Ian En-Hsu Yen

Publication Activity (10 Years)

Years Active: 2013-2023
Publications (10 Years): 38

Top Topics

Sparse Representation

Multiple Sequence Alignment

Dirichlet Process Mixture Models

Top Venues

Publications

Jianwei Li, Tianchi Zhang, Ian En-Hsu Yen, Dongkuan Xu
FP8-BERT: Post-Training Quantization for Transformer. CoRR (2023)
Ian En-Hsu Yen, Zhibin Xiao, Dongkuan Xu
S4: a High-sparsity, High-performance AI Accelerator. CoRR (2022)
Ta-Chun Shen, Chun-Pai Yang, Ian En-Hsu Yen, Shou-De Lin
Towards ℓ1 Regularization for Deep Neural Networks: Model Sparsity Versus Task Difficulty. DSAA (2022)
Shaoyi Huang, Dongkuan Xu, Ian En-Hsu Yen, Yijue Wang, Sung-En Chang, Bingbing Li, Shiyang Chen, Mimi Xie, Sanguthevar Rajasekaran, Hang Liu, Caiwen Ding
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm. ACL (1) (2022)
Dongkuan Xu, Ian En-Hsu Yen, Jinxi Zhao, Zhibin Xiao
Rethinking Network Pruning - under the Pre-train and Fine-tune Paradigm. NAACL-HLT (2021)
Dongkuan Xu, Ian En-Hsu Yen, Jinxi Zhao, Zhibin Xiao
Rethinking Network Pruning - under the Pre-train and Fine-tune Paradigm. CoRR (2021)
Shaoyi Huang, Dongkuan Xu, Ian En-Hsu Yen, Sung-en Chang, Bingbing Li, Shiyang Chen, Mimi Xie, Hang Liu, Caiwen Ding
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm. CoRR (2021)
Biswajit Paria, Chih-Kuan Yeh, Ian En-Hsu Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos
Minimizing FLOPs to Learn Efficient Sparse Representations. ICLR (2020)
Biswajit Paria, Chih-Kuan Yeh, Ian En-Hsu Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos
Minimizing FLOPs to Learn Efficient Sparse Representations. CoRR (2020)
Lingfei Wu, Ian En-Hsu Yen, Siyu Huo, Liang Zhao, Kun Xu, Liang Ma, Shouling Ji, Charu C. Aggarwal
Efficient Global String Kernel with Random Features: Beyond Counting Substructures. CoRR (2019)
Lingfei Wu, Ian En-Hsu Yen, Zhen Zhang, Kun Xu, Liang Zhao, Xi Peng, Yinglong Xia, Charu C. Aggarwal
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding. CoRR (2019)
Lingfei Wu, Ian En-Hsu Yen, Zhen Zhang, Kun Xu, Liang Zhao, Xi Peng, Yinglong Xia, Charu C. Aggarwal
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding. KDD (2019)
Lingfei Wu, Ian En-Hsu Yen, Siyu Huo, Liang Zhao, Kun Xu, Liang Ma, Shouling Ji, Charu C. Aggarwal
Efficient Global String Kernel with Random Features: Beyond Counting Substructures. KDD (2019)
Hsun-Ping Hsieh, Fandel Lin, Cheng-Te Li, Ian En-Hsu Yen, Hsin-Yu Chen
Temporal popularity prediction of locations for geographical placement of retail stores. Knowl. Inf. Syst. 60 (1) (2019)
Lingfei Wu, Ian En-Hsu Yen, Jie Chen, Rui Yan
Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability. CoRR (2018)
Chih-Kuan Yeh, Joon Sik Kim, Ian En-Hsu Yen, Pradeep Ravikumar
Representer Point Selection for Explaining Deep Neural Networks. CoRR (2018)
Lingfei Wu, Pin-Yu Chen, Ian En-Hsu Yen, Fangli Xu, Yinglong Xia, Charu C. Aggarwal
Scalable Spectral Clustering Using Random Binning Features. CoRR (2018)
Sung-En Chang, Xun Zheng, Ian En-Hsu Yen, Pradeep Ravikumar, Rose Yu
Learning Tensor Latent Features. CoRR (2018)
Lingfei Wu, Ian En-Hsu Yen, Jinfeng Yi, Fangli Xu, Qi Lei, Michael Witbrock
Random Warping Series: A Random Features Method for Time-Series Embedding. AISTATS (2018)
Ian En-Hsu Yen, Wei-Cheng Lee, Kai Zhong, Sung-En Chang, Pradeep Ravikumar, Shou-De Lin
MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization. NeurIPS (2018)
Ian En-Hsu Yen, Satyen Kale, Felix X. Yu, Daniel Niels Holtmann-Rice, Sanjiv Kumar, Pradeep Ravikumar
Loss Decomposition for Fast Learning in Large Output Spaces. ICML (2018)
Lingfei Wu, Ian En-Hsu Yen, Fangli Xu, Pradeep Ravikumar, Michael Witbrock
D2KE: From Distance to Kernel and Embedding. CoRR (2018)
Lingfei Wu, Ian En-Hsu Yen, Kun Xu, Fangli Xu, Avinash Balakrishnan, Pin-Yu Chen, Pradeep Ravikumar, Michael J. Witbrock
Word Mover's Embedding: From Word2Vec to Document Embedding. CoRR (2018)
Lingfei Wu, Ian En-Hsu Yen, Kun Xu, Fangli Xu, Avinash Balakrishnan, Pin-Yu Chen, Pradeep Ravikumar, Michael J. Witbrock
Word Mover's Embedding: From Word2Vec to Document Embedding. EMNLP (2018)
Chih-Kuan Yeh, Joon Sik Kim, Ian En-Hsu Yen, Pradeep Ravikumar
Representer Point Selection for Explaining Deep Neural Networks. NeurIPS (2018)
Lingfei Wu, Ian En-Hsu Yen, Jinfeng Yi, Fangli Xu, Qi Lei, Michael Witbrock
Random Warping Series: A Random Features Method for Time-Series Embedding. CoRR (2018)
Lingfei Wu, Pin-Yu Chen, Ian En-Hsu Yen, Fangli Xu, Yinglong Xia, Charu C. Aggarwal
Scalable Spectral Clustering Using Random Binning Features. KDD (2018)
Qi Lei, Ian En-Hsu Yen, Chao-Yuan Wu, Inderjit S. Dhillon, Pradeep Ravikumar
Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization. ICML (2017)
Ian En-Hsu Yen, Wei-Cheng Lee, Sung-En Chang, Arun Sai Suggala, Shou-De Lin, Pradeep Ravikumar
Latent Feature Lasso. ICML (2017)
Jiong Zhang, Ian En-Hsu Yen, Pradeep Ravikumar, Inderjit S. Dhillon
Scalable Convex Multiple Sequence Alignment via Entropy-Regularized Dual Decomposition. AISTATS (2017)
Xiangru Huang, Ian En-Hsu Yen, Ruohan Zhang, Qixing Huang, Pradeep Ravikumar, Inderjit S. Dhillon
Greedy Direction Method of Multiplier for MAP Inference of Large Output Domain. AISTATS (2017)
Ian En-Hsu Yen, Xiangru Huang, Wei Dai, Pradeep Ravikumar, Inderjit S. Dhillon, Eric P. Xing
PPDsparse: A Parallel Primal-Dual Sparse Method for Extreme Classification. KDD (2017)
Ian En-Hsu Yen, Xiangru Huang, Pradeep Ravikumar, Kai Zhong, Inderjit S. Dhillon
PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification. ICML (2016)
Ian En-Hsu Yen, Xin Lin, Jiong Zhang, Pradeep Ravikumar, Inderjit S. Dhillon
A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery. ICML (2016)
Ian En-Hsu Yen, Xiangru Huang, Kai Zhong, Ruohan Zhang, Pradeep Ravikumar, Inderjit S. Dhillon
Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain. NIPS (2016)
Lingfei Wu, Ian En-Hsu Yen, Jie Chen, Rui Yan
Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability. KDD (2016)
Dmitry Malioutov, Abhishek Kumar, Ian En-Hsu Yen
Large-scale Submodular Greedy Exemplar Selection with Structured Similarity Matrices. UAI (2016)
Ian En-Hsu Yen, Dmitry Malioutov, Abhishek Kumar
Scalable Exemplar Clustering and Facility Location via Augmented Block Coordinate Descent with Column Generation. AISTATS (2016)
Rui Yan, Ian En-Hsu Yen, Cheng-Te Li, Shiqi Zhao, Xiaohua Hu
Tackling the Achilles Heel of Social Networks: Influence Propagation based Language Model Smoothing. WWW (2015)
Ian En-Hsu Yen, Shan-Wei Lin, Shou-De Lin
A Dual Augmented Block Minimization Framework for Learning with Limited Memory. NIPS (2015)
Ian En-Hsu Yen, Kai Zhong, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon
Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent. NIPS (2015)
Ian En-Hsu Yen, Xin Lin, Kai Zhong, Pradeep Ravikumar, Inderjit S. Dhillon
A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models. ICML (2015)
Kai Zhong, Ian En-Hsu Yen, Inderjit S. Dhillon, Pradeep Ravikumar
Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators. NIPS (2014)
Ian En-Hsu Yen, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon
Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings. NIPS (2014)
Ian En-Hsu Yen, Ting-Wei Lin, Shou-De Lin, Pradeep Ravikumar, Inderjit S. Dhillon
Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space. NIPS (2014)
Ian En-Hsu Yen, Chun-Fu Chang, Ting-Wei Lin, Shan-Wei Lin, Shou-De Lin
Indexed block coordinate descent for large-scale linear classification with limited memory. KDD (2013)