Yaoliang Yu — Full list of papers

Drafts

2021 —

Y. Lu, M. Yang, Z. Liu, G. Kamath and Y. Yu, Disguised Copyright Infringement of Latent Diffusion Models. ICML 2024. [pdf] [bibtex]

S. Malekmohammadi, Y. Yu and Y. Cao, Noise-Aware Aggregation for Heterogeneous Differentially Private Federated Learning. ICML 2024. [pdf] [bibtex]

Y. Lu, M. Yang, G. Kamath and Y. Yu, Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors. IEEE SaTML, 2024. [pdf] [bibtex]

J. Dong, B. Wang and Y. Yu, Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games. AISTATS, 2024. [pdf] [bibtex]

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

Y. Lu, G. Zhang, S. Sun, H. Guo and Y. Yu, f-Mutual Information Contrastive Learning. TMLR, 2023. [pdf] [bibtex]

W. Li and Y. Yu, Robust Data Valuation with Weighted Banzhaf Values. NeurIPS, 2023. [pdf] [bibtex]

D. Jiang, S. Sun and Y. Yu, Functional Renyi Differential Privacy for Generative Modeling. NeurIPS, 2023. [pdf] [bibtex]

Y. Lu, Y. Yu, X. Li and V. P. Nia, Understanding Neural Network Binarization with Forward and Backward Proximal Quantizers. NeurIPS, 2023. [pdf] [bibtex]

A. Ghose, A. Gupta, Y. Yu and P. Poupart, Batchnorm Allows Unsupervised Radial Attacks. NeurIPS, 2023. [pdf] [bibtex]

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

J. Xin, R. Tang, Z. Jiang, Y. Yu and J. Lin, Operator Selection and Ordering in a Pipeline Approach to Efficiency Optimizations for Transformers. ACL Findings, 2023. [pdf] [bibtex]

W. Li, L. Kari, Y. Yu and L. Hug, MT-MAG: Accurate and interpretable machine learning for complete or partial taxonomic assignments of metagenome-assembled genomes. PLOS One, 2023. [pdf] [bibtex]

H. Lu, D. Herman and Y. Yu, Multi-Objective Reinforcement Learning: Convexity, Stationarity and Pareto Optimality. ICLR, 2023. [pdf] [bibtex]

G. Zhang, S. Malekmohammadi, X. Chen and Y. Yu, Proportional Fairness in Federated Learning. TMLR, 2023. [pdf] [bibtex]

Y. Lu, G. Kamath and Y. Yu, Indiscriminate Data Poisoning Attacks on Neural Networks. TMLR, 2022. [pdf] [bibtex]

Z. Hu, K. Shaloudegi, G. Zhang and Y. Yu, FedMGDA+: Federated Learning meets Multi-objective Optimization. IEEE Transactions on Network Science and Engineering, 2022. [pdf] [bibtex]

T. Fujiwara, J. Zhao, F. Chen, Y. Yu and K. Ma, Interpretable Contrastive Learning for Networks. Journal of Data Science, Statistics, and Visualisation, 2022. [pdf] [bibtex]

D. Jiang, S. Sun and Y. Yu, Revisiting flow generative models for Out-of-distribution detection. ICLR 2022. [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]

G. Zhang, H. Zhao, Y. Yu and P. Poupart, Quantifying and Improving Transferability in Domain Generalization. NeurIPS 2021. [pdf] [bibtex]

X. Li, B. Liu, Y. Yu, W. Liu, C. Xu and V. Nia, S3: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks. NeurIPS 2021. [pdf] [bibtex]

S. Qian, H. Pham, T. Lutellier, Z. Hu, J. Kim, T. Lin, Y. Yu, J. Chen and S. Shah, Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training. NeurIPS 2021. [pdf] [bibtex]

J. Xin, R. Tang, Y. Yu and J. Lin, The Art of Abstention: Selective Prediction and Error Regularization for Natural Language Processing. ACL 2021. [pdf] [bibtex]

H. Cheng, X. Liu, L. Pereira, Y. Yu and J. Gao, Posterior Differential Regularization with f-divergence for Improving Model Robustness. NAACL 2021. [pdf] [bibtex]

J. Xin, R. Tang, Y. Yu and J. Lin, BERxiT: Better-fine-tuned and Wider-applicable Early Exit for BERT. EACL, 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. [pdf] [bibtex]

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

P. Jaini, I. Kobyzev, Y. Yu and M. Brubaker, Tails of Lipschitz Triangular Flows. ICML 2020. [pdf] [bibtex]

Y. Ma, V. Ganapathiraman, Y. Yu and X. Zhang, Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space. ICML 2020. [pdf] [bibtex]

X. Lian, K. Jain, J. Truszkowski, P. Poupart and Y. Yu, Unsupervised Multilingual Alignment using Wasserstein Barycenters. IJCAI 2020. [pdf] [bib]

J. Xin, R. Tang, J. Lee, and Y. Yu and J. Lin, DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference. ACL 2020 (short). [pdf] [bibtex]

R. Tang, J. Lee, J. Xin, X. Liu, Y. Yu and J. Lin, Showing Your Work Doesn't Always Work. ACL 2020 (short). [pdf] [bibtex]

K. Wu, W. Ding, R. Huang and Y. Yu, On Minimax Optimality of GANs for Robust Mean Estimation. AISTATS 2020. [pdf] [bibtex] [code]

G. Zhang and Y. Yu, Convergence of Gradient Methods on Bilinear Zero-Sum Games. ICLR 2020. [pdf] [bibtex] [code]

J. Xin, J. Lin and Y. Yu, What Part of the Neural Network Does This? Understanding LSTMs by Measuring and Dissecting Neurons. EMNLP 2019 (short). [pdf] [bibtex]

K. Wu and Y. Yu, Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin. NeurIPS workshop 2019. [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]

M. Marchetti-Bowick, Y. Yu, W. Wu and E. Xing, A Penalized Regression Model for the Joint Estimation of eQTL Associations and Gene Network Structure. Annals of Applied Statistics, vol. 13, no. 1, pp. 248–270, 2019. [pdf] [bibtex]

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

V. Ganapathiraman, Z. Shi, X. Zhang and Y. Yu, Inductive Two-Layer Modeling with Parametric Bregman Transfer. ICML 2018. [pdf] [bibtex]

P. Xie, J. Kim, Q. Ho, Y. Yu and E. Xing, Orpheus: Efficient Distributed Machine Learning via System and Algorithm Co-design. SoCC 2018. [pdf] [bibtex]

Y. Zhou, Y. Liang, Y. Yu, W. Dai and E. Xing, Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters. JMLR 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]

P. Xie, Y. Deng, Y. Zhou, A. Kumar, Y. Yu, J. Zou and E. Xing, Learning Latent Space Models with Angular Constraints. ICML 2017. [pdf] [bibtex]

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

M. Law, Y. Yu, R. Urtasun, R. Zemel and E. Xing, Efficient Multiple Instance Metric Learning using Weakly Supervised Data. CVPR 2017. [pdf] [bibtex]

X. Ma, Y. Gao, Z. Hu, Y. Yu, Y. Deng and E. Hovy, Dropout with Expectation-Linear Regularization. ICLR 2017. [pdf] [bibtex]

S. Xu, Y. Zhou, K. Yuan, Y. Yu, X. Ni, P. Xie and E. Xing, Inference of Multiple-wave Population Admixture by Modeling Decay of Linkage Disequilibrium With Polynomial Functions. Heredity, vol. 118, pp. 503–510, 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

V. Ganapathiraman, X. Zhang, Y. Yu and J. Wen, Convex Two-Layer Modeling with Latent Structure. NIPS 2016. [pdf] [bibtex]

P. Xie, J. Kim, Y. Zhou, Q. Ho, A. Kumar, Y. Yu and E. Xing, Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting. UAI 2016. [pdf] [bibtex]

K. Kandasamy and Y. Yu, Additive Approximations in High Dimensional Nonparametric Regression via the SALSA. ICML 2016. [pdf] [bibtex]

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]

M. Law, Y. Yu, M. Cord and E. Xing, Closed-Form Training of Mahalanobis Metric for Supervised Clustering. 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]

E. Xing, Q. Ho, W. Dai, J. Kim, J. Wei, S. Lee, X. Zheng, P. Xie, A. Kumar and Y. Yu, Petuum: A New Platform for Distributed Machine Learning on Big Data. IEEE Transactions on Big Data, vol. 1, no. 2, pp. 49-67, 2015. [pdf] [bibtex] [code]

Y. Yu and E. Xing, Exact Algorithms for Isotonic Regression and Related. In: Journal of Physics: Conference Series, 2016. [pdf] [bibtex]

X. Chang, Y. Yu, Y. Yang and A. Hauptmann, Searching Persuasively: Joint Event Detection and Evidence Justification with Limited Supervision. ACM MM, 2015. [pdf] [bibtex]

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

X. Chang, Y. Yang, A. Hauptmann, E. Xing and Y.Yu, Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection. IJCAI 2015. [pdf] [bibtex]

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

A. Yu, W. Ma, Y. Yu, J. Carbonell and S. Sra, Efficient Structured Matrix Rank Minimization. NIPS 2014. [pdf] [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, Better Approximation and Faster Algorithm Using the Proximal Average. NIPS 2013. [pdf] [bibtex]

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

X. Zhang, Y. Yu and D. Schuurmans, Polar Operators for Structured Sparse Estimation. NIPS 2013. [pdf] [bibtex]

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

X. Zhang, Y. Yu and D. Schuurmans, Accelerated Training for Matrix-norm Regularization: A Boosting Approach. NIPS 2012. [pdf] [bibtex] [code]

M. White, Y. Yu, X. Zhang and D. Schuurmans, Convex Multi-view Subspace Learning. NIPS 2012. [pdf] [bibtex]

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

Y. Yu and C. Szepesvári, Analysis of Kernel Mean Matching under Covariate Shift. ICML 2012. [pdf] [bibtex]

Y. Yu, J. Neufeld, R. Kyros, X. Zhang and D. Schuurmans, Regularizers versus Losses for Nonlinear Dimensionality Reduction. ICML 2012. [pdf] [bibtex]

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

X. Zhang, Y. Yu, M. White, R. Huang and D. Schuurmans, Convex sparse coding, subspace learning, and semi-supervised extensions. AAAI 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, M. Yang, L. Xu, M. White and D. Schuurmans, Relaxed Clipping: A Global Training Method for Robust Regression and Classification. NIPS 2010. [pdf] [bibtex]

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