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ORCIDIan En-Hsu Yen
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- affiliation: University of Texas at Austin, Department of Computer Science
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2020 – today
- 2025
[c33]Ranajoy Sadhukhan, Jian Chen, Zhuoming Chen, Vashisth Tiwari, Ruihang Lai, Jinyuan Shi, Ian En-Hsu Yen, Avner May, Tianqi Chen, Beidi Chen:
MagicDec: Breaking the Latency-Throughput Tradeoff for Long Context Generation with Speculative Decoding. ICLR 2025- 2024
- [i15]Jian Chen, Vashisth Tiwari, Ranajoy Sadhukhan, Zhuoming Chen, Jinyuan Shi, Ian En-Hsu Yen, Beidi Chen:
MagicDec: Breaking the Latency-Throughput Tradeoff for Long Context Generation with Speculative Decoding. CoRR abs/2408.11049 (2024) - 2023
- [i14]Jianwei Li, Tianchi Zhang, Ian En-Hsu Yen, Dongkuan Xu:
FP8-BERT: Post-Training Quantization for Transformer. CoRR abs/2312.05725 (2023) - 2022
- [c32]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: 190-200 - [c31]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: 1-9 - [i13]Ian En-Hsu Yen, Zhibin Xiao, Dongkuan Xu:
S4: a High-sparsity, High-performance AI Accelerator. CoRR abs/2207.08006 (2022) - 2021
- [c30]Dongkuan Xu, Ian En-Hsu Yen, Jinxi Zhao, Zhibin Xiao:
Rethinking Network Pruning - under the Pre-train and Fine-tune Paradigm. NAACL-HLT 2021: 2376-2382 - [i12]Dongkuan Xu, Ian En-Hsu Yen, Jinxi Zhao, Zhibin Xiao:
Rethinking Network Pruning - under the Pre-train and Fine-tune Paradigm. CoRR abs/2104.08682 (2021) - [i11]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 abs/2110.08190 (2021) - 2020
- [c29]Biswajit Paria, Chih-Kuan Yeh, Ian En-Hsu Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos:
Minimizing FLOPs to Learn Efficient Sparse Representations. ICLR 2020 - [i10]Biswajit Paria, Chih-Kuan Yeh, Ian En-Hsu Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos:
Minimizing FLOPs to Learn Efficient Sparse Representations. CoRR abs/2004.05665 (2020)
2010 – 2019
- 2019
- [j1]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): 247-273 (2019) - [c28]Tan Yu, Zhou Ren, Yuncheng Li, Enxu Yan, Ning Xu, Junsong Yuan:
Temporal Structure Mining for Weakly Supervised Action Detection. ICCV 2019: 5521-5530 - [c27]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: 520-528 - [c26]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: 1418-1428 - [i9]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 abs/1911.11119 (2019) - [i8]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 abs/1911.11121 (2019) - 2018
- [b1]Ian En-Hsu Yen:
Sublinear-Time Learning and Inference for High-Dimensional Models. Carnegie Mellon University, USA, 2018 - [c25]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: 793-802 - [c24]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: 4524-4534 - [c23]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: 5626-5635 - [c22]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: 2506-2515 - [c21]Chih-Kuan Yeh, Joon Sik Kim, Ian En-Hsu Yen, Pradeep Ravikumar:
Representer Point Selection for Explaining Deep Neural Networks. NeurIPS 2018: 9311-9321 - [c20]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: 10891-10899 - [i7]Lingfei Wu, Ian En-Hsu Yen, Fangli Xu, Pradeep Ravikumar, Michael Witbrock:
D2KE: From Distance to Kernel and Embedding. CoRR abs/1802.04956 (2018) - [i6]Lingfei Wu, Pin-Yu Chen, Ian En-Hsu Yen, Fangli Xu, Yinglong Xia, Charu C. Aggarwal:
Scalable Spectral Clustering Using Random Binning Features. CoRR abs/1805.11048 (2018) - [i5]Lingfei Wu, Ian En-Hsu Yen, Jie Chen, Rui Yan:
Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability. CoRR abs/1809.05247 (2018) - [i4]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 abs/1809.05259 (2018) - [i3]Sung-En Chang, Xun Zheng, Ian En-Hsu Yen, Pradeep Ravikumar, Rose Yu:
Learning Tensor Latent Features. CoRR abs/1810.04754 (2018) - [i2]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 abs/1811.01713 (2018) - [i1]Chih-Kuan Yeh, Joon Sik Kim, Ian En-Hsu Yen, Pradeep Ravikumar:
Representer Point Selection for Explaining Deep Neural Networks. CoRR abs/1811.09720 (2018) - 2017
- [c19]Jiong Zhang, Ian En-Hsu Yen, Pradeep Ravikumar, Inderjit S. Dhillon:
Scalable Convex Multiple Sequence Alignment via Entropy-Regularized Dual Decomposition. AISTATS 2017: 1514-1522 - [c18]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: 1550-1559 - [c17]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: 2034-2042 - [c16]Ian En-Hsu Yen, Wei-Cheng Lee, Sung-En Chang, Arun Sai Suggala, Shou-De Lin, Pradeep Ravikumar:
Latent Feature Lasso. ICML 2017: 3949-3957 - [c15]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: 545-553 - 2016
- [c14]Ian En-Hsu Yen, Dmitry Malioutov, Abhishek Kumar:
Scalable Exemplar Clustering and Facility Location via Augmented Block Coordinate Descent with Column Generation. AISTATS 2016: 1260-1269 - [c13]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: 2272-2280 - [c12]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: 3069-3077 - [c11]Lingfei Wu, Ian En-Hsu Yen, Jie Chen, Rui Yan:
Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability. KDD 2016: 1265-1274 - [c10]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: 5024-5032 - [c9]Dmitry Malioutov, Abhishek Kumar, Ian En-Hsu Yen:
Large-scale Submodular Greedy Exemplar Selection with Structured Similarity Matrices. UAI 2016 - 2015
- [c8]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: 2418-2426 - [c7]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: 2368-2376 - [c6]Ian En-Hsu Yen, Shan-Wei Lin, Shou-De Lin:
A Dual Augmented Block Minimization Framework for Learning with Limited Memory. NIPS 2015: 3582-3590 - [c5]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: 1318-1328 - 2014
- [c4]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: 1008-1016 - [c3]Kai Zhong, Ian En-Hsu Yen, Inderjit S. Dhillon, Pradeep Ravikumar:
Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators. NIPS 2014: 2375-2383 - [c2]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: 2456-2464 - 2013
- [c1]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: 248-256
Coauthor Index
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