Yaoliang Yu - RankMe

158 papers A* 48A 11Journal 74Unranked 24

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	Demystifying Foreground-Background Memorization in Diffusion Models. AAAI Jimmy Z. Di, Yiwei Lu, Yaoliang Yu, Gautam Kamath, Adam Dziedzic, Franziska Boenisch
2026	J	`jnl`	Efficient Bilevel Optimization with KFAC-Based Hypergradients. CoRR Disen Liao, Felix Dangel, Yaoliang Yu
2026	J	`jnl`	TreeGrad-Ranker: Feature Ranking via O(L)-Time Gradients for Decision Trees. CoRR Weida Li, Yaoliang Yu, Bryan Kian Hsiang Low
2025	A*	`conf`	A Comprehensive Framework for Analyzing the Convergence of Adam: Bridging the Gap with SGD. ICML Ruinan Jin, Xiao Li, Yaoliang Yu, Baoxiang Wang
2025	J	`jnl`	Adaptive Context Length Optimization with Low-Frequency Truncation for Multi-Agent Reinforcement Learning. CoRR Wenchang Duan, Yaoliang Yu, Jiwan He, Yi Shi
2025	J	`jnl`	Demystifying Foreground-Background Memorization in Diffusion Models. CoRR Jimmy Z. Di, Yiwei Lu, Yaoliang Yu, Gautam Kamath, Adam Dziedzic, Franziska Boenisch
2025	A	`conf`	Diffusion Models under Group Transformations. AISTATS Haoye Lu, Spencer Szabados, Yaoliang Yu
2025	A*	`conf`	Last-iterate Convergence in Regularized Graphon Mean Field Game. AAAI Jing Dong, Baoxiang Wang, Yaoliang Yu
2025	A*	`conf`	Leveraging Variable Sparsity to Refine Pareto Stationarity in Multi-Objective Optimization. ICLR Zeou Hu, Yaoliang Yu
2025	J	`jnl`	MUC: Machine Unlearning for Contrastive Learning with Black-box Evaluation. Trans. Mach. Learn. Res. Yihan Wang, Yiwei Lu, Guojun Zhang, Franziska Boenisch, Adam Dziedzic, Yaoliang Yu, Xiao-Shan Gao
2025	J	`jnl`	Not All Samples Are Equal: Quantifying Instance-level Difficulty in Targeted Data Poisoning. CoRR William Xu, Yiwei Lu, Yihan Wang, Matthew Y. R. Yang, Zuoqiu Liu, Gautam Kamath, Yaoliang Yu
2025	J	`jnl`	SFBD Flow: A Continuous-Optimization Framework for Training Diffusion Models with Noisy Samples. CoRR Haoye Lu, Darren Lo, Yaoliang Yu
2025	J	`jnl`	SFBD-OMNI: Bridge models for lossy measurement restoration with limited clean samples. CoRR Haoye Lu, Yaoliang Yu, Darren Ho
2025	A*	`conf`	Stochastic Forward-Backward Deconvolution: Training Diffusion Models with Finite Noisy Datasets. ICML Haoye Lu, Qifan Wu, Yaoliang Yu
2025	J	`jnl`	Stochastic Forward-Backward Deconvolution: Training Diffusion Models with Finite Noisy Datasets. CoRR Haoye Lu, Qifan Wu, Yaoliang Yu
2024	J	`jnl`	A Comprehensive Framework for Analyzing the Convergence of Adam: Bridging the Gap with SGD. CoRR Ruinan Jin, Xiao Li, Yaoliang Yu, Baoxiang Wang
2024	J	`jnl`	Alignment Calibration: Machine Unlearning for Contrastive Learning under Auditing. CoRR Yihan Wang, Yiwei Lu, Guojun Zhang, Franziska Boenisch, Adam Dziedzic, Yaoliang Yu, Xiao-Shan Gao
2024	J	`jnl`	BridgePure: Revealing the Fragility of Black-box Data Protection. CoRR Yihan Wang, Yiwei Lu, Xiao-Shan Gao, Gautam Kamath, Yaoliang Yu
2024	A	`conf`	Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games. AISTATS Jing Dong, Baoxiang Wang, Yaoliang Yu
2024	J	`jnl`	Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games. CoRR Jing Dong, Baoxiang Wang, Yaoliang Yu
2024	A*	`conf`	Disguised Copyright Infringement of Latent Diffusion Models. ICML Yiwei Lu, Matthew Y. R. Yang, Zuoqiu Liu, Gautam Kamath, Yaoliang Yu
2024	J	`jnl`	Disguised Copyright Infringement of Latent Diffusion Models. CoRR Yiwei Lu, Matthew Y. R. Yang, Zuoqiu Liu, Gautam Kamath, Yaoliang Yu
2024	A*	`conf`	Faster Approximation of Probabilistic and Distributional Values via Least Squares. ICLR Weida Li, Yaoliang Yu
2024	—	`conf`	Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors. SaTML Yiwei Lu, Matthew Y. R. Yang, Gautam Kamath, Yaoliang Yu
2024	J	`jnl`	Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors. CoRR Yiwei Lu, Matthew Y. R. Yang, Gautam Kamath, Yaoliang Yu
2024	J	`jnl`	Last-iterate Convergence in Regularized Graphon Mean Field Game. CoRR Jing Dong, Baoxiang Wang, Yaoliang Yu
2024	A*	`conf`	Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning. ICML Saber Malekmohammadi, Yaoliang Yu, Yang Cao
2024	J	`jnl`	Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning. CoRR Saber Malekmohammadi, Yaoliang Yu, Yang Cao
2024	A*	`conf`	One Sample Fits All: Approximating All Probabilistic Values Simultaneously and Efficiently. NeurIPS Weida Li, Yaoliang Yu
2024	J	`jnl`	One Sample Fits All: Approximating All Probabilistic Values Simultaneously and Efficiently. CoRR Weida Li, Yaoliang Yu
2024	J	`jnl`	Structure Preserving Diffusion Models. CoRR Haoye Lu, Spencer Szabados, Yaoliang Yu
2024	J	`jnl`	Uncoupled and Convergent Learning in Monotone Games under Bandit Feedback. CoRR Jing Dong, Baoxiang Wang, Yaoliang Yu
2024	J	`jnl`	Understanding Neural Network Binarization with Forward and Backward Proximal Quantizers. CoRR Yiwei Lu, Yaoliang Yu, Xinlin Li, Vahid Partovi Nia
2024	J	`jnl`	Unlocking The Potential of Adaptive Attacks on Diffusion-Based Purification. CoRR Andre Kassis, Urs Hengartner, Yaoliang Yu
2024	J	`jnl`	f-MICL: Understanding and Generalizing InfoNCE-based Contrastive Learning. CoRR Yiwei Lu, Guojun Zhang, Sun Sun, Hongyu Guo, Yaoliang Yu
2023	A*	`conf`	Batchnorm Allows Unsupervised Radial Attacks. NeurIPS Amur Ghose, Apurv Gupta, Yaoliang Yu, Pascal Poupart
2023	J	`jnl`	Exploring the Limits of Indiscriminate Data Poisoning Attacks. CoRR Yiwei Lu, Gautam Kamath, Yaoliang Yu
2023	A*	`conf`	Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks. ICML Yiwei Lu, Gautam Kamath, Yaoliang Yu
2023	A*	`conf`	Functional Renyi Differential Privacy for Generative Modeling. NeurIPS Dihong Jiang, Sun Sun, Yaoliang Yu
2023	A*	`conf`	Multi-Objective Reinforcement Learning: Convexity, Stationarity and Pareto Optimality. ICLR Haoye Lu, Daniel Herman, Yaoliang Yu
2023	—	`conf`	Operator Selection and Ordering in a Pipeline Approach to Efficiency Optimizations for Transformers. ACL (Findings) Ji Xin, Raphael Tang, Zhiying Jiang, Yaoliang Yu, Jimmy Lin
2023	J	`jnl`	Proportional Fairness in Federated Learning. Trans. Mach. Learn. Res. Guojun Zhang, Saber Malekmohammadi, Xi Chen, Yaoliang Yu
2023	A*	`conf`	Robust Data Valuation with Weighted Banzhaf Values. NeurIPS Weida Li, Yaoliang Yu
2023	A*	`conf`	Understanding Neural Network Binarization with Forward and Backward Proximal Quantizers. NeurIPS Yiwei Lu, Yaoliang Yu, Xinlin Li, Vahid Partovi Nia
2023	J	`jnl`	f-MICL: Understanding and Generalizing InfoNCE-based Contrastive Learning. Trans. Mach. Learn. Res. Yiwei Lu, Guojun Zhang, Sun Sun, Hongyu Guo, Yaoliang Yu
2022	J	`jnl`	Building an Efficiency Pipeline: Commutativity and Cumulativeness of Efficiency Operators for Transformers. CoRR Ji Xin, Raphael Tang, Zhiying Jiang, Yaoliang Yu, Jimmy Lin
2022	J	`jnl`	DP CoRR Dihong Jiang, Guojun Zhang, Mahdi Karami, Xi Chen, Yunfeng Shao, Yaoliang Yu
2022	J	`jnl`	Equality Is Not Equity: Proportional Fairness in Federated Learning. CoRR Guojun Zhang, Saber Malekmohammadi, Xi Chen, Yaoliang Yu
2022	J	`jnl`	Federated Learning Meets Multi-Objective Optimization. IEEE Trans. Netw. Sci. Eng. Zeou Hu, Kiarash Shaloudegi, Guojun Zhang, Yaoliang Yu
2022	J	`jnl`	Indiscriminate Data Poisoning Attacks on Neural Networks. CoRR Yiwei Lu, Gautam Kamath, Yaoliang Yu
2022	J	`jnl`	Indiscriminate Data Poisoning Attacks on Neural Networks. Trans. Mach. Learn. Res. Yiwei Lu, Gautam Kamath, Yaoliang Yu
2022	J	`jnl`	Mitigating Data Heterogeneity in Federated Learning with Data Augmentation. CoRR Artur Back de Luca, Guojun Zhang, Xi Chen, Yaoliang Yu
2022	J	`jnl`	Network Comparison with Interpretable Contrastive Network Representation Learning. J. Data Sci. Stat. Vis. Takanori Fujiwara, Jian Zhao, Francine Chen, Yaoliang Yu, Kwan-Liu Ma
2022	J	`jnl`	Optimality and Stability in Non-Convex Smooth Games. J. Mach. Learn. Res. Guojun Zhang, Pascal Poupart, Yaoliang Yu
2022	A*	`conf`	Revisiting flow generative models for Out-of-distribution detection. ICLR Dihong Jiang, Sun Sun, Yaoliang Yu
2022	J	`jnl`	Towards Explanation for Unsupervised Graph-Level Representation Learning. CoRR Qinghua Zheng, Jihong Wang, Minnan Luo, Yaoliang Yu, Jundong Li, Lina Yao, Xiaojun Chang
2021	J	`jnl`	An Operator Splitting View of Federated Learning. CoRR Saber Malekmohammadi, Kiarash Shaloudegi, Zeou Hu, Yaoliang Yu
2021	A*	`conf`	Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training. NeurIPS Shangshu Qian, Hung Viet Pham, Thibaud Lutellier, Zeou Hu, Jungwon Kim, Lin Tan, Yaoliang Yu, Jiahao Chen, Sameena Shah
2021	A	`conf`	BERxiT: Early Exiting for BERT with Better Fine-Tuning and Extension to Regression. EACL Ji Xin, Raphael Tang, Yaoliang Yu, Jimmy Lin
2021	A*	`conf`	DEVIATE: A Deep Learning Variance Testing Framework. ASE Hung Viet Pham, Mijung Kim, Lin Tan, Yaoliang Yu, Nachiappan Nagappan
2021	A*	`conf`	Demystifying and Generalizing BinaryConnect. NeurIPS Tim Dockhorn, Yaoliang Yu, Eyyüb Sari, Mahdi Zolnouri, Vahid Partovi Nia
2021	J	`jnl`	Demystifying and Generalizing BinaryConnect. CoRR Tim Dockhorn, Yaoliang Yu, Eyyüb Sari, Mahdi Zolnouri, Vahid Partovi Nia
2021	—	`conf`	Posterior Differential Regularization with f-divergence for Improving Model Robustness. NAACL-HLT Hao Cheng, Xiaodong Liu, Lis Pereira, Yaoliang Yu, Jianfeng Gao
2021	A*	`conf`	Quantifying and Improving Transferability in Domain Generalization. NeurIPS Guojun Zhang, Han Zhao, Yaoliang Yu, Pascal Poupart
2021	J	`jnl`	Quantifying and Improving Transferability in Domain Generalization. CoRR Guojun Zhang, Han Zhao, Yaoliang Yu, Pascal Poupart
2021	J	`jnl`	S CoRR Xinlin Li, Bang Liu, Yaoliang Yu, Wulong Liu, Chunjing Xu, Vahid Partovi Nia
2021	A*	`conf`	S$^3$: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks. NeurIPS Xinlin Li, Bang Liu, Yaoliang Yu, Wulong Liu, Chunjing Xu, Vahid Partovi Nia
2021	—	`conf`	Splitting Algorithms for Federated Learning. PKDD/ECML Workshops (1) Saber Malekmohammadi, Kiarash Shaloudegi, Zeou Hu, Yaoliang Yu
2021	—	`conf`	The Art of Abstention: Selective Prediction and Error Regularization for Natural Language Processing. ACL/IJCNLP (1) Ji Xin, Raphael Tang, Yaoliang Yu, Jimmy Lin
2020	J	`jnl`	Complete Hierarchy of Relaxation for Constrained Signomial Positivity. CoRR Allen Houze Wang, Priyank Jaini, Yaoliang Yu, Pascal Poupart
2020	A*	`conf`	Convergence of Gradient Methods on Bilinear Zero-Sum Games. ICLR Guojun Zhang, Yaoliang Yu
2020	A*	`conf`	Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space. ICML Yingyi Ma, Vignesh Ganapathiraman, Yaoliang Yu, Xinhua Zhang
2020	J	`jnl`	Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space. CoRR Yingyi Ma, Vignesh Ganapathiraman, Yaoliang Yu, Xinhua Zhang
2020	A*	`conf`	DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference. ACL Ji Xin, Raphael Tang, Jaejun Lee, Yaoliang Yu, Jimmy Lin
2020	J	`jnl`	DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference. CoRR Ji Xin, Raphael Tang, Jaejun Lee, Yaoliang Yu, Jimmy Lin
2020	J	`jnl`	DeepAntigen: a novel method for neoantigen prioritization via 3D genome and deep sparse learning. Bioinform. 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, Zeguang Han
2020	J	`jnl`	Density Deconvolution with Normalizing Flows. CoRR Tim Dockhorn, James A. Ritchie, Yaoliang Yu, Iain Murray
2020	—	`conf`	Early Exiting BERT for Efficient Document Ranking. SustaiNLP@EMNLP Ji Xin, Rodrigo Nogueira, Yaoliang Yu, Jimmy Lin
2020	J	`jnl`	FedMGDA+: Federated Learning meets Multi-objective Optimization. CoRR Zeou Hu, Kiarash Shaloudegi, Guojun Zhang, Yaoliang Yu
2020	J	`jnl`	Interpretable Contrastive Learning for Networks. CoRR Takanori Fujiwara, Jian Zhao, Francine Chen, Yaoliang Yu, Kwan-Liu Ma
2020	J	`jnl`	Newton-type Methods for Minimax Optimization. CoRR Guojun Zhang, Kaiwen Wu, Pascal Poupart, Yaoliang Yu
2020	J	`jnl`	OLALA: Object-Level Active Learning Based Layout Annotation. CoRR Zejiang Shen, Jian Zhao, Melissa Dell, Yaoliang Yu, Weining Li
2020	A	`conf`	On Minimax Optimality of GANs for Robust Mean Estimation. AISTATS Kaiwen Wu, Gavin Weiguang Ding, Ruitong Huang, Yaoliang Yu
2020	J	`jnl`	Optimality and Stability in Non-Convex-Non-Concave Min-Max Optimization. CoRR Guojun Zhang, Pascal Poupart, Yaoliang Yu
2020	J	`jnl`	Posterior Differential Regularization with f-divergence for Improving Model Robustness. CoRR Hao Cheng, Xiaodong Liu, Lis Pereira, Yaoliang Yu, Jianfeng Gao
2020	A*	`conf`	Problems and Opportunities in Training Deep Learning Software Systems: An Analysis of Variance. ASE Hung Viet Pham, Shangshu Qian, Jiannan Wang, Thibaud Lutellier, Jonathan Rosenthal, Lin Tan, Yaoliang Yu, Nachiappan Nagappan
2020	A*	`conf`	Showing Your Work Doesn't Always Work. ACL Raphael Tang, Jaejun Lee, Ji Xin, Xinyu Liu, Yaoliang Yu, Jimmy Lin
2020	J	`jnl`	Showing Your Work Doesn't Always Work. CoRR Raphael Tang, Jaejun Lee, Ji Xin, Xinyu Liu, Yaoliang Yu, Jimmy Lin
2020	A*	`conf`	Stronger and Faster Wasserstein Adversarial Attacks. ICML Kaiwen Wu, Allen Houze Wang, Yaoliang Yu
2020	J	`jnl`	Stronger and Faster Wasserstein Adversarial Attacks. CoRR Kaiwen Wu, Allen Houze Wang, Yaoliang Yu
2020	A*	`conf`	Tails of Lipschitz Triangular Flows. ICML Priyank Jaini, Ivan Kobyzev, Yaoliang Yu, Marcus A. Brubaker
2020	A*	`conf`	Unsupervised Multilingual Alignment using Wasserstein Barycenter. IJCAI Xin Lian, Kshitij Jain, Jakub Truszkowski, Pascal Poupart, Yaoliang Yu
2020	J	`jnl`	Unsupervised Multilingual Alignment using Wasserstein Barycenter. CoRR Xin Lian, Kshitij Jain, Jakub Truszkowski, Pascal Poupart, Yaoliang Yu
2019	J	`jnl`	Convergence Behaviour of Some Gradient-Based Methods on Bilinear Games. CoRR Guojun Zhang, Yaoliang Yu
2019	A*	`conf`	Distributional Reinforcement Learning for Efficient Exploration. ICML Borislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu
2019	J	`jnl`	Distributional Reinforcement Learning for Efficient Exploration. CoRR Borislav Mavrin, Shangtong Zhang, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu
2019	J	`jnl`	Exploiting Token and Path-based Representations of Code for Identifying Security-Relevant Commits. CoRR Achyudh Ram, Ji Xin, Meiyappan Nagappan, Yaoliang Yu, Rocío Cabrera Lozoya, Antonino Sabetta, Jimmy Lin
2019	A	`conf`	Least Squares Estimation of Weakly Convex Functions. AISTATS Sun Sun, Yaoliang Yu
2019	A*	`conf`	Multivariate Triangular Quantile Maps for Novelty Detection. NeurIPS Jingjing Wang, Sun Sun, Yaoliang Yu
2019	A*	`conf`	Sum-of-Squares Polynomial Flow. ICML Priyank Jaini, Kira A. Selby, Yaoliang Yu
2019	J	`jnl`	Sum-of-Squares Polynomial Flow. CoRR Priyank Jaini, Kira A. Selby, Yaoliang Yu
2019	J	`jnl`	Tails of Triangular Flows. CoRR Priyank Jaini, Ivan Kobyzev, Marcus A. Brubaker, Yaoliang Yu
2019	J	`jnl`	Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin. CoRR Kaiwen Wu, Yaoliang Yu
2019	—	`conf`	What Part of the Neural Network Does This? Understanding LSTMs by Measuring and Dissecting Neurons. EMNLP/IJCNLP (1) Ji Xin, Jimmy Lin, Yaoliang Yu
2018	A*	`conf`	Deep Homogeneous Mixture Models: Representation, Separation, and Approximation. NeurIPS Priyank Jaini, Pascal Poupart, Yaoliang Yu
2018	J	`jnl`	Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters. J. Mach. Learn. Res. Yi Zhou, Yingbin Liang, Yaoliang Yu, Wei Dai, Eric P. Xing
2018	A*	`conf`	Inductive Two-layer Modeling with Parametric Bregman Transfer. ICML Vignesh Ganapathiraman, Zhan Shi, Xinhua Zhang, Yaoliang Yu
2018	—	`conf`	Orpheus: Efficient Distributed Machine Learning via System and Algorithm Co-design. SoCC Pengtao Xie, Jin Kyu Kim, Qirong Ho, Yaoliang Yu, Eric P. Xing
2017	—	`conf`	Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction. NIPS Zhan Shi, Xinhua Zhang, Yaoliang Yu
2017	A	`conf`	Convex-constrained Sparse Additive Modeling and Its Extensions. UAI Junming Yin, Yaoliang Yu
2017	J	`jnl`	Convex-constrained Sparse Additive Modeling and Its Extensions. CoRR Junming Yin, Yaoliang Yu
2017	—	`conf`	Dropout with Expectation-linear Regularization. ICLR (Poster) Xuezhe Ma, Yingkai Gao, Zhiting Hu, Yaoliang Yu, Yuntian Deng, Eduard H. Hovy
2017	A*	`conf`	Efficient Multiple Instance Metric Learning Using Weakly Supervised Data. CVPR Marc T. Law, Yaoliang Yu, Raquel Urtasun, Richard S. Zemel, Eric P. Xing
2017	J	`jnl`	Generalized Conditional Gradient for Sparse Estimation. J. Mach. Learn. Res. Yaoliang Yu, Xinhua Zhang, Dale Schuurmans
2017	A*	`conf`	Learning Latent Space Models with Angular Constraints. ICML Pengtao Xie, Yuntian Deng, Yi Zhou, Abhimanu Kumar, Yaoliang Yu, James Zou, Eric P. Xing
2017	J	`jnl`	Provably noise-robust, regularised k-means clustering. CoRR Shrinu Kushagra, Nicole McNabb, Yaoliang Yu, Shai Ben-David
2017	A*	`conf`	Robust Top- KDD Xiaojun Chang, Yaoliang Yu, Yi Yang
2017	J	`jnl`	Semantic Pooling for Complex Event Analysis in Untrimmed Videos. IEEE Trans. Pattern Anal. Mach. Intell. Xiaojun Chang, Yaoliang Yu, Yi Yang, Eric P. Xing
2016	A*	`conf`	Additive Approximations in High Dimensional Nonparametric Regression via the SALSA. ICML Kirthevasan Kandasamy, Yaoliang Yu
2016	J	`jnl`	Additive Approximations in High Dimensional Nonparametric Regression via the SALSA. CoRR Kirthevasan Kandasamy, Yaoliang Yu
2016	A*	`conf`	Closed-Form Training of Mahalanobis Distance for Supervised Clustering. CVPR Marc T. Law, Yaoliang Yu, Matthieu Cord, Eric P. Xing
2016	—	`conf`	Convex Two-Layer Modeling with Latent Structure. NIPS Vignesh Ganapathiraman, Xinhua Zhang, Yaoliang Yu, Junfeng Wen
2016	J	`jnl`	Dropout with Expectation-linear Regularization. CoRR Xuezhe Ma, Yingkai Gao, Zhiting Hu, Yaoliang Yu, Yuntian Deng, Eduard H. Hovy
2016	A	`conf`	Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting. UAI Pengtao Xie, Jin Kyu Kim, Yi Zhou, Qirong Ho, Abhimanu Kumar, Yaoliang Yu, Eric P. Xing
2016	A	`conf`	On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System. AISTATS Yi Zhou, Yaoliang Yu, Wei Dai, Yingbin Liang, Eric P. Xing
2016	—	`ch.`	Online Learning and Optimization. Encyclopedia of Algorithms Yaoliang Yu
2016	A	`conf`	Scalable and Sound Low-Rank Tensor Learning. AISTATS Hao Cheng, Yaoliang Yu, Xinhua Zhang, Eric P. Xing, Dale Schuurmans
2016	A*	`conf`	They are Not Equally Reliable: Semantic Event Search Using Differentiated Concept Classifiers. CVPR Xiaojun Chang, Yaoliang Yu, Yi Yang, Eric P. Xing
2015	A*	`conf`	Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM. ICML Xiaojun Chang, Yi Yang, Eric P. Xing, Yaoliang Yu
2015	J	`jnl`	Distributed Machine Learning via Sufficient Factor Broadcasting. CoRR Pengtao Xie, Jin Kyu Kim, Yi Zhou, Qirong Ho, Abhimanu Kumar, Yaoliang Yu, Eric P. Xing
2015	J	`jnl`	Efficient Structured Matrix Rank Minimization. CoRR Adams Wei Yu, Wanli Ma, Yaoliang Yu, Jaime G. Carbonell, Suvrit Sra
2015	A*	`conf`	Linear Time Samplers for Supervised Topic Models using Compositional Proposals. KDD Xun Zheng, Yaoliang Yu, Eric P. Xing
2015	A	`conf`	Minimizing Nonconvex Non-Separable Functions. AISTATS Yaoliang Yu, Xun Zheng, Micol Marchetti-Bowick, Eric P. Xing
2015	A*	`conf`	Petuum: A New Platform for Distributed Machine Learning on Big Data. KDD Eric P. Xing, Qirong Ho, Wei Dai, Jin Kyu Kim, Jinliang Wei, Seunghak Lee, Xun Zheng, Pengtao Xie, Abhimanu Kumar, Yaoliang Yu
2015	J	`jnl`	Petuum: A New Platform for Distributed Machine Learning on Big Data. IEEE Trans. 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	A*	`conf`	Searching Persuasively: Joint Event Detection and Evidence Recounting with Limited Supervision. ACM Multimedia Xiaojun Chang, Yaoliang Yu, Yi Yang, Alexander G. Hauptmann
2015	A*	`conf`	Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection. IJCAI Xiaojun Chang, Yi Yang, Alexander G. Hauptmann, Eric P. Xing, Yaoliang Yu
2014	—	`conf`	Efficient Structured Matrix Rank Minimization. NIPS Adams Wei Yu, Wanli Ma, Yaoliang Yu, Jaime G. Carbonell, Suvrit Sra
2014	J	`jnl`	Generalized Conditional Gradient for Sparse Estimation. CoRR Yaoliang Yu, Xinhua Zhang, Dale Schuurmans
2013	—	`conf`	Better Approximation and Faster Algorithm Using the Proximal Average. NIPS Yaoliang Yu
2013	—	`conf`	Characterizing the Representer Theorem. ICML (1) Yaoliang Yu, Hao Cheng, Dale Schuurmans, Csaba Szepesvári
2013	—	`conf`	On Decomposing the Proximal Map. NIPS Yaoliang Yu
2013	—	`conf`	Polar Operators for Structured Sparse Estimation. NIPS Xinhua Zhang, Yaoliang Yu, Dale Schuurmans
2012	—	`conf`	A Polynomial-time Form of Robust Regression. NIPS Yaoliang Yu, Özlem Aslan, Dale Schuurmans
2012	—	`conf`	Accelerated Training for Matrix-norm Regularization: A Boosting Approach. NIPS Xinhua Zhang, Yaoliang Yu, Dale Schuurmans
2012	J	`jnl`	Analysis of Kernel Mean Matching under Covariate Shift CoRR Yaoliang Yu, Csaba Szepesvári
2012	A*	`conf`	Analysis of Kernel Mean Matching under Covariate Shift. ICML Yaoliang Yu, Csaba Szepesvári
2012	—	`conf`	Convex Multi-view Subspace Learning. NIPS Martha White, Yaoliang Yu, Xinhua Zhang, Dale Schuurmans
2012	J	`jnl`	Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering CoRR Yaoliang Yu, Dale Schuurmans
2012	A*	`conf`	Regularizers versus Losses for Nonlinear Dimensionality Reduction: A Factored View with New Convex Relaxations. ICML James Neufeld, Yaoliang Yu, Xinhua Zhang, Ryan Kiros, Dale Schuurmans
2011	A*	`conf`	Convex Sparse Coding, Subspace Learning, and Semi-Supervised Extensions. AAAI Xinhua Zhang, Yaoliang Yu, Martha White, Ruitong Huang, Dale Schuurmans
2011	J	`jnl`	Distance metric learning by minimal distance maximization. Pattern Recognit. Yaoliang Yu, Jiayan Jiang, Liming Zhang
2011	A	`conf`	Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering. UAI Yaoliang Yu, Dale Schuurmans
2010	—	`conf`	Relaxed Clipping: A Global Training Method for Robust Regression and Classification. NIPS Yaoliang Yu, Min Yang, Linli Xu, Martha White, Dale Schuurmans
2009	—	`conf`	A General Projection Property for Distribution Families. NIPS Yaoliang Yu, Yuxi Li, Dale Schuurmans, Csaba Szepesvári
2007	—	`conf`	A Novel Facial Feature Point Localization Method on 3D Faces. ICIP (3) Peng Guan, Yaoliang Yu, Liming Zhang
2007	—	`conf`	Discriminant Analysis with Label Constrained Graph Partition. ISNN (2) Peng Guan, Yaoliang Yu, Liming Zhang
2007	—	`conf`	Extensions of Manifold Learning Algorithms in Kernel Feature Space. ISNN (1) Yaoliang Yu, Peng Guan, Liming Zhang