| 2026 | AAAI | Demystifying Foreground-Background Memorization in Diffusion Models. | Jimmy Z. Di, Yiwei Lu, Yaoliang Yu, Gautam Kamath, Adam Dziedzic, Franziska Boenisch |
| 2025 | AAAI | Last-iterate Convergence in Regularized Graphon Mean Field Game. | Jing Dong, Baoxiang Wang, Yaoliang Yu |
| 2025 | AISTATS | Diffusion Models under Group Transformations. | Haoye Lu, Spencer Szabados, Yaoliang Yu |
| 2025 | ICLR | Leveraging Variable Sparsity to Refine Pareto Stationarity in Multi-Objective Optimization. | Zeou Hu, Yaoliang Yu |
| 2025 | ICML | A Comprehensive Framework for Analyzing the Convergence of Adam: Bridging the Gap with SGD. | Ruinan Jin, Xiao Li, Yaoliang Yu, Baoxiang Wang |
| 2025 | ICML | Stochastic Forward-Backward Deconvolution: Training Diffusion Models with Finite Noisy Datasets. | Haoye Lu, Qifan Wu, Yaoliang Yu |
| 2024 | AISTATS | Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games. | Jing Dong, Baoxiang Wang, Yaoliang Yu |
| 2024 | ICLR | Faster Approximation of Probabilistic and Distributional Values via Least Squares. | Weida Li, Yaoliang Yu |
| 2024 | ICML | Disguised Copyright Infringement of Latent Diffusion Models. | Yiwei Lu, Matthew Y. R. Yang, Zuoqiu Liu, Gautam Kamath, Yaoliang Yu |
| 2024 | ICML | Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning. | Saber Malekmohammadi, Yaoliang Yu, Yang Cao |
| 2023 | ACL | Operator Selection and Ordering in a Pipeline Approach to Efficiency Optimizations for Transformers. | Ji Xin, Raphael Tang, Zhiying Jiang, Yaoliang Yu, Jimmy Lin |
| 2023 | ICLR | Multi-Objective Reinforcement Learning: Convexity, Stationarity and Pareto Optimality. | Haoye Lu, Daniel Herman, Yaoliang Yu |
| 2023 | ICML | Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks. | Yiwei Lu, Gautam Kamath, Yaoliang Yu |
| 2022 | ICLR | Revisiting flow generative models for Out-of-distribution detection. | Dihong Jiang, Sun Sun, Yaoliang Yu |
| 2021 | ACL | The Art of Abstention: Selective Prediction and Error Regularization for Natural Language Processing. | Ji Xin, Raphael Tang, Yaoliang Yu, Jimmy Lin |
| 2021 | EACL | BERxiT: Early Exiting for BERT with Better Fine-Tuning and Extension to Regression. | Ji Xin, Raphael Tang, Yaoliang Yu, Jimmy Lin |
| 2021 | NAACL | Posterior Differential Regularization with f-divergence for Improving Model Robustness. | Hao Cheng, Xiaodong Liu, Lis Pereira, Yaoliang Yu, Jianfeng Gao |
| 2020 | ACL | Showing Your Work Doesn't Always Work. | Raphael Tang, Jaejun Lee, Ji Xin, Xinyu Liu, Yaoliang Yu, Jimmy Lin |
| 2020 | ACL | DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference. | Ji Xin, Raphael Tang, Jaejun Lee, Yaoliang Yu, Jimmy Lin |
| 2020 | AISTATS | On Minimax Optimality of GANs for Robust Mean Estimation. | Kaiwen Wu, Gavin Weiguang Ding, Ruitong Huang, Yaoliang Yu |
| 2020 | EMNLP | Early Exiting BERT for Efficient Document Ranking. | Ji Xin, Rodrigo Nogueira, Yaoliang Yu, Jimmy Lin |
| 2020 | ICLR | Convergence of Gradient Methods on Bilinear Zero-Sum Games. | Guojun Zhang, Yaoliang Yu |
| 2020 | ICML | Tails of Lipschitz Triangular Flows. | Priyank Jaini, Ivan Kobyzev, Yaoliang Yu, Marcus A. Brubaker |
| 2020 | ICML | Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space. | Yingyi Ma, Vignesh Ganapathiraman, Yaoliang Yu, Xinhua Zhang |
| 2020 | ICML | Stronger and Faster Wasserstein Adversarial Attacks. | Kaiwen Wu, Allen Houze Wang, Yaoliang Yu |
| 2020 | IJCAI | Unsupervised Multilingual Alignment using Wasserstein Barycenter. | Xin Lian, Kshitij Jain, Jakub Truszkowski, Pascal Poupart, Yaoliang Yu |
| 2019 | AISTATS | Least Squares Estimation of Weakly Convex Functions. | Sun Sun, Yaoliang Yu |
| 2019 | EMNLP | What Part of the Neural Network Does This? Understanding LSTMs by Measuring and Dissecting Neurons. | Ji Xin, Jimmy Lin, Yaoliang Yu |
| 2019 | ICML | Sum-of-Squares Polynomial Flow. | Priyank Jaini, Kira A. Selby, Yaoliang Yu |
| 2019 | ICML | Distributional Reinforcement Learning for Efficient Exploration. | Borislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu |
| 2018 | CLOUD | Orpheus: Efficient Distributed Machine Learning via System and Algorithm Co-design. | Pengtao Xie, Jin Kyu Kim, Qirong Ho, Yaoliang Yu, Eric P. Xing |
| 2018 | ICML | Inductive Two-layer Modeling with Parametric Bregman Transfer. | Vignesh Ganapathiraman, Zhan Shi, Xinhua Zhang, Yaoliang Yu |
| 2017 | CVPR | Efficient Multiple Instance Metric Learning Using Weakly Supervised Data. | Marc T. Law, Yaoliang Yu, Raquel Urtasun, Richard S. Zemel, Eric P. Xing |
| 2017 | ICLR | Dropout with Expectation-linear Regularization. | Xuezhe Ma, Yingkai Gao, Zhiting Hu, Yaoliang Yu, Yuntian Deng, Eduard H. Hovy |
| 2017 | ICML | Learning Latent Space Models with Angular Constraints. | Pengtao Xie, Yuntian Deng, Yi Zhou, Abhimanu Kumar, Yaoliang Yu, James Zou, Eric P. Xing |
| 2017 | KDD | Robust Top- | Xiaojun Chang, Yaoliang Yu, Yi Yang |
| 2017 | UAI | Convex-constrained Sparse Additive Modeling and Its Extensions. | Junming Yin, Yaoliang Yu |
| 2016 | AISTATS | Scalable and Sound Low-Rank Tensor Learning. | Hao Cheng, Yaoliang Yu, Xinhua Zhang, Eric P. Xing, Dale Schuurmans |
| 2016 | AISTATS | On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System. | Yi Zhou, Yaoliang Yu, Wei Dai, Yingbin Liang, Eric P. Xing |
| 2016 | CVPR | They are Not Equally Reliable: Semantic Event Search Using Differentiated Concept Classifiers. | Xiaojun Chang, Yaoliang Yu, Yi Yang, Eric P. Xing |
| 2016 | CVPR | Closed-Form Training of Mahalanobis Distance for Supervised Clustering. | Marc T. Law, Yaoliang Yu, Matthieu Cord, Eric P. Xing |
| 2016 | ICML | Additive Approximations in High Dimensional Nonparametric Regression via the SALSA. | Kirthevasan Kandasamy, Yaoliang Yu |
| 2016 | UAI | Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting. | Pengtao Xie, Jin Kyu Kim, Yi Zhou, Qirong Ho, Abhimanu Kumar, Yaoliang Yu, Eric P. Xing |
| 2015 | AISTATS | Minimizing Nonconvex Non-Separable Functions. | Yaoliang Yu, Xun Zheng, Micol Marchetti-Bowick, Eric P. Xing |
| 2015 | ICML | Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM. | Xiaojun Chang, Yi Yang, Eric P. Xing, Yaoliang Yu |
| 2015 | IJCAI | Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection. | Xiaojun Chang, Yi Yang, Alexander G. Hauptmann, Eric P. Xing, Yaoliang Yu |
| 2015 | KDD | Petuum: A New Platform for Distributed Machine Learning on Big Data. | Eric P. Xing, Qirong Ho, Wei Dai, Jin Kyu Kim, Jinliang Wei, Seunghak Lee, Xun Zheng, Pengtao Xie, Abhimanu Kumar, Yaoliang Yu |
| 2015 | KDD | Linear Time Samplers for Supervised Topic Models using Compositional Proposals. | Xun Zheng, Yaoliang Yu, Eric P. Xing |
| 2013 | ICML | Characterizing the Representer Theorem. | Yaoliang Yu, Hao Cheng, Dale Schuurmans, Csaba Szepesvri |
| 2012 | ICML | Regularizers versus Losses for Nonlinear Dimensionality Reduction: A Factored View with New Convex Relaxations. | James Neufeld, Yaoliang Yu, Xinhua Zhang, Ryan Kiros, Dale Schuurmans |
| 2012 | ICML | Analysis of Kernel Mean Matching under Covariate Shift. | Yaoliang Yu, Csaba Szepesvri |
| 2011 | AAAI | Convex Sparse Coding, Subspace Learning, and Semi-Supervised Extensions. | Xinhua Zhang, Yaoliang Yu, Martha White, Ruitong Huang, Dale Schuurmans |
| 2011 | UAI | Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering. | Yaoliang Yu, Dale Schuurmans |
| 2007 | ICIP | A Novel Facial Feature Point Localization Method on 3D Faces. | Peng Guan, Yaoliang Yu, Liming Zhang |
| 2007 | ISNN | Discriminant Analysis with Label Constrained Graph Partition. | Peng Guan, Yaoliang Yu, Liming Zhang |
| 2007 | ISNN | Extensions of Manifold Learning Algorithms in Kernel Feature Space. | Yaoliang Yu, Peng Guan, Liming Zhang |