhou  

Postgraduate Affiliates – 2018-19 Cohort

Tristan Aumentado-Armstrong Mahtab Ahmed Shazia Akbar Aryan Arbabi
Nabiha Asghar Ahmed Ashraf Seyed Ershad Banijamali Felix Berkenkamp
Rober Boshra Omar Boursalie Andrew Boutros Alberto  Camacho
Jaskiret    Dhindsa Elham Dolatabadi Mohamed Helwa Kathleen Houlahan
Ethan Jackson Mehran Karimzadeh Reghbati Shehroz Khan Jin Hee Kim
Dmitry Marin Sean Robertson Petr Smirnov Ian Smith
Laleh Soltan Ghoraie Robin Swanson Matthew Tesfaldet Rodrigo Andres  Toro Icarte
Stavros Tsogkas Ga Wu SiQi Zhou Mohammad Hassan Zokaei Ashtiani

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