Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDIan En-Hsu Yen
Person information
- affiliation: University of Texas at Austin, Department of Computer Science
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2020 – today
- 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
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-07-26 18:42 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
