Publications | Yaoliang Yu's Webpage

H. Lu, Q. Wu, Y. Yu (2025). Stochastic Forward-Backward Deconvolution: Training Diffusion Models with Finite Noisy Datasets. International Conference on Machine Learning (ICML).

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Z. Hu, Y. Yu (2025). Leveraging Variable Sparsity to Refine Pareto Stationarity in Multi-Objective Optimization. International Conference on Learning Representations (ICLR).

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H. Lu, S. Szabados, Y. Yu (2025). Diffusion Models under Group Transformations. International Conference on Artificial Intelligence and Statistics (AISTATS).

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J. Dong, B. Wang, Y. Yu (2025). Last-iterate Convergence in Regularized Graphon Mean Field Game. Association for the Advancement of Artificial Intelligence (AAAI).

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Y. Lu, M. Yang, Z. Liu, G. Kamath, Y. Yu (2024). Disguised Copyright Infringement of Latent Diffusion Models. International Conference on Machine Learning (ICML).

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S. Malekmohammadi, Y. Yu, Y. Cao (2024). Noise-Aware Aggregation for Heterogeneous Differentially Private Federated Learning. International Conference on Machine Learning (ICML).

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Y. Lu, M. Yang, G. Kamath, Y. Yu (2024). Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors. 2nd IEEE Conference on Secure and Trustworthy Machine Learning (SaTML).

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J. Dong, B. Wang, Y. Yu (2024). Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games. International Conference on Artificial Intelligence and Statistics (AISTATS).

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W. Li, Y. Yu (2024). Faster Approximation of Probabilistic and Distributional Values via Least Squares. International Conference on Learning Representations (ICLR).

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W. Li, Y. Yu (2024). One Sample Fits All: Approximating All Probabilistic Values Simultaneously and Efficiently. Advances in Neural Information Processing Systems (NeurIPS).

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W. Li, Y. Yu (2023). Robust Data Valuation with Weighted Banzhaf Values. Advances in Neural Information Processing Systems (NeurIPS).

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Y. Lu, Y. Yu, X. Li, V. P. Nia (2023). Understanding Neural Network Binarization with Forward and Backward Proximal Quantizers. Advances in Neural Information Processing Systems (NeurIPS).

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A. Ghose, A. Gupta, Y. Yu, P. Poupart (2023). Batchnorm Allows Unsupervised Radial Attacks. Advances in Neural Information Processing Systems (NeurIPS).

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Y. Lu, G. Zhang, S. Sun, H. Guo, Y. Yu (2023). $f$-MICL: Understanding and Generalizing InfoNCE-based Contrastive Learning. Transactions on Machine Learning Research.

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

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H. Lu, Y. Lu, D. Jiang, S. Szabados, S. Sun, Y. Yu (2023). CM-GAN: Stabilizing GAN Training with Consistency Models. ICML Workshop on Structured Probabilistic Inference & Generative Modeling.

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Y. Lu, G. Kamath, Y. Yu (2023). Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks. International Conference on Machine Learning (ICML).

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D. Jiang, S. Sun, Y. Yu (2023). Functional Rényi Differential Privacy for Generative Modeling. Advances in Neural Information Processing Systems (NeurIPS).

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J. Xin, R. Tang, Z. Jiang, Y. Yu, J. Lin (2023). Operator Selection and Ordering in a Pipeline Approach to Efficiency Optimizations for Transformers. Findings of the Association for Computational Linguistics (ACL).

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Tim Dockhorn, Robin Rombach, Andreas Blatmann, Yaoliang Yu (2023). Distilling the Knowledge in Diffusion Models. CVPR workshop on Generative Models for Computer Vision.

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H. Lu, D. Herman, Y. Yu (2023). Multi-Objective Reinforcement Learning: Convexity, Stationarity and Pareto Optimality. International Conference on Learning Representations (ICLR).

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G. Zhang, S. Malekmohammadi, Xi Chen, Y. Yu (2023). Proportional Fairness in Federated Learning. Transactions on Machine Learning Research.

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Y. Lu, G. Kamath, Y. Yu (2022). Indiscriminate Data Poisoning Attacks on Neural Networks. Transactions on Machine Learning Research.

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S. Malekmohammadi, K. Shaloudegi, Z. Hu, Y. Yu (2022). A Unifying Framework for Federated Learning. Federated and Transfer Learning.

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T. Fujiwara, J. Zhao, F. Chen, Y. Yu, Kwan-Liu Ma (2022). Network Comparison with Interpretable Contrastive Network Representation Learning. Journal of Data Science, Statistics, and Visualisation.

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

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D. Jiang, S. Sun, Y. Yu (2022). Revisiting flow generative models for Out-of-distribution detection. International Conference on Learning Representations (ICLR).

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J. Sun, D. Jiang, Y. Yu (2022). Conditional Generative Quantile Networks via Optimal Transport. ICLR Workshop on Deep Generative Models for Highly Structured Data.

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G. Zhang, P. Poupart, Y. Yu (2022). Optimality and Stability in Non-Convex Smooth Games. Journal of Machine Learning Research.

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X. Li, B. Liu, Y. Yu, W. Liu, C. Xu, V. Nia (2021). S$^3$: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks. Advances in Neural Information Processing Systems (NeurIPS).

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G. Zhang, H. Zhao, Y. Yu, P. Poupart (2021). Quantifying and Improving Transferability in Domain Generalization. Advances in Neural Information Processing Systems (NeurIPS).

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T. Dockhorn, Y. Yu, E. Sari, M. Zolnouri, V. Nia (2021). Demystifying and Generalizing BinaryConnect. Advances in Neural Information Processing Systems (NeurIPS).

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S. Qian, H. Pham, T. Lutellier, Z. Hu, J. Kim, T. Lin, Y. Yu, J. Chen, S. Shah (2021). Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training. Advances in Neural Information Processing Systems (NeurIPS).

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J. Xin, R. Tang, Y. Yu, J. Lin (2021). The Art of Abstention: Selective Prediction and Error Regularization for Natural Language Processing. The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP).

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G. Zhang, K. Wu, P. Poupart, Y. Yu (2021). Newton-type Methods for Minimax Optimization. ICML Workshop on Beyond First-Order Methods in ML Systems.

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H. Cheng, X. Liu, L. Pereira, Y. Yu, J. Gao (2021). Posterior Differential Regularization with $f$-divergence for Improving Model Robustness. Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL).

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J. Xin, R. Tang, Y. Yu, J. Lin (2021). BERxiT: Better-fine-tuned and Wider-applicable Early Exit for *BERT. The 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL).

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H. Pham, S. Qian, J. Wang, T. Lutellier, J. Rosenthal, L. Tan, Y. Yu, N. Nagappan (2021). Problems and Opportunities in Training Deep-Learning Software Systems: An Analysis of Variance. 35th IEEE/ACM International Conference on Automated Software Engineering (ASE).

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X. Lian, K. Jain, J. Truszkowski, P. Poupart, Y. Yu (2021). Unsupervised Multilingual Alignment using Wasserstein Barycenters. International Joint Conference on Artificial Intelligence (IJCAI).

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J. Xin, R. Nogueira, Y. Yu, J. Lin (2020). Early Exiting BERT for Efficient Document Ranking. Proceedings of the First Workshop on Simple and Efficient Natural Language Processing (SustaiNLP 2020).

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Yi Shi, Zehua Guo, Xianbin Su, Luming Meng, Mingxuan Zhang, Jing Sun, Chao Wu, Minhua Zheng, Xueyin Shang, Xin Zou, Wangqiu Cheng, Yaoliang Yu, Yujia Cai, Chaoyi Zhang, Weidong Cai, Lin-Tai Da, Guang He, Ze-Guang Han (2020). DeepAntigen: a novel method for neoantigen prioritization via 3D genome and deep sparse learning. Bioinformatics.

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Yi Shi, Mingxuan Zhang, Luming Meng, Xianbin Su, Xueying Shang, Zehua Guo, Qingjiao Li, Mengna Lin, Xin Zou, Qing Luo, Yaoliang Yu, Yanting Wu, Lintai Da, Tom Weidong Cai, Guang He, Ze-Guang Han (2020). A novel neoantigen discovery approach based on chromatin high order conformation. BMC Med Genomics.

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T. Dockhorn, J. Ritchie, Y. Yu, I. Murray (2020). Density Deconvolution with Normalizing Flows. ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models.

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R. Tang, J. Lee, J. Xin, X. Liu, Y. Yu, J. Lin (2020). Showing Your Work Doesn't Always Work. Proceedings of the Association for Computational Linguistics (ACL).

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J. Xin, R. Tang, J. Lee, Y. Yu, J. Lin (2020). DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference. Proceedings of the Association for Computational Linguistics (ACL).

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P. Jaini, I. Kobyzev, Y. Yu, M. Brubaker (2020). Tails of Lipschitz Triangular Flows. International Conference on Machine Learning (ICML).

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K. Wu, W. Ding, R. Huang, Y. Yu (2020). On Minimax Optimality of GANs for Robust Mean Estimation. International Conference on Artificial Intelligence and Statistics (AISTATS).

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Y. Ma, V. Ganapathiraman, Y. Yu, X. Zhang (2020). Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space. International Conference on Machine Learning (ICML).

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K. Wu, H. Wang, Y. Yu (2020). Stronger and Faster Wasserstein Adversarial Attacks. International Conference on Machine Learning (ICML).

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G. Zhang, Y. Yu (2019). Convergence of Gradient Methods on Bilinear Zero-Sum Games. International Conference on Learning Representations (ICLR).

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K. Wu, Y. Yu (2019). Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin. NeurIPS Workshop on Maching Learning with Guarantees.

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S. Sun, Y. Yu (2019). Least-Squares Estimation of Weakly Convex Functions. International Conference on Artificial Intelligence and Statistics (AISTATS).

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M. Marchetti-Bowick, Y. Yu, W. Wu, E. Xing (2019). A Penalized Regression Model for the Joint Estimation of eQTL Associations and Gene Network Structure. Annals of Applied Statistics.

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J. Xin, J. Lin, Y. Yu (2019). What Part of the Neural Network Does This? Understanding LSTMs by Measuring and Dissecting Neurons. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP).

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P. Jaini, K. Selby, Y. Yu (2019). Sum-of-squares Polynomial Flow. International Conference on Machine Learning (ICML).

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J. Wang, S. Sun, Y. Yu (2019). Multivariate Triangular Quantile Maps for Novelty Detection. Advances in Neural Information Processing Systems (NeurIPS).

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P. Xie, J. Kim, Q. Ho, Y. Yu, E. Xing (2018). Orpheus: Efficient Distributed Machine Learning via System and Algorithm Co-design. ACM Symposium on Cloud Computing (SoCC).

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V. Ganapathiraman, Z. Shi, X. Zhang, Y. Yu (2018). Inductive Two-Layer Modeling with Parametric Bregman Transfer. International Conference on Machine Learning (ICML).

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Y. Zhou, Y. Liang, Y. Yu, W. Dai, E. Xing (2018). Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters. Journal of Machine Learning Research.

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P. Jaini, P. Poupart, Y. Yu (2018). Deep Homogeneous Mixture Models: Representation, Separation and Approximation. Advances in Neural Information Processing Systems (NeurIPS).

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Z. Shi, X. Zhang, Y. Yu (2017). Bregman Divergence for Stochastic Variance Reduction Methods: Adversarial Prediction and Saddle-Point Problems. Advances in Neural Information Processing Systems (NeurIPS).

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M. Law, Y. Yu, R. Urtasun, R. Zemel, E. Xing (2017). Efficient Multiple Instance Metric Learning using Weakly Supervised Data. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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J. Yin, Y. Yu (2017). Convex-constrained Sparse Additive Modeling and Its Extensions. Conference on Uncertainty in Artificial Intelligence (UAI).

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X. Chang, Y. Yu, Y. Yang (2017). Robust Top-$k$ Multiclass SVM for Visual Category Recognition. ACM Conference on Knowledge Discovery and Data Mining (KDD).

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P. Xie, Y. Deng, Y. Zhou, A. Kumar, Y. Yu, J. Zou, E. Xing (2017). Learning Latent Space Models with Angular Constraints. International Conference on Machine Learning (ICML).

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Y. Zhou, K. Yuan, Y. Yu, X. Ni, P. Xie, E. Xing, S. Xu (2017). Inference of Multiple-wave Population Admixture by Modeling Decay of Linkage Disequilibrium With Polynomial Functions. Heredity.

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X. Ma, Y. Gao, Z. Hu, Y. Yu, Y. Deng, E. Hovy (2017). Dropout with Expectation-Linear Regularization. International Conference on Learning Representations (ICLR).

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Y. Yu, X. Zhang, D. Schuurmans (2017). Generalized Conditional Gradient for Sparse Estimation. Journal of Machine Learning Research (JMLR).

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X. Chang, Y. Yu, Y. Yang, E. Xing (2016). They Are Not Equally Reliable: Semantic Event Search using Differentiated Concept Classifiers. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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M. Law, Y. Yu, M. Cord, E. Xing (2016). Closed-Form Training of Mahalanobis Distance for Supervised Clustering. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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V. Ganapathiraman, X. Zhang, Y. Yu, J. Wen (2016). Convex Two-Layer Modeling with Latent Structure. Advances in Neural Information Processing Systems (NeurIPS).

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X. Chang, Y. Yu, Y. Yang, E. Xing (2016). Semantic Pooling for Complex Event Analysis in Untrimmed Videos. IEEE Transactions on Pattern Analysis and Machine Intelligence.

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H. Cheng, Y. Yu, X. Zhang, E. Xing, D. Schuurmans (2016). Scalable and Sound Low-Rank Tensor Learning. International Conference on Artificial Intelligence and Statistics (AISTATS).

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Y. Zhou, Y. Yu, W. Dai, Y. Liang, E. Xing (2016). On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System. International Conference on Artificial Intelligence and Statistics (AISTATS).

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K. Kandasamy, Y. Yu (2016). Additive Approximations in High Dimensional Nonparametric Regression via the SALSA. International Conference on Machine Learning (ICML).

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P. Xie, J. Kim, Y. Zhou, Q. Ho, A. Kumar, Y. Yu, E. Xing (2016). Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting. Conference on Uncertainty in Artificial Intelligence (UAI).

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Y. Yu (2016). Online Learning and Optimization. Encyclopedia of Algorithms.

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Y. Yu, E. Xing (2015). Exact Algorithms for Isotonic Regression and Related. Journal of Physics: Conference Series.

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X. Chang, Y. Yu, Y. Yang, A. Hauptmann (2015). Searching Persuasively: Joint Event Detection and Evidence Recounting with Limited Supervision. ACM Conference on Multimedia (MM).

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E. Xing, Q. Ho, W. Dai, J. Kim, J. Wei, S. Lee, X. Zheng, P. Xie, A. Kumar, Y. Yu (2015). Petuum: A New Platform for Distributed Machine Learning on Big Data. IEEE Transactions on Big Data.

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X. Zheng, Y. Yu, E. Xing (2015). Linear Time Samplers for Supervised Topic Models using Compositional Proposals. ACM Conference on Knowledge Discovery and Data Mining (KDD).

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X. Chang, Y. Yang, A. Hauptmann, E. Xing, Y. Yu (2015). Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection. International Joint Conference on Artificial Intelligence (IJCAI).

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X. Chang, Y. Yang, E. Xing, Y. Yu (2015). Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM. International Conference on Machine Learning (ICML).

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Y. Yu, X. Zheng, M. Marchetti-Bowick, E. Xing (2015). Minimizing Nonconvex Non-Separable Functions. International Conference on Artificial Intelligence and Statistics (AISTATS).

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A. Yu, W. Ma, Y. Yu, J. Carbonell, S. Sra (2014). Efficient Structured Matrix Rank Minimization. Advances in Neural Information Processing Systems (NeurIPS).

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X. Zhang, Y. Yu, D. Schuurmans (2013). Polar Operators for Structured Sparse Estimation. Advances in Neural Information Processing Systems (NeurIPS).

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Y. Yu (2013). Better Approximation and Faster Algorithm Using the Proximal Average. Advances in Neural Information Processing Systems (NeurIPS).

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Y. Yu (2013). On Decomposing the Proximal Map. Advances in Neural Information Processing Systems (NeurIPS).

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Y. Yu, H. Cheng, D. Schuurmans, C. Szepesvári (2013). Characterizing the Representer Theorem. International Conference on Machine Learning (ICML).

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M. White, Y. Yu, X. Zhang, D. Schuurmans (2012). Convex Multi-view Subspace Learning. Advances in Neural Information Processing Systems (NeurIPS).

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X. Zhang, Y. Yu, D. Schuurmans (2012). Accelerated Training for Matrix-Norm Regularization: A Boosting Approach. Advances in Neural Information Processing Systems (NeurIPS).

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Y. Yu, Ö. Aslan, D. Schuurmans (2012). A Polynomial-time Form of Robust Regression. Advances in Neural Information Processing Systems (NeurIPS).

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Y. Yu, J. Neufeld, R. Kiros, X. Zhang, D. Schuurmans (2012). Regularizers versus Losses for Nonlinear Dimensionality Reduction. International Conference on Machine Learning (ICML).

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Y. Yu, C. Szepesvári (2012). Analysis of Kernel Mean Matching under Covariate Shift. International Conference on Machine Learning (ICML).

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X. Zhang, Y. Yu, M. White, R. Huang, D. Schuurmans (2011). Convex Sparse Coding, Subspace Learning, and Semi-Supervised Extensions. Association for the Advancement of Artificial Intelligence (AAAI).

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Y. Yu, J. Jiang, L. Zhang (2011). Distance Metric Learning by Minimal Distance Maximization. Pattern Recognition.

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Y. Yu, D. Schuurmans (2011). Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering. Conference on Uncertainty in Artificial Intelligence (UAI).

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Y. Yu, M. Yang, L. Xu, M. White, D. Schuurmans (2010). Relaxed Clipping: A Global Training Method for Robust Regression and Classification. Advances in Neural Information Processing Systems (NeurIPS).

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Y. Yu, Y. Zhang, C. Szepesvári (2009). Online TD(1) Meets Offline Monte Carlo. Multidisciplinary Symposium on Reinforcement Learning.

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Y. Yu, Y. Li, D. Schuurmans, C. Szepesvári (2009). A General Projection Property for Distribution Families. Advances in Neural Information Processing Systems (NeurIPS).

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Y. Yu, C. Szepesvári, Y. Li, D. Schuurmans (2009). A Conditional Value-at-Risk Approach for Uncertain Markov Decision Processes. Multidisciplinary Symposium on Reinforcement Learning.

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