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Yaoliang Yu

University of Alberta

Posterior Differential Regularization with f-divergence for Improving Model Robustness

Oct 23, 2020
Hao Cheng, Xiaodong Liu, Lis Pereira, Yaoliang Yu, Jianfeng Gao

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OLALA: Object-Level Active Learning Based Layout Annotation

Oct 14, 2020
Zejiang Shen, Jian Zhao, Melissa Dell, Yaoliang Yu, Weining Li

* 8 pages, 3 figures 

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Stronger and Faster Wasserstein Adversarial Attacks

Aug 06, 2020
Kaiwen Wu, Allen Houze Wang, Yaoliang Yu

* 30 pages, accepted to ICML 2020 

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Density Deconvolution with Normalizing Flows

Jul 13, 2020
Tim Dockhorn, James A. Ritchie, Yaoliang Yu, Iain Murray

* Appearing at the second workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models (ICML 2020), Virtual Conference. 8 pages, 6 figures, 5 tables 

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Newton-type Methods for Minimax Optimization

Jun 25, 2020
Guojun Zhang, Kaiwen Wu, Pascal Poupart, Yaoliang Yu

* 26 pages 

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FedMGDA+: Federated Learning meets Multi-objective Optimization

Jun 20, 2020
Zeou Hu, Kiarash Shaloudegi, Guojun Zhang, Yaoliang Yu

* 26 pages, 9 figures; initial draft, comments welcome! 

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Interpretable Contrastive Learning for Networks

May 25, 2020
Takanori Fujiwara, Jian Zhao, Francine Chen, Yaoliang Yu, Kwan-Liu Ma

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Showing Your Work Doesn't Always Work

Apr 28, 2020
Raphael Tang, Jaejun Lee, Ji Xin, Xinyu Liu, Yaoliang Yu, Jimmy Lin

* Accepted to ACL 2020 

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DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference

Apr 27, 2020
Ji Xin, Raphael Tang, Jaejun Lee, Yaoliang Yu, Jimmy Lin

* Accepted at ACL 2020 

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Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space

Apr 25, 2020
Yingyi Ma, Vignesh Ganapathiraman, Yaoliang Yu, Xinhua Zhang

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Complete Hierarchy of Relaxation for Constrained Signomial Positivity

Mar 08, 2020
Allen Houze Wang, Priyank Jaini, Yaoliang Yu, Pascal Poupart

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Optimality and Stability in Non-Convex-Non-Concave Min-Max Optimization

Feb 27, 2020
Guojun Zhang, Pascal Poupart, Yaoliang Yu

* 39 pages 

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Unsupervised Multilingual Alignment using Wasserstein Barycenter

Jan 28, 2020
Xin Lian, Kshitij Jain, Jakub Truszkowski, Pascal Poupart, Yaoliang Yu

* Work in progress; comments welcome! 

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Exploiting Token and Path-based Representations of Code for Identifying Security-Relevant Commits

Nov 15, 2019
Achyudh Ram, Ji Xin, Meiyappan Nagappan, Yaoliang Yu, Rocío Cabrera Lozoya, Antonino Sabetta, Jimmy Lin

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Convergence Behaviour of Some Gradient-Based Methods on Bilinear Games

Aug 15, 2019
Guojun Zhang, Yaoliang Yu

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Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin

Jul 26, 2019
Kaiwen Wu, Yaoliang Yu

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Tails of Triangular Flows

Jul 10, 2019
Priyank Jaini, Ivan Kobyzev, Marcus Brubaker, Yaoliang Yu

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Distributional Reinforcement Learning for Efficient Exploration

May 13, 2019
Borislav Mavrin, Shangtong Zhang, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu

* ICML, 2019 

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Sum-of-Squares Polynomial Flow

May 07, 2019
Priyank Jaini, Kira A. Selby, Yaoliang Yu

* 13 pages, ICML'2019 

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Provably noise-robust, regularised $k$-means clustering

Aug 27, 2018
Shrinu Kushagra, Yaoliang Yu, Shai Ben-David

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Convex-constrained Sparse Additive Modeling and Its Extensions

May 01, 2017
Junming Yin, Yaoliang Yu

* 17 pages, 2 figures 

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Dropout with Expectation-linear Regularization

Feb 15, 2017
Xuezhe Ma, Yingkai Gao, Zhiting Hu, Yaoliang Yu, Yuntian Deng, Eduard Hovy

* Published as a conference paper at ICLR 2017. Camera-ready Version. 23 pages (paper + appendix) 

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Additive Approximations in High Dimensional Nonparametric Regression via the SALSA

May 24, 2016
Kirthevasan Kandasamy, Yaoliang Yu

* International Conference on Machine Learning (ICML) 2016 

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Distributed Machine Learning via Sufficient Factor Broadcasting

Nov 26, 2015
Pengtao Xie, Jin Kyu Kim, Yi Zhou, Qirong Ho, Abhimanu Kumar, Yaoliang Yu, Eric Xing

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Petuum: A New Platform for Distributed Machine Learning on Big Data

May 14, 2015
Eric P. Xing, Qirong Ho, Wei Dai, Jin Kyu Kim, Jinliang Wei, Seunghak Lee, Xun Zheng, Pengtao Xie, Abhimanu Kumar, Yaoliang Yu

* 15 pages, 10 figures, final version in KDD 2015 under the same title 

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Generalized Conditional Gradient for Sparse Estimation

Oct 17, 2014
Yaoliang Yu, Xinhua Zhang, Dale Schuurmans

* 67 pages, 20 figures 

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Regularizers versus Losses for Nonlinear Dimensionality Reduction: A Factored View with New Convex Relaxations

Jun 27, 2012
Yaoliang Yu, James Neufeld, Ryan Kiros, Xinhua Zhang, Dale Schuurmans

* Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012) 

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