Christopher A. Choquette-Choo

I am a researcher in the CleverHans Lab at the Vector Institute, where I am researching new attacks and defenses in adversarial machine learning.

I've worked on deep learning projects, namely DualDNN while I was at Intel Corp., differential privacy in collaboration with Google's Tensorflow/Privacy, and AutoML while I was at Georgian Partners LP. Now, I am researching with Professor Nicolas Papernot on adversarial machine learning and with Professor Alan Aspuru-Guzik on Bayesian models and active learning. I am in my final year of Engineering Science, with a major in Robotics, at the University of Toronto, where I have a full scholarship for leadership and academic achievement.

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I'm interested in machine learning and its applications, including adversarial ML, deep learning, NLP, and Bayesian models.

Machine Unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, Nicolas Papernot
arXiv pre-print 2019
This paper was completed in equal contribution between myself, Lucas, Varun, Hengrui, Adelin, and Baiwu. The names are ordered alphabetically.

How can we minimize the accuracy degradation and computational retraining cost using a true unlearning approach - retraining a model from scratch? We define a stricter unlearning requirement as well as an approach to drastically minimizing these risks in uniform and distribution-aware settings.

A multi-label, dual-output deep neural network for automated bug triaging
Christopher A. Choquette-Choo, David Sheldon, Jonny Proppe, John Alphonso-Gibbs, Harsha Gupta
ICMLA, 2019 (TBD)

By utilizing a model's own knowledge of an analogous lower-dimensionality solution-space, we can achieve higher accuracies in a higher-dimensionality solution-space.

Invited Talks
Adversarial Machine Learning: Ensuring Security and Privacy of ML Models and Sensitive Data.
Presented at the REWORK Responsible AI Summit 2019.

This website was based off Jon Barron's.