Michael Zhang

I’m Michael Zhang, a third year PhD student at the University of Toronto and Vector Institute, where I am fortunate to be supervised by Jimmy Ba and supported by the NSERC CGS Fellowship.

My main research interests are in optimization and reinforcement learning. I'm interested in improving our understanding of neural networks and developing algorithms which are theoretically motivated and work across a broad set of problems. I did my undergrad and fifth year Master's at the University of California, Berkeley, where I did robotics research in Pieter Abbeel's group. I've previously done internships at Google Research and LinkedIn (in Dublin, Ireland!)

You can contact me at [first name].cs.toronto.edu.

Email  /  CV  /  Google Scholar  /  Twitter

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Autoregressive Models for Offline Policy Evaluation and Optimization
Michael R. Zhang, Tom Le Paine, Ofir Nachum, Cosmin Paduraru, George Tucker, Ziyu Wang, Mohammad Norouzi
ICLR 2021
Paper / Video

Autogressive models are better for learning dynamics models than standard feedforward models in the fixed dataset setting.

Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes
James Lucas, Juhan Bae, Michael R. Zhang , Stanislav Fort, Richard Zemel, Roger Grosse
ICML 2021

We investigate and analyze why many neural networks have the loss decreasing monotonically in weight space from initialization to final solution.

Objective Social Choice: Using Auxiliary Information to Improve Voting Outcomes
Silviu Pitis, Michael R. Zhang
AAMAS 2020
Paper / Code / Video

Framework and aggregation rules for combining the preferences of multiple agents with noisy views of some ground truth.

Lookahead Optimizer: k steps forward, 1 step back
Michael R. Zhang, James Lucas, Geoffrey Hinton, Jimmy Ba
NeurIPS 2019
Paper / Code / Video

Optimization algorithm that speeds up training by using a search direction generated by multiple steps of an inner optimizer.

Reverse Curriculum Generation for Reinforcement Learning
Carlos Florensa, David Held, Markus Wulfmeier, Michael Zhang, Pieter Abbeel
CoRL 2017
Webpage / Paper / Blog post

Approach for tackling sparse reward tasks by generating goals of intermediate difficulty.

Probabilistically Safe Policy Transfer
David Held, Zoe McCarthy, Michael Zhang, Fred Shentu, Pieter Abbeel
ICRA 2017
Paper / Video

Framework for safely transferring policies learning in simulation to the real world.


I enjoy teaching and am grateful to have had the opportunity to teach a variety of different subjects in computer science.

U of Toronto CSC2547: Introduction to Reinforcement Learning : Winter 2021

U of Toronto CSC421/2516: Neural Networks and Deep Learning : Winter 2019

U of Toronto CSC2541: Deep Reinforcement Learning : Fall 2018

UC Berkeley CS170: Introduction to Algorithms - Spring 2018

UC Berkeley CS189: Introduction to Machine Learning - Fall 2017

UC Berkeley CS189: Introduction to Machine Learning - Spring 2017

UC Berkeley CS70: Discrete Math and Probability - Fall 2016

UC Berkeley CS70: Discrete Math and Probability - Spring 2016

At Berkeley, I was awarded the Outstanding Graduate Student Instructor award.


Some places I've been:

Canada (Vancouver, Toronto, Montreal, Edmonton, Calgary)
United States (SF, NY, LA, Chicago, Boston, Oahu),
China (Guangzhou, Beijing, Shanghai, Changsha, Wuxi),
Ireland (Dublin, Galway, Sligo, Cork, Doolin),
Japan (Tokyo, Osaka, Kyoto, Hakone, Takayama),
Northern Ireland, England, Amsterdam, Mexico

I love being outside and enjoy most sports. During quarantine, I have picked up and enjoy playing the card game Dominion.

2020 Recs

Book recommendations from 2017

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