Ian En-Hsu Yen
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
24
Venues
10
Active years
2013–2025
Best venue rank
A*
Where they publish
Papers
24 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | ICLR | MagicDec: Breaking the Latency-Throughput Tradeoff for Long Context Generation with Speculative Decoding. | Ranajoy Sadhukhan, Jian Chen, Zhuoming Chen, Vashisth Tiwari, Ruihang Lai, Jinyuan Shi, Ian En-Hsu Yen, Avner May, Tianqi Chen, Beidi Chen |
| 2022 | ACL | Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm. | 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 |
| 2022 | DSAA | Towards ℓ1 Regularization for Deep Neural Networks: Model Sparsity Versus Task Difficulty. | Ta-Chun Shen, Chun-Pai Yang, Ian En-Hsu Yen, Shou-De Lin |
| 2021 | NAACL | Rethinking Network Pruning - under the Pre-train and Fine-tune Paradigm. | Dongkuan Xu, Ian En-Hsu Yen, Jinxi Zhao, Zhibin Xiao |
| 2020 | ICLR | Minimizing FLOPs to Learn Efficient Sparse Representations. | Biswajit Paria, Chih-Kuan Yeh, Ian En-Hsu Yen, Ning Xu, Pradeep Ravikumar, Barnabs Pczos |
| 2019 | KDD | Efficient Global String Kernel with Random Features: Beyond Counting Substructures. | Lingfei Wu, Ian En-Hsu Yen, Siyu Huo, Liang Zhao, Kun Xu, Liang Ma, Shouling Ji, Charu C. Aggarwal |
| 2019 | KDD | Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding. | Lingfei Wu, Ian En-Hsu Yen, Zhen Zhang, Kun Xu, Liang Zhao, Xi Peng, Yinglong Xia, Charu C. Aggarwal |
| 2018 | AISTATS | Random Warping Series: A Random Features Method for Time-Series Embedding. | Lingfei Wu, Ian En-Hsu Yen, Jinfeng Yi, Fangli Xu, Qi Lei, Michael Witbrock |
| 2018 | EMNLP | Word Mover's Embedding: From Word2Vec to Document Embedding. | Lingfei Wu, Ian En-Hsu Yen, Kun Xu, Fangli Xu, Avinash Balakrishnan, Pin-Yu Chen, Pradeep Ravikumar, Michael J. Witbrock |
| 2018 | ICML | Loss Decomposition for Fast Learning in Large Output Spaces. | Ian En-Hsu Yen, Satyen Kale, Felix X. Yu, Daniel Niels Holtmann-Rice, Sanjiv Kumar, Pradeep Ravikumar |
| 2018 | KDD | Scalable Spectral Clustering Using Random Binning Features. | Lingfei Wu, Pin-Yu Chen, Ian En-Hsu Yen, Fangli Xu, Yinglong Xia, Charu C. Aggarwal |
| 2017 | AISTATS | Greedy Direction Method of Multiplier for MAP Inference of Large Output Domain. | Xiangru Huang, Ian En-Hsu Yen, Ruohan Zhang, Qixing Huang, Pradeep Ravikumar, Inderjit S. Dhillon |
| 2017 | AISTATS | Scalable Convex Multiple Sequence Alignment via Entropy-Regularized Dual Decomposition. | Jiong Zhang, Ian En-Hsu Yen, Pradeep Ravikumar, Inderjit S. Dhillon |
| 2017 | ICML | Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization. | Qi Lei, Ian En-Hsu Yen, Chao-Yuan Wu, Inderjit S. Dhillon, Pradeep Ravikumar |
| 2017 | ICML | Latent Feature Lasso. | Ian En-Hsu Yen, Wei-Cheng Lee, Sung-En Chang, Arun Sai Suggala, Shou-De Lin, Pradeep Ravikumar |
| 2017 | KDD | PPDsparse: A Parallel Primal-Dual Sparse Method for Extreme Classification. | Ian En-Hsu Yen, Xiangru Huang, Wei Dai, Pradeep Ravikumar, Inderjit S. Dhillon, Eric P. Xing |
| 2016 | AISTATS | Scalable Exemplar Clustering and Facility Location via Augmented Block Coordinate Descent with Column Generation. | Ian En-Hsu Yen, Dmitry Malioutov, Abhishek Kumar |
| 2016 | ICML | PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification. | Ian En-Hsu Yen, Xiangru Huang, Pradeep Ravikumar, Kai Zhong, Inderjit S. Dhillon |
| 2016 | ICML | A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery. | Ian En-Hsu Yen, Xin Lin, Jiong Zhang, Pradeep Ravikumar, Inderjit S. Dhillon |
| 2016 | KDD | Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability. | Lingfei Wu, Ian En-Hsu Yen, Jie Chen, Rui Yan |
| 2016 | UAI | Large-scale Submodular Greedy Exemplar Selection with Structured Similarity Matrices. | Dmitry Malioutov, Abhishek Kumar, Ian En-Hsu Yen |
| 2015 | ICML | A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models. | Ian En-Hsu Yen, Xin Lin, Kai Zhong, Pradeep Ravikumar, Inderjit S. Dhillon |
| 2015 | WWW | Tackling the Achilles Heel of Social Networks: Influence Propagation based Language Model Smoothing. | Rui Yan, Ian En-Hsu Yen, Cheng-Te Li, Shiqi Zhao, Xiaohua Hu |
| 2013 | KDD | Indexed block coordinate descent for large-scale linear classification with limited memory. | Ian En-Hsu Yen, Chun-Fu Chang, Ting-Wei Lin, Shan-Wei Lin, Shou-De Lin |