A Novel Facial Feature Point Localization Method on 3D Faces. - Peng Guan; Yaoliang Yu; Liming Zhang 001 (ICIP (3), 2007)

Discriminant Analysis with Label Constrained Graph Partition. - Peng Guan; Yaoliang Yu; Liming Zhang 001 (ISNN (2), 2007)

Extensions of Manifold Learning Algorithms in Kernel Feature Space. - Yaoliang Yu; Peng Guan; Liming Zhang 001 (ISNN (1), 2007)

A General Projection Property for Distribution Families. - Yaoliang Yu; Yuxi Li; Dale Schuurmans; Csaba Szepesvári (NIPS, 2009)

Relaxed Clipping: A Global Training Method for Robust Regression and Classification. - Yaoliang Yu; Min Yang; Linli Xu; Martha White; Dale Schuurmans (NIPS, 2010)

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

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

Distance metric learning by minimal distance maximization. - Yaoliang Yu; Jiayan Jiang; Liming Zhang 001 (Pattern Recognit., 2011)

Regularizers versus Losses for Nonlinear Dimensionality Reduction: A Factored View with New Convex Relaxations. - James Neufeld; Yaoliang Yu; Xinhua Zhang; Ryan Kiros; Dale Schuurmans (ICML, 2012)

Analysis of Kernel Mean Matching under Covariate Shift. - Yaoliang Yu; Csaba Szepesvári (ICML, 2012)

Convex Multi-view Subspace Learning. - Martha White; Yaoliang Yu; Xinhua Zhang; Dale Schuurmans (NIPS, 2012)

A Polynomial-time Form of Robust Regression. - Yaoliang Yu; Özlem Aslan; Dale Schuurmans (NIPS, 2012)

Accelerated Training for Matrix-norm Regularization: A Boosting Approach. - Xinhua Zhang; Yaoliang Yu; Dale Schuurmans (NIPS, 2012)

Characterizing the Representer Theorem. - Yaoliang Yu; Hao Cheng 002; Dale Schuurmans; Csaba Szepesvári (ICML (1), 2013)

On Decomposing the Proximal Map. - Yaoliang Yu (NIPS, 2013)

Better Approximation and Faster Algorithm Using the Proximal Average. - Yaoliang Yu (NIPS, 2013)

Polar Operators for Structured Sparse Estimation. - Xinhua Zhang; Yaoliang Yu; Dale Schuurmans (NIPS, 2013)

Efficient Structured Matrix Rank Minimization. - Adams Wei Yu; Wanli Ma; Yaoliang Yu; Jaime G. Carbonell; Suvrit Sra (NIPS, 2014)

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

Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM. - Xiaojun Chang; Yi Yang 001; Eric P. Xing; Yaoliang Yu (ICML, 2015)

Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection. - Xiaojun Chang; Yi Yang 001; Alexander G. Hauptmann; Eric P. Xing; Yaoliang Yu (IJCAI, 2015)

Petuum: A New Platform for Distributed Machine Learning on Big Data. - Eric P. Xing; Qirong Ho; Wei Dai 003; Jin Kyu Kim; Jinliang Wei; Seunghak Lee; Xun Zheng; Pengtao Xie; Abhimanu Kumar; Yaoliang Yu (KDD, 2015)

Linear Time Samplers for Supervised Topic Models using Compositional Proposals. - Xun Zheng; Yaoliang Yu; Eric P. Xing (KDD, 2015)

Searching Persuasively: Joint Event Detection and Evidence Recounting with Limited Supervision. - Xiaojun Chang; Yaoliang Yu; Yi Yang 001; Alexander G. Hauptmann (ACM Multimedia, 2015)

Distributed Machine Learning via Sufficient Factor Broadcasting. - Pengtao Xie; Jin Kyu Kim; Yi Zhou 017; Qirong Ho; Abhimanu Kumar; Yaoliang Yu; Eric P. Xing (CoRR, 2015)

Scalable and Sound Low-Rank Tensor Learning. - Hao Cheng 002; Yaoliang Yu; Xinhua Zhang; Eric P. Xing; Dale Schuurmans (AISTATS, 2016)

On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System. - Yi Zhou 017; Yaoliang Yu; Wei Dai 003; Yingbin Liang; Eric P. Xing (AISTATS, 2016)

They are Not Equally Reliable: Semantic Event Search Using Differentiated Concept Classifiers. - Xiaojun Chang; Yaoliang Yu; Yi Yang 001; Eric P. Xing (CVPR, 2016)

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

Additive Approximations in High Dimensional Nonparametric Regression via the SALSA. - Kirthevasan Kandasamy; Yaoliang Yu (ICML, 2016)

Convex Two-Layer Modeling with Latent Structure. - Vignesh Ganapathiraman; Xinhua Zhang; Yaoliang Yu; Junfeng Wen (NIPS, 2016)

Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting. - Pengtao Xie; Jin Kyu Kim; Yi Zhou 017; Qirong Ho; Abhimanu Kumar; Yaoliang Yu; Eric P. Xing (UAI, 2016)

Online Learning and Optimization. - Yaoliang Yu (Encyclopedia of Algorithms, 2016)

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

Dropout with Expectation-linear Regularization. - Xuezhe Ma; Yingkai Gao; Zhiting Hu; Yaoliang Yu; Yuntian Deng; Eduard H. Hovy (ICLR (Poster), 2017)

Learning Latent Space Models with Angular Constraints. - Pengtao Xie; Yuntian Deng; Yi Zhou 017; Abhimanu Kumar; Yaoliang Yu; James Zou 001; Eric P. Xing (ICML, 2017)

Robust Top-k Multiclass SVM for Visual Category Recognition. - Xiaojun Chang; Yaoliang Yu; Yi Yang 001 (KDD, 2017)

Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction. - Zhan Shi; Xinhua Zhang; Yaoliang Yu (NIPS, 2017)

Convex-constrained Sparse Additive Modeling and Its Extensions. - Junming Yin; Yaoliang Yu (UAI, 2017)

Provably noise-robust, regularised k-means clustering. - Shrinu Kushagra; Nicole McNabb; Yaoliang Yu; Shai Ben-David (CoRR, 2017)

Generalized Conditional Gradient for Sparse Estimation. - Yaoliang Yu; Xinhua Zhang; Dale Schuurmans (J. Mach. Learn. Res., 2017)

Semantic Pooling for Complex Event Analysis in Untrimmed Videos. - Xiaojun Chang; Yaoliang Yu; Yi Yang 001; Eric P. Xing (IEEE Trans. Pattern Anal. Mach. Intell., 2017)

Orpheus: Efficient Distributed Machine Learning via System and Algorithm Co-design. - Pengtao Xie; Jin Kyu Kim; Qirong Ho; Yaoliang Yu; Eric P. Xing (SoCC, 2018)

Inductive Two-layer Modeling with Parametric Bregman Transfer. - Vignesh Ganapathiraman; Zhan Shi; Xinhua Zhang; Yaoliang Yu (ICML, 2018)

Deep Homogeneous Mixture Models: Representation, Separation, and Approximation. - Priyank Jaini; Pascal Poupart; Yaoliang Yu (NeurIPS, 2018)

Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters. - Yi Zhou 017; Yingbin Liang; Yaoliang Yu; Wei Dai 003; Eric P. Xing (J. Mach. Learn. Res., 2018)

Least Squares Estimation of Weakly Convex Functions. - Sun Sun 002; Yaoliang Yu (AISTATS, 2019)

What Part of the Neural Network Does This? Understanding LSTMs by Measuring and Dissecting Neurons. - Ji Xin; Jimmy Lin; Yaoliang Yu (EMNLP/IJCNLP (1), 2019)

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

Distributional Reinforcement Learning for Efficient Exploration. - Borislav Mavrin; Hengshuai Yao; Linglong Kong; Kaiwen Wu; Yaoliang Yu (ICML, 2019)

Multivariate Triangular Quantile Maps for Novelty Detection. - Jingjing Wang; Sun Sun 002; Yaoliang Yu (NeurIPS, 2019)

Distributional Reinforcement Learning for Efficient Exploration. - Borislav Mavrin; Shangtong Zhang; Hengshuai Yao; Linglong Kong; Kaiwen Wu; Yaoliang Yu (CoRR, 2019)

Tails of Triangular Flows. - Priyank Jaini; Ivan Kobyzev; Marcus A. Brubaker; Yaoliang Yu (CoRR, 2019)

Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin. - Kaiwen Wu; Yaoliang Yu (CoRR, 2019)

Convergence Behaviour of Some Gradient-Based Methods on Bilinear Games. - Guojun Zhang; Yaoliang Yu (CoRR, 2019)

Exploiting Token and Path-based Representations of Code for Identifying Security-Relevant Commits. - Achyudh Ram; Ji Xin; Meiyappan Nagappan; Yaoliang Yu; Rocío Cabrera Lozoya; Antonino Sabetta; Jimmy Lin (CoRR, 2019)

Showing Your Work Doesn't Always Work. - Raphael Tang; Jaejun Lee; Ji Xin; Xinyu Liu; Yaoliang Yu; Jimmy Lin (ACL, 2020)

DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference. - Ji Xin; Raphael Tang; Jaejun Lee; Yaoliang Yu; Jimmy Lin (ACL, 2020)

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

Early Exiting BERT for Efficient Document Ranking. - Ji Xin; Rodrigo Nogueira 001; Yaoliang Yu; Jimmy Lin (SustaiNLP@EMNLP, 2020)

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

Tails of Lipschitz Triangular Flows. - Priyank Jaini; Ivan Kobyzev; Yaoliang Yu; Marcus A. Brubaker (ICML, 2020)

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

Stronger and Faster Wasserstein Adversarial Attacks. - Kaiwen Wu; Allen Houze Wang; Yaoliang Yu (ICML, 2020)

Unsupervised Multilingual Alignment using Wasserstein Barycenter. - Xin Lian; Kshitij Jain 001; Jakub Truszkowski; Pascal Poupart; Yaoliang Yu (IJCAI, 2020)

Problems and Opportunities in Training Deep Learning Software Systems: An Analysis of Variance. - Hung Viet Pham; Shangshu Qian; Jiannan Wang 002; Thibaud Lutellier; Jonathan Rosenthal; Lin Tan 001; Yaoliang Yu; Nachiappan Nagappan (ASE, 2020)

DeepAntigen: a novel method for neoantigen prioritization via 3D genome and deep sparse learning. - Yi Shi 007; Zehua Guo 004; Xianbin Su; Luming Meng; Mingxuan Zhang; Jing Sun; Chao Wu; Minhua Zheng; Xueyin Shang; Xin Zou; Wangqiu Cheng; Yaoliang Yu; et al. (Bioinform., 2020)

Optimality and Stability in Non-Convex-Non-Concave Min-Max Optimization. - Guojun Zhang; Pascal Poupart; Yaoliang Yu (CoRR, 2020)

Complete Hierarchy of Relaxation for Constrained Signomial Positivity. - Allen Houze Wang; Priyank Jaini; Yaoliang Yu; Pascal Poupart (CoRR, 2020)

Interpretable Contrastive Learning for Networks. - Takanori Fujiwara; Jian Zhao 010; Francine Chen 001; Yaoliang Yu; Kwan-Liu Ma (CoRR, 2020)

Density Deconvolution with Normalizing Flows. - Tim Dockhorn; James A. Ritchie; Yaoliang Yu; Iain Murray 001 (CoRR, 2020)

FedMGDA+: Federated Learning meets Multi-objective Optimization. - Zeou Hu; Kiarash Shaloudegi; Guojun Zhang; Yaoliang Yu (CoRR, 2020)

Newton-type Methods for Minimax Optimization. - Guojun Zhang; Kaiwen Wu; Pascal Poupart; Yaoliang Yu (CoRR, 2020)

OLALA: Object-Level Active Learning Based Layout Annotation. - Zejiang Shen 001; Jian Zhao 010; Melissa Dell; Yaoliang Yu; Weining Li (CoRR, 2020)

The Art of Abstention: Selective Prediction and Error Regularization for Natural Language Processing. - Ji Xin; Raphael Tang; Yaoliang Yu; Jimmy Lin (ACL/IJCNLP (1), 2021)

BERxiT: Early Exiting for BERT with Better Fine-Tuning and Extension to Regression. - Ji Xin; Raphael Tang; Yaoliang Yu; Jimmy Lin (EACL, 2021)

DEVIATE: A Deep Learning Variance Testing Framework. - Hung Viet Pham; Mijung Kim; Lin Tan 001; Yaoliang Yu; Nachiappan Nagappan (ASE, 2021)

Posterior Differential Regularization with f-divergence for Improving Model Robustness. - Hao Cheng 002; Xiaodong Liu 003; Lis Pereira; Yaoliang Yu; Jianfeng Gao 001 (NAACL-HLT, 2021)

Demystifying and Generalizing BinaryConnect. - Tim Dockhorn; Yaoliang Yu; Eyyüb Sari; Mahdi Zolnouri; Vahid Partovi Nia (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 (NeurIPS, 2021)

Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training. - Shangshu Qian; Hung Viet Pham; Thibaud Lutellier; Zeou Hu; Jungwon Kim; Lin Tan 001; Yaoliang Yu; Jiahao Chen 001; Sameena Shah (NeurIPS, 2021)

Quantifying and Improving Transferability in Domain Generalization. - Guojun Zhang; Han Zhao 002; Yaoliang Yu; Pascal Poupart (NeurIPS, 2021)

Splitting Algorithms for Federated Learning. - Saber Malekmohammadi; Kiarash Shaloudegi; Zeou Hu; Yaoliang Yu (PKDD/ECML Workshops (1), 2021)

S3: 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 (CoRR, 2021)

An Operator Splitting View of Federated Learning. - Saber Malekmohammadi; Kiarash Shaloudegi; Zeou Hu; Yaoliang Yu (CoRR, 2021)

Revisiting flow generative models for Out-of-distribution detection. - Dihong Jiang; Sun Sun 002; Yaoliang Yu (ICLR, 2022)

Equality Is Not Equity: Proportional Fairness in Federated Learning. - Guojun Zhang; Saber Malekmohammadi; Xi Chen; Yaoliang Yu (CoRR, 2022)

Towards Explanation for Unsupervised Graph-Level Representation Learning. - Qinghua Zheng; Jihong Wang 003; Minnan Luo; Yaoliang Yu; Jundong Li; Lina Yao 001; Xiaojun Chang (CoRR, 2022)

Mitigating Data Heterogeneity in Federated Learning with Data Augmentation. - Artur Back de Luca; Guojun Zhang; Xi Chen; Yaoliang Yu (CoRR, 2022)

Building an Efficiency Pipeline: Commutativity and Cumulativeness of Efficiency Operators for Transformers. - Ji Xin; Raphael Tang; Zhiying Jiang; Yaoliang Yu; Jimmy Lin (CoRR, 2022)

DP2-VAE: Differentially Private Pre-trained Variational Autoencoders. - Dihong Jiang; Guojun Zhang; Mahdi Karami; Xi Chen; Yunfeng Shao; Yaoliang Yu (CoRR, 2022)

Network Comparison with Interpretable Contrastive Network Representation Learning. - Takanori Fujiwara; Jian Zhao 010; Francine Chen 001; Yaoliang Yu; Kwan-Liu Ma (J. Data Sci. Stat. Vis., 2022)

Optimality and Stability in Non-Convex Smooth Games. - Guojun Zhang; Pascal Poupart; Yaoliang Yu (J. Mach. Learn. Res., 2022)

Indiscriminate Data Poisoning Attacks on Neural Networks. - Yiwei Lu 001; Gautam Kamath 001; Yaoliang Yu (Trans. Mach. Learn. Res., 2022)

Federated Learning Meets Multi-Objective Optimization. - Zeou Hu; Kiarash Shaloudegi; Guojun Zhang; Yaoliang Yu (IEEE Trans. Netw. Sci. Eng., 2022)

Operator Selection and Ordering in a Pipeline Approach to Efficiency Optimizations for Transformers. - Ji Xin; Raphael Tang; Zhiying Jiang; Yaoliang Yu; Jimmy Lin (ACL (Findings), 2023)

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

Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks. - Yiwei Lu 001; Gautam Kamath 001; Yaoliang Yu (ICML, 2023)

Batchnorm Allows Unsupervised Radial Attacks. - Amur Ghose; Apurv Gupta; Yaoliang Yu; Pascal Poupart (NeurIPS, 2023)

Functional Renyi Differential Privacy for Generative Modeling. - Dihong Jiang; Sun Sun 002; 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 001; Yaoliang Yu; Xinlin Li; Vahid Partovi Nia (NeurIPS, 2023)

Exploring the Limits of Indiscriminate Data Poisoning Attacks. - Yiwei Lu 001; Gautam Kamath 001; Yaoliang Yu (CoRR, 2023)

f-MICL: Understanding and Generalizing InfoNCE-based Contrastive Learning. - Yiwei Lu 001; Guojun Zhang; Sun Sun 002; Hongyu Guo; Yaoliang Yu (Trans. Mach. Learn. Res., 2023)

Proportional Fairness in Federated Learning. - Guojun Zhang; Saber Malekmohammadi; Xi Chen; Yaoliang Yu (Trans. Mach. Learn. Res., 2023)

Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games. - Jing Dong 008; Baoxiang Wang 001; Yaoliang Yu (AISTATS, 2024)

Faster Approximation of Probabilistic and Distributional Values via Least Squares. - Weida Li; Yaoliang Yu (ICLR, 2024)

Disguised Copyright Infringement of Latent Diffusion Models. - Yiwei Lu 001; Matthew Y. R. Yang; Zuoqiu Liu; Gautam Kamath 001; Yaoliang Yu (ICML, 2024)

Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning. - Saber Malekmohammadi; Yaoliang Yu; Yang Cao 011 (ICML, 2024)

One Sample Fits All: Approximating All Probabilistic Values Simultaneously and Efficiently. - Weida Li; Yaoliang Yu (NeurIPS, 2024)

Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors. - Yiwei Lu 001; Matthew Y. R. Yang; Gautam Kamath 001; Yaoliang Yu (SaTML, 2024)

Structure Preserving Diffusion Models. - Haoye Lu; Spencer Szabados; Yaoliang Yu (CoRR, 2024)

Alignment Calibration: Machine Unlearning for Contrastive Learning under Auditing. - Yihan Wang; Yiwei Lu 001; Guojun Zhang; Franziska Boenisch; Adam Dziedzic; Yaoliang Yu; Xiao-Shan Gao (CoRR, 2024)

Uncoupled and Convergent Learning in Monotone Games under Bandit Feedback. - Jing Dong 008; Baoxiang Wang 001; Yaoliang Yu (CoRR, 2024)

A Comprehensive Framework for Analyzing the Convergence of Adam: Bridging the Gap with SGD. - Ruinan Jin; Xiao Li; Yaoliang Yu; Baoxiang Wang 001 (CoRR, 2024)

Unlocking The Potential of Adaptive Attacks on Diffusion-Based Purification. - Andre Kassis; Urs Hengartner; Yaoliang Yu (CoRR, 2024)

BridgePure: Revealing the Fragility of Black-box Data Protection. - Yihan Wang; Yiwei Lu 001; Xiao-Shan Gao; Gautam Kamath 001; Yaoliang Yu (CoRR, 2024)

Last-iterate Convergence in Regularized Graphon Mean Field Game. - Jing Dong 008; Baoxiang Wang 001; Yaoliang Yu (AAAI, 2025)

Diffusion Models under Group Transformations. - Haoye Lu; Spencer Szabados; Yaoliang Yu (AISTATS, 2025)

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

MUC: Machine Unlearning for Contrastive Learning with Black-box Evaluation. - Yihan Wang; Yiwei Lu 001; Guojun Zhang; Franziska Boenisch; Adam Dziedzic; Yaoliang Yu; Xiao-Shan Gao (Trans. Mach. Learn. Res., 2025)

Stochastic Forward-Backward Deconvolution: Training Diffusion Models with Finite Noisy Datasets. - Haoye Lu; Qifan Wu; Yaoliang Yu (CoRR, 2025)

SFBD Flow: A Continuous-Optimization Framework for Training Diffusion Models with Noisy Samples. - Haoye Lu; Darren Lo; Yaoliang Yu (CoRR, 2025)

Demystifying Foreground-Background Memorization in Diffusion Models. - Jimmy Z. Di; Yiwei Lu 001; Yaoliang Yu; Gautam Kamath 001; Adam Dziedzic; Franziska Boenisch (CoRR, 2025)

Not All Samples Are Equal: Quantifying Instance-level Difficulty in Targeted Data Poisoning. - William Xu; Yiwei Lu 001; Yihan Wang; Matthew Y. R. Yang; Zuoqiu Liu; Gautam Kamath 001; Yaoliang Yu (CoRR, 2025)

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