Yu, Yaoliang

75 publications

ICML 2025 A Comprehensive Framework for Analyzing the Convergence of Adam: Bridging the Gap with SGD Ruinan Jin, Xiao Li, Yaoliang Yu, Baoxiang Wang

NeurIPS 2025 Adaptive Context Length Optimization with Low-Frequency Truncation for Multi-Agent Reinforcement Learning Wenchang Duan, Yaoliang Yu, Jiwan He, Yi Shi

NeurIPS 2025 BridgePure: Limited Protection Leakage Can Break Black-Box Data Protection Yihan Wang, Yiwei Lu, Xiao-Shan Gao, Gautam Kamath, Yaoliang Yu

NeurIPS 2025 DiffBreak: Is Diffusion-Based Purification Robust? Andre Kassis, Urs Hengartner, Yaoliang Yu

AISTATS 2025 Diffusion Models Under Group Transformations Haoye Lu, Spencer Szabados, Yaoliang Yu

AAAI 2025 Last-Iterate Convergence in Regularized Graphon Mean Field Game Jing Dong, Baoxiang Wang, Yaoliang Yu

ICLR 2025 Leveraging Variable Sparsity to Refine Pareto Stationarity in Multi-Objective Optimization Zeou Hu, Yaoliang Yu

TMLR 2025 MUC: Machine Unlearning for Contrastive Learning with Black-Box Evaluation Yihan Wang, Yiwei Lu, Guojun Zhang, Franziska Boenisch, Adam Dziedzic, Yaoliang Yu, Xiao-Shan Gao

ICLRW 2025 SFBD: A Method for Training Diffusion Models with Noisy Data Haoye Lu, Qifan Wu, Yaoliang Yu

ICML 2025 Stochastic Forward–Backward Deconvolution: Training Diffusion Models with Finite Noisy Datasets Haoye Lu, Qifan Wu, Yaoliang Yu

NeurIPS 2025 Uncoupled and Convergent Learning in Monotone Games Under Bandit Feedback Jing Dong, Baoxiang Wang, Yaoliang Yu

ICMLW 2024 Alignment Calibration: Machine Unlearning for Contrastive Learning Under Auditing Yihan Wang, Yiwei Lu, Guojun Zhang, Franziska Boenisch, Adam Dziedzic, Yaoliang Yu, Xiao-Shan Gao

AISTATS 2024 Convergence to Nash Equilibrium and No-Regret Guarantee in (Markov) Potential Games Jing Dong, Baoxiang Wang, Yaoliang Yu

ICMLW 2024 Diffusion Models with Group Equivariance Haoye Lu, Spencer Szabados, Yaoliang Yu

ICML 2024 Disguised Copyright Infringement of Latent Diffusion Models Yiwei Lu, Matthew Y. R. Yang, Zuoqiu Liu, Gautam Kamath, Yaoliang Yu

ICLR 2024 Faster Approximation of Probabilistic and Distributional Values via Least Squares Weida Li, Yaoliang Yu

ICML 2024 Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning Saber Malekmohammadi, Yaoliang Yu, Yang Cao

ICMLW 2024 On the Robustness of Neural Networks Quantization Against Data Poisoning Attacks Yiwei Lu, Yihan Wang, Guojun Zhang, Yaoliang Yu

NeurIPS 2024 One Sample Fits All: Approximating All Probabilistic Values Simultaneously and Efficiently Weida Li, Yaoliang Yu

NeurIPSW 2024 Uncoupled and Convergent Learning in Monotone Games Under Bandit Feedback Jing Dong, Baoxiang Wang, Yaoliang Yu

TMLR 2023 $f$-MICL: Understanding and Generalizing InfoNCE-Based Contrastive Learning Yiwei Lu, Guojun Zhang, Sun Sun, Hongyu Guo, Yaoliang Yu

NeurIPS 2023 BatchNorm Allows Unsupervised Radial Attacks Amur Ghose, Apurv Gupta, Yaoliang Yu, Pascal Poupart

ICMLW 2023 CM-GAN: Stabilizing GAN Training with Consistency Models Haoye Lu, Yiwei Lu, Dihong Jiang, Spencer Ryan Szabados, Sun Sun, Yaoliang Yu

ICML 2023 Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks Yiwei Lu, Gautam Kamath, Yaoliang Yu

NeurIPS 2023 Functional Renyi Differential Privacy for Generative Modeling Dihong Jiang, Sun Sun, Yaoliang Yu

ICMLW 2023 Functional Renyi Differential Privacy for Generative Modeling Dihong Jiang, Sun Sun, Yaoliang Yu

ICLR 2023 Multi-Objective Reinforcement Learning: Convexity, Stationarity and Pareto Optimality Haoye Lu, Daniel Herman, Yaoliang Yu

TMLR 2023 Proportional Fairness in Federated Learning Guojun Zhang, Saber Malekmohammadi, Xi Chen, Yaoliang Yu

NeurIPS 2023 Robust Data Valuation with Weighted Banzhaf Values Weida Li, Yaoliang Yu

NeurIPS 2023 Understanding Neural Network Binarization with Forward and Backward Proximal Quantizers Yiwei Lu, Yaoliang Yu, Xinlin Li, Vahid Partovi Nia

ICLRW 2022 Conditional Generative Quantile Networks via Optimal Transport Jesse Sun, Dihong Jiang, Yaoliang Yu

NeurIPSW 2022 Geometric Attacks on Batch Normalization Amur Ghose, Apurv Gupta, Yaoliang Yu, Pascal Poupart

TMLR 2022 Indiscriminate Data Poisoning Attacks on Neural Networks Yiwei Lu, Gautam Kamath, Yaoliang Yu

NeurIPSW 2022 Indiscriminate Data Poisoning Attacks on Neural Networks Yiwei Lu, Gautam Kamath, Yaoliang Yu

NeurIPSW 2022 Indiscriminate Data Poisoning Attacks on Neural Networks Yiwei Lu, Gautam Kamath, Yaoliang Yu

JMLR 2022 Optimality and Stability in Non-Convex Smooth Games Guojun Zhang, Pascal Poupart, Yaoliang Yu

ICLR 2022 Revisiting Flow Generative Models for Out-of-Distribution Detection Dihong Jiang, Sun Sun, Yaoliang Yu

NeurIPS 2021 Are My Deep Learning Systems Fair? an Empirical Study of Fixed-Seed Training Shangshu Qian, Viet Hung Pham, Thibaud Lutellier, Zeou Hu, Jungwon Kim, Lin Tan, Yaoliang Yu, Jiahao Chen, Sameena Shah

NeurIPS 2021 Demystifying and Generalizing BinaryConnect Tim Dockhorn, Yaoliang Yu, Eyyüb Sari, Mahdi Zolnouri, Vahid Partovi Nia

NeurIPS 2021 Quantifying and Improving Transferability in Domain Generalization Guojun Zhang, Han Zhao, Yaoliang Yu, Pascal Poupart

NeurIPS 2021 S$^3$: Sign-Sparse-Shift Reparametrization for Effective Training of Low-Bit Shift Networks Xinlin Li, Bang Liu, Yaoliang Yu, Wulong Liu, Chunjing Xu, Vahid Partovi Nia

ICLR 2020 Convergence of Gradient Methods on Bilinear Zero-Sum Games Guojun Zhang, Yaoliang Yu

ICML 2020 Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space Yingyi Ma, Vignesh Ganapathiraman, Yaoliang Yu, Xinhua Zhang

AISTATS 2020 On Minimax Optimality of GANs for Robust Mean Estimation Kaiwen Wu, Gavin Weiguang Ding, Ruitong Huang, Yaoliang Yu

ICML 2020 Stronger and Faster Wasserstein Adversarial Attacks Kaiwen Wu, Allen Wang, Yaoliang Yu

ICML 2020 Tails of Lipschitz Triangular Flows Priyank Jaini, Ivan Kobyzev, Yaoliang Yu, Marcus Brubaker

IJCAI 2020 Unsupervised Multilingual Alignment Using Wasserstein Barycenter Xin Lian, Kshitij Jain, Jakub Truszkowski, Pascal Poupart, Yaoliang Yu

ICML 2019 Distributional Reinforcement Learning for Efficient Exploration Borislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu

AISTATS 2019 Least Squares Estimation of Weakly Convex Functions Sun Sun, Yaoliang Yu

NeurIPS 2019 Multivariate Triangular Quantile Maps for Novelty Detection Jingjing Wang, Sun Sun, Yaoliang Yu

ICML 2019 Sum-of-Squares Polynomial Flow Priyank Jaini, Kira A. Selby, Yaoliang Yu

NeurIPS 2018 Deep Homogeneous Mixture Models: Representation, Separation, and Approximation Priyank Jaini, Pascal Poupart, Yaoliang Yu

JMLR 2018 Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters Yi Zhou, Yingbin Liang, Yaoliang Yu, Wei Dai, Eric P. Xing

ICML 2018 Inductive Two-Layer Modeling with Parametric Bregman Transfer Vignesh Ganapathiraman, Zhan Shi, Xinhua Zhang, Yaoliang Yu

NeurIPS 2017 Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction Zhan Shi, Xinhua Zhang, Yaoliang Yu

UAI 2017 Convex-Constrained Sparse Additive Modeling and Its Extensions Junming Yin, Yaoliang Yu

ICLR 2017 Dropout with Expectation-Linear Regularization Xuezhe Ma, Yingkai Gao, Zhiting Hu, Yaoliang Yu, Yuntian Deng, Eduard H. Hovy

CVPR 2017 Efficient Multiple Instance Metric Learning Using Weakly Supervised Data Marc T. Law, Yaoliang Yu, Raquel Urtasun, Richard S. Zemel, Eric P. Xing

JMLR 2017 Generalized Conditional Gradient for Sparse Estimation Yaoliang Yu, Xinhua Zhang, Dale Schuurmans

ICML 2017 Learning Latent Space Models with Angular Constraints Pengtao Xie, Yuntian Deng, Yi Zhou, Abhimanu Kumar, Yaoliang Yu, James Zou, Eric P. Xing

ICML 2016 Additive Approximations in High Dimensional Nonparametric Regression via the SALSA Kirthevasan Kandasamy, Yaoliang Yu

CVPR 2016 Closed-Form Training of Mahalanobis Distance for Supervised Clustering Marc T. Law, YaoLiang Yu, Matthieu Cord, Eric P. Xing

NeurIPS 2016 Convex Two-Layer Modeling with Latent Structure Vignesh Ganapathiraman, Xinhua Zhang, Yaoliang Yu, Junfeng Wen

UAI 2016 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

AISTATS 2016 On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System Yi Zhou, Yaoliang Yu, Wei Dai, Yingbin Liang, Eric P. Xing

AISTATS 2016 Scalable and Sound Low-Rank Tensor Learning Hao Cheng, Yaoliang Yu, Xinhua Zhang, Eric P. Xing, Dale Schuurmans

ICML 2015 Complex Event Detection Using Semantic Saliency and Nearly-Isotonic SVM Xiaojun Chang, Yi Yang, Eric Xing, Yaoliang Yu

AISTATS 2015 Minimizing Nonconvex Non-Separable Functions Yaoliang Yu, Xun Zheng, Micol Marchetti-Bowick, Eric P. Xing

IJCAI 2015 Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection Xiaojun Chang, Yi Yang, Alexander G. Hauptmann, Eric P. Xing, Yaoliang Yu

NeurIPS 2014 Efficient Structured Matrix Rank Minimization Adams Wei Yu, Wanli Ma, Yaoliang Yu, Jaime Carbonell, Suvrit Sra

ICML 2013 Characterizing the Representer Theorem Yaoliang Yu, Hao Cheng, Dale Schuurmans, Csaba Szepesvari

ICML 2012 Analysis of Kernel Mean Matching Under Covariate Shift Yaoliang Yu, Csaba Szepesvári

ICML 2012 Regularizers Versus Losses for Nonlinear Dimensionality Reduction: A Factored View with New Convex Relaxations James Neufeld, Yaoliang Yu, Xinhua Zhang, Ryan Kiros, Dale Schuurmans

AAAI 2011 Convex Sparse Coding, Subspace Learning, and Semi-Supervised Extensions Xinhua Zhang, Yaoliang Yu, Martha White, Ruitong Huang, Dale Schuurmans

UAI 2011 Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering Yaoliang Yu, Dale Schuurmans