2021  — 

• W. Li and Y. Yu, Faster Approximation of Probabilistic and Distributional Values via Least Squares. ICLR 2024. [pdf] [bibtex]

• Y. Lu, G. Kamath and Y. Yu, Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks. ICML 2023. [pdf] [bibtex]

• G. Zhang, P. Poupart and Y. Yu, Optimality and Stability in Non-Convex Smooth Games. JMLR 2022. [pdf] [bibtex]

• T. Dockhorn, Y. Yu, E. Sari, M. Zolnouri and V. Nia, Demystifying and Generalizing BinaryConnect. NeurIPS 2021. [pdf] [bibtex]

2017  —  2020

• H. Pham, S. Qian, J. Wang, T. Lutellier, J. Rosenthal, L. Tan, Y. Yu and N. Nagappan, Problems and Opportunities in Training Deep-Learning Software Systems: An Analysis of Variance. ASE 2020. Distinguished Paper [pdf] [bibtex]

• K. Wu, H. Wang, and Y. Yu, Stronger and Faster Wasserstein Adversarial Attacks. ICML 2020. [pdf] [bibtex] [code]

• J. Wang, S. Sun, and Y. Yu, Multivariate Triangular Quantile Maps for Novelty Detection. NeurIPS 2019. [pdf] [bibtex]

• P. Jaini, K. Selby, and Y. Yu, Sum-of-Squares Polynomial Flow. ICML 2019. [pdf] [bibtex]

• S. Sun and Y. Yu, Least-Squares Estimation of Weakly Convex Functions. AISTATS 2019. [pdf] [bibtex]

• P. Jaini, P. Poupart, and Y. Yu, Deep Homogeneous Mixture Models: Representation, Separation and Approximation. NeurIPS 2018. [pdf] [bibtex]

• Z. Shi, X. Zhang, and Y. Yu, Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction. NeurIPS 2017. [pdf] [bibtex]

• J. Yin and Y. Yu, Convex-constrained Sparse Additive Modeling and Its Extensions. UAI 2017. [pdf] [bibtex]

• X. Chang, Y. Yu, and Y. Yang, Robust Top-k Multiclass SVM for Visual Category Recognition. KDD 2017. [pdf] [bibtex]

• X. Chang, Y. Yu, Y. Yang, and E. Xing, Semantic Pooling for Complex Event Analysis in Untrimmed Videos. TPAMI 2017. [pdf] [bibtex] [code]

2014  —  2016

• X. Chang, Y. Yu, Y. Yang, and E. Xing, They Are Not Equally Reliable: Semantic Event Search using Differentiated Concept Classifiers. CVPR 2016. [pdf] [bibtex]

• H. Cheng, Y. Yu, X. Zhang, E. Xing, and D. Schuurmans, Scalable and Sound Low-Rank Tensor Learning. AISTATS 2016. [pdf] [bibtex] [code]

• X. Zheng, Y. Yu, and E. Xing, Linear Time Samplers for Supervised Topic Models using Compositional Proposals. ACM KDD 2015. [pdf] [bibtex] [code]

• Y. Yu, X. Zheng, M. Marchetti-Bowick and E. Xing, Minimizing Nonconvex Non-Separable Functions. AISTATS 2015. [pdf] [appendix] [bibtex]

• Y. Yu, X. Zhang and D. Schuurmans, Generalized Conditional Gradient for Sparse Estimation. Journal of Machine Learning Research, vol. 18, pp. 1-46, 2017. [pdf] [bibtex] [code] [arXiv]

  • Early versions in [NIPS12] [PhD Thesis Chapter 4]

2008  —  2013

• Y. Yu, Fast Gradient Algorithms for Structured Sparsity. PhD Thesis 2013. [pdf] [Longer version with more proofs] [bibtex]

• Y. Yu, On Decomposing the Proximal Map. NIPS 2013. [pdf] [bibtex]

• Y. Yu, H. Cheng, D. Schuurmans and C. Szepesvári, Characterizing the Representer Theorem. ICML 2013. [pdf] [bibtex]

• Y. Yu, O. Aslan and D. Schuurmans, A Polynomial-time Form of Robust Regression. NIPS 2012. [pdf] [bibtex]

• Y. Yu and D. Schuurmans, Rank/norm regularization with closed-form solutions: Application to subspace clustering. UAI 2011. [pdf] [bibtex]

• Y. Yu, J. Jiang and L. Zhang, Distance Metric Learning by Minimal Distance Maximization. Pattern Recognition, 2011, vol. 44: 639-649. [pdf] [bibtex]

• Y. Yu, Y. Li, D. Schuurmans and C. Szepesvári, A General Projection Property for Distribution Families. NIPS 2009. [pdf] [bibtex]