Yaoliang Yu

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 103

Top Venues

CoRR

NeurIPS

ICML

AISTATS

Publications

Yiwei Lu, Yaoliang Yu, Xinlin Li, Vahid Partovi Nia
Understanding Neural Network Binarization with Forward and Backward Proximal Quantizers. CoRR (2024)
Jing Dong, Baoxiang Wang, Yaoliang Yu
Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games. AISTATS (2024)
Yiwei Lu, Guojun Zhang, Sun Sun, Hongyu Guo, Yaoliang Yu
f-MICL: Understanding and Generalizing InfoNCE-based Contrastive Learning. CoRR (2024)
Jing Dong, Baoxiang Wang, Yaoliang Yu
Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games. CoRR (2024)
Weida Li, Yaoliang Yu
Faster Approximation of Probabilistic and Distributional Values via Least Squares. ICLR (2024)
Yihan Wang, Yiwei Lu, Guojun Zhang, Franziska Boenisch, Adam Dziedzic, Yaoliang Yu, Xiao-Shan Gao
Alignment Calibration: Machine Unlearning for Contrastive Learning under Auditing. CoRR (2024)
Yiwei Lu, Matthew Y. R. Yang, Gautam Kamath, Yaoliang Yu
Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors. SaTML (2024)
Yiwei Lu, Matthew Y. R. Yang, Zuoqiu Liu, Gautam Kamath, Yaoliang Yu
Disguised Copyright Infringement of Latent Diffusion Models. CoRR (2024)
Haoye Lu, Spencer Szabados, Yaoliang Yu
Structure Preserving Diffusion Models. CoRR (2024)
Yiwei Lu, Matthew Y. R. Yang, Gautam Kamath, Yaoliang Yu
Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors. CoRR (2024)
Saber Malekmohammadi, Yaoliang Yu, Yang Cao
Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning. CoRR (2024)
Yiwei Lu, Guojun Zhang, Sun Sun, Hongyu Guo, Yaoliang Yu
f-MICL: Understanding and Generalizing InfoNCE-based Contrastive Learning. Trans. Mach. Learn. Res. 2023 (2023)
Yiwei Lu, Gautam Kamath, Yaoliang Yu
Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks. ICML (2023)
Guojun Zhang, Saber Malekmohammadi, Xi Chen, Yaoliang Yu
Proportional Fairness in Federated Learning. Trans. Mach. Learn. Res. 2023 (2023)
Ji Xin, Raphael Tang, Zhiying Jiang, Yaoliang Yu, Jimmy Lin
Operator Selection and Ordering in a Pipeline Approach to Efficiency Optimizations for Transformers. ACL (Findings) (2023)
Yiwei Lu, Yaoliang Yu, Xinlin Li, Vahid Partovi Nia
Understanding Neural Network Binarization with Forward and Backward Proximal Quantizers. NeurIPS (2023)
Dihong Jiang, Sun Sun, Yaoliang Yu
Functional Renyi Differential Privacy for Generative Modeling. NeurIPS (2023)
Yiwei Lu, Gautam Kamath, Yaoliang Yu
Exploring the Limits of Indiscriminate Data Poisoning Attacks. CoRR (2023)
Haoye Lu, Daniel Herman, Yaoliang Yu
Multi-Objective Reinforcement Learning: Convexity, Stationarity and Pareto Optimality. ICLR (2023)
Amur Ghose, Apurv Gupta, Yaoliang Yu, Pascal Poupart
Batchnorm Allows Unsupervised Radial Attacks. NeurIPS (2023)
Weida Li, Yaoliang Yu
Robust Data Valuation with Weighted Banzhaf Values. NeurIPS (2023)
Dihong Jiang, Sun Sun, Yaoliang Yu
Revisiting flow generative models for Out-of-distribution detection. ICLR (2022)
Yiwei Lu, Gautam Kamath, Yaoliang Yu
Indiscriminate Data Poisoning Attacks on Neural Networks. CoRR (2022)
Artur Back de Luca, Guojun Zhang, Xi Chen, Yaoliang Yu
Mitigating Data Heterogeneity in Federated Learning with Data Augmentation. CoRR (2022)
Dihong Jiang, Guojun Zhang, Mahdi Karami, Xi Chen, Yunfeng Shao, Yaoliang Yu
-VAE: Differentially Private Pre-trained Variational Autoencoders. CoRR (2022)
Takanori Fujiwara, Jian Zhao, Francine Chen, Yaoliang Yu, Kwan-Liu Ma
Network Comparison with Interpretable Contrastive Network Representation Learning. J. Data Sci. Stat. Vis. 2 (5) (2022)
Guojun Zhang, Pascal Poupart, Yaoliang Yu
Optimality and Stability in Non-Convex Smooth Games. J. Mach. Learn. Res. 23 (2022)
Yiwei Lu, Gautam Kamath, Yaoliang Yu
Indiscriminate Data Poisoning Attacks on Neural Networks. Trans. Mach. Learn. Res. 2022 (2022)
Zeou Hu, Kiarash Shaloudegi, Guojun Zhang, Yaoliang Yu
Federated Learning Meets Multi-Objective Optimization. IEEE Trans. Netw. Sci. Eng. 9 (4) (2022)
Ji Xin, Raphael Tang, Zhiying Jiang, Yaoliang Yu, Jimmy Lin
Building an Efficiency Pipeline: Commutativity and Cumulativeness of Efficiency Operators for Transformers. CoRR (2022)
Qinghua Zheng, Jihong Wang, Minnan Luo, Yaoliang Yu, Jundong Li, Lina Yao, Xiaojun Chang
Towards Explanation for Unsupervised Graph-Level Representation Learning. CoRR (2022)
Guojun Zhang, Saber Malekmohammadi, Xi Chen, Yaoliang Yu
Equality Is Not Equity: Proportional Fairness in Federated Learning. CoRR (2022)
Guojun Zhang, Han Zhao, Yaoliang Yu, Pascal Poupart
Quantifying and Improving Transferability in Domain Generalization. NeurIPS (2021)
Hung Viet Pham, Mijung Kim, Lin Tan, Yaoliang Yu, Nachiappan Nagappan
DEVIATE: A Deep Learning Variance Testing Framework. ASE (2021)
Tim Dockhorn, Yaoliang Yu, Eyyüb Sari, Mahdi Zolnouri, Vahid Partovi Nia
Demystifying and Generalizing BinaryConnect. CoRR (2021)
Xinlin Li, Bang Liu, Yaoliang Yu, Wulong Liu, Chunjing Xu, Vahid Partovi Nia
: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks. CoRR (2021)
Guojun Zhang, Han Zhao, Yaoliang Yu, Pascal Poupart
Quantifying and Improving Transferability in Domain Generalization. CoRR (2021)
Saber Malekmohammadi, Kiarash Shaloudegi, Zeou Hu, Yaoliang Yu
Splitting Algorithms for Federated Learning. PKDD/ECML Workshops (1) (2021)
Shangshu Qian, Hung Viet Pham, Thibaud Lutellier, Zeou Hu, Jungwon Kim, Lin Tan, Yaoliang Yu, Jiahao Chen, Sameena Shah
Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training. NeurIPS (2021)
Ji Xin, Raphael Tang, Yaoliang Yu, Jimmy Lin
The Art of Abstention: Selective Prediction and Error Regularization for Natural Language Processing. ACL/IJCNLP (1) (2021)
Saber Malekmohammadi, Kiarash Shaloudegi, Zeou Hu, Yaoliang Yu
An Operator Splitting View of Federated Learning. CoRR (2021)
Tim Dockhorn, Yaoliang Yu, Eyyüb Sari, Mahdi Zolnouri, Vahid Partovi Nia
Demystifying and Generalizing BinaryConnect. NeurIPS (2021)
Ji Xin, Raphael Tang, Yaoliang Yu, Jimmy Lin
BERxiT: Early Exiting for BERT with Better Fine-Tuning and Extension to Regression. EACL (2021)
Hao Cheng, Xiaodong Liu, Lis Pereira, Yaoliang Yu, Jianfeng Gao
Posterior Differential Regularization with f-divergence for Improving Model Robustness. NAACL-HLT (2021)
Xinlin Li, Bang Liu, Yaoliang Yu, Wulong Liu, Chunjing Xu, Vahid Partovi Nia
S$^3$: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks. NeurIPS (2021)
Hao Cheng, Xiaodong Liu, Lis Pereira, Yaoliang Yu, Jianfeng Gao
Posterior Differential Regularization with f-divergence for Improving Model Robustness. CoRR (2020)
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
DeepAntigen: a novel method for neoantigen prioritization via 3D genome and deep sparse learning. Bioinform. 36 (19) (2020)
Yingyi Ma, Vignesh Ganapathiraman, Yaoliang Yu, Xinhua Zhang
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space. ICML (2020)
Guojun Zhang, Yaoliang Yu
Convergence of Gradient Methods on Bilinear Zero-Sum Games. ICLR (2020)
Zeou Hu, Kiarash Shaloudegi, Guojun Zhang, Yaoliang Yu
FedMGDA+: Federated Learning meets Multi-objective Optimization. CoRR (2020)
Priyank Jaini, Ivan Kobyzev, Yaoliang Yu, Marcus Brubaker
Tails of Lipschitz Triangular Flows. ICML (2020)
Ji Xin, Raphael Tang, Jaejun Lee, Yaoliang Yu, Jimmy Lin
DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference. ACL (2020)
Raphael Tang, Jaejun Lee, Ji Xin, Xinyu Liu, Yaoliang Yu, Jimmy Lin
Showing Your Work Doesn't Always Work. ACL (2020)
Allen Houze Wang, Priyank Jaini, Yaoliang Yu, Pascal Poupart
Complete Hierarchy of Relaxation for Constrained Signomial Positivity. CoRR (2020)
Kaiwen Wu, Allen Houze Wang, Yaoliang Yu
Stronger and Faster Wasserstein Adversarial Attacks. CoRR (2020)
Tim Dockhorn, James A. Ritchie, Yaoliang Yu, Iain Murray
Density Deconvolution with Normalizing Flows. CoRR (2020)
Xin Lian, Kshitij Jain, Jakub Truszkowski, Pascal Poupart, Yaoliang Yu
Unsupervised Multilingual Alignment using Wasserstein Barycenter. CoRR (2020)
Guojun Zhang, Pascal Poupart, Yaoliang Yu
Optimality and Stability in Non-Convex-Non-Concave Min-Max Optimization. CoRR (2020)
Raphael Tang, Jaejun Lee, Ji Xin, Xinyu Liu, Yaoliang Yu, Jimmy Lin
Showing Your Work Doesn't Always Work. CoRR (2020)
Kaiwen Wu, Allen Houze Wang, Yaoliang Yu
Stronger and Faster Wasserstein Adversarial Attacks. ICML (2020)
Kaiwen Wu, Gavin Weiguang Ding, Ruitong Huang, Yaoliang Yu
On Minimax Optimality of GANs for Robust Mean Estimation. AISTATS (2020)
Guojun Zhang, Kaiwen Wu, Pascal Poupart, Yaoliang Yu
Newton-type Methods for Minimax Optimization. CoRR (2020)
Hung Viet Pham, Shangshu Qian, Jiannan Wang, Thibaud Lutellier, Jonathan Rosenthal, Lin Tan, Yaoliang Yu, Nachiappan Nagappan
Problems and Opportunities in Training Deep Learning Software Systems: An Analysis of Variance. ASE (2020)
Ji Xin, Rodrigo Nogueira, Yaoliang Yu, Jimmy Lin
Early Exiting BERT for Efficient Document Ranking. SustaiNLP@EMNLP (2020)
Xin Lian, Kshitij Jain, Jakub Truszkowski, Pascal Poupart, Yaoliang Yu
Unsupervised Multilingual Alignment using Wasserstein Barycenter. IJCAI (2020)
Zejiang Shen, Jian Zhao, Melissa Dell, Yaoliang Yu, Weining Li
OLALA: Object-Level Active Learning Based Layout Annotation. CoRR (2020)
Takanori Fujiwara, Jian Zhao, Francine Chen, Yaoliang Yu, Kwan-Liu Ma
Interpretable Contrastive Learning for Networks. CoRR (2020)
Yingyi Ma, Vignesh Ganapathiraman, Yaoliang Yu, Xinhua Zhang
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space. CoRR (2020)
Ji Xin, Raphael Tang, Jaejun Lee, Yaoliang Yu, Jimmy Lin
DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference. CoRR (2020)
Priyank Jaini, Ivan Kobyzev, Marcus Brubaker, Yaoliang Yu
Tails of Triangular Flows. CoRR (2019)
Priyank Jaini, Kira A. Selby, Yaoliang Yu
Sum-of-Squares Polynomial Flow. ICML (2019)
Achyudh Ram, Ji Xin, Meiyappan Nagappan, Yaoliang Yu, Rocío Cabrera Lozoya, Antonino Sabetta, Jimmy Lin
Exploiting Token and Path-based Representations of Code for Identifying Security-Relevant Commits. CoRR (2019)
Borislav Mavrin, Shangtong Zhang, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu
Distributional Reinforcement Learning for Efficient Exploration. CoRR (2019)
Jingjing Wang, Sun Sun, Yaoliang Yu
Multivariate Triangular Quantile Maps for Novelty Detection. NeurIPS (2019)
Ji Xin, Jimmy Lin, Yaoliang Yu
What Part of the Neural Network Does This? Understanding LSTMs by Measuring and Dissecting Neurons. EMNLP/IJCNLP (1) (2019)
Priyank Jaini, Kira A. Selby, Yaoliang Yu
Sum-of-Squares Polynomial Flow. CoRR (2019)
Borislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu
Distributional Reinforcement Learning for Efficient Exploration. ICML (2019)
Kaiwen Wu, Yaoliang Yu
Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin. CoRR (2019)
Sun Sun, Yaoliang Yu
Least Squares Estimation of Weakly Convex Functions. AISTATS (2019)
Guojun Zhang, Yaoliang Yu
Convergence Behaviour of Some Gradient-Based Methods on Bilinear Games. CoRR (2019)
Priyank Jaini, Pascal Poupart, Yaoliang Yu
Deep Homogeneous Mixture Models: Representation, Separation, and Approximation. NeurIPS (2018)
Pengtao Xie, Jin Kyu Kim, Qirong Ho, Yaoliang Yu, Eric P. Xing
Orpheus: Efficient Distributed Machine Learning via System and Algorithm Co-design. SoCC (2018)
Vignesh Ganapathiraman, Zhan Shi, Xinhua Zhang, Yaoliang Yu
Inductive Two-layer Modeling with Parametric Bregman Transfer. ICML (2018)
Yi Zhou, Yingbin Liang, Yaoliang Yu, Wei Dai, Eric P. Xing
Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters. J. Mach. Learn. Res. 19 (2018)
Zhan Shi, Xinhua Zhang, Yaoliang Yu
Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction. NIPS (2017)
Xuezhe Ma, Yingkai Gao, Zhiting Hu, Yaoliang Yu, Yuntian Deng, Eduard H. Hovy
Dropout with Expectation-linear Regularization. ICLR (Poster) (2017)
Marc T. Law, Yaoliang Yu, Raquel Urtasun, Richard S. Zemel, Eric P. Xing
Efficient Multiple Instance Metric Learning Using Weakly Supervised Data. CVPR (2017)
Junming Yin, Yaoliang Yu
Convex-constrained Sparse Additive Modeling and Its Extensions. UAI (2017)
Xiaojun Chang, Yaoliang Yu, Yi Yang
Multiclass SVM for Visual Category Recognition. KDD (2017)
Xiaojun Chang, Yaoliang Yu, Yi Yang, Eric P. Xing
Semantic Pooling for Complex Event Analysis in Untrimmed Videos. IEEE Trans. Pattern Anal. Mach. Intell. 39 (8) (2017)
Shrinu Kushagra, Nicole McNabb, Yaoliang Yu, Shai Ben-David
Provably noise-robust, regularised k-means clustering. CoRR (2017)
Yaoliang Yu, Xinhua Zhang, Dale Schuurmans
Generalized Conditional Gradient for Sparse Estimation. J. Mach. Learn. Res. 18 (2017)
Pengtao Xie, Yuntian Deng, Yi Zhou, Abhimanu Kumar, Yaoliang Yu, James Zou, Eric P. Xing
Learning Latent Space Models with Angular Constraints. ICML (2017)
Junming Yin, Yaoliang Yu
Convex-constrained Sparse Additive Modeling and Its Extensions. CoRR (2017)
Yi Zhou, Yaoliang Yu, Wei Dai, Yingbin Liang, Eric P. Xing
On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System. AISTATS (2016)
Hao Cheng, Yaoliang Yu, Xinhua Zhang, Eric P. Xing, Dale Schuurmans
Scalable and Sound Low-Rank Tensor Learning. AISTATS (2016)
Xiaojun Chang, Yaoliang Yu, Yi Yang, Eric P. Xing
They are Not Equally Reliable: Semantic Event Search Using Differentiated Concept Classifiers. CVPR (2016)
Kirthevasan Kandasamy, Yaoliang Yu
Additive Approximations in High Dimensional Nonparametric Regression via the SALSA. ICML (2016)
Marc T. Law, Yaoliang Yu, Matthieu Cord, Eric P. Xing
Closed-Form Training of Mahalanobis Distance for Supervised Clustering. CVPR (2016)
Kirthevasan Kandasamy, Yaoliang Yu
Additive Approximations in High Dimensional Nonparametric Regression via the SALSA. CoRR (2016)

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Publication Activity (10 Years)

Top Topics

Top Venues

Yaoliang Yu