Ruitong Huang

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Research Team Lead

PhD Computing Science, University of Alberta

Ruitong Huang is currently a research team lead at Borealis AI. His research interests broadly include topics such as online learning, convex optimization, adversarial learning, and reinforcement learning. Ruitong obtained his PhD in Statistical Machine Learning from the computing science department of University of Alberta, where he was advised by Professor Csaba Szepesvari and Professor Dale Schuurmans. Before that, Ruitong spent two wonderful years in the David R. Cheriton School of Computer Science at University of Waterloo for his Master's in Symbolic Computation, advised by Mark Giesbrecht.

Research Areas

Reinforcement Learning


April 30, 2020

Max-Margin Adversarial Training: Direct Input Space Margin Maximization through Adversarial Training

International Conference on Learning Representations (ICLR), 2020
Authors: G. W. Ding , Y. Sharma, K. Lui , R. Huang
Dec. 8, 2019

Maximum Entropy Monte-Carlo Planning

Neural Information Processing Systems (NeurIPS), 2019
Authors: C. Xiao, R. Huang , J. Mei, D. Schuurmans, M. Müller
Aug. 10, 2019

On Principled Entropy Exploration in Policy Optimization

International Joint Conference on Artificial Intelligence (IJCAI), 2019
Authors: *J. Mei, *C. Xiao, R. Huang , D. Schuurmans, M. Müller
* Denotes equal contribution
May 6, 2019

On the Sensitivity of Adversarial Robustness to Input Data Distributions

International Conference on Learning Representations (ICLR), 2019
Authors: G. Weiguang Ding , K. Lui , T. Jin, L. Wang, R. Huang
Dec. 3, 2018

Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds

Neural Information Processing Systems (NeurIPS), 2018
Authors: K. Lui , G. W. Ding , R. Huang , R.J. McCann
Dec. 3, 2018

Few-Shot Self Reminder to Overcome Catastrophic Forgetting

Workshop on Continual Learning (NeurIPS), 2018
Authors: J. Wen, Y. Cao , R. Huang
April 30, 2018

Improving GAN Training via Binarized Representation Entropy (BRE) Regularization

International Conference on Learning Representations (ICLR), 2018
Authors: Y. Cao , G. W. Ding , K. Lui , R. Huang
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