Ian En-Hsu Yen

According to our database, Ian En-Hsu Yen authored at least 39 papers between 2013 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

FP8-BERT: Post-Training Quantization for Transformer.

Jianwei Li, Tianchi Zhang, Ian En-Hsu Yen, Dongkuan Xu

CoRR, 2023

2022

S4: a High-sparsity, High-performance AI Accelerator.

Ian En-Hsu Yen, Zhibin Xiao, Dongkuan Xu

CoRR, 2022

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

Proceedings of the 9th IEEE International Conference on Data Science and Advanced Analytics, 2022

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

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

2021

Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm.

Shaoyi Huang, Dongkuan Xu, Ian En-Hsu Yen, Sung-En Chang, Bingbing Li, Shiyang Chen, Mimi Xie, Hang Liu, Caiwen Ding

CoRR, 2021

Rethinking Network Pruning - under the Pre-train and Fine-tune Paradigm.

Dongkuan Xu, Ian En-Hsu Yen, Jinxi Zhao, Zhibin Xiao

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

2020

Minimizing FLOPs to Learn Efficient Sparse Representations.

Biswajit Paria, Chih-Kuan Yeh, Ian En-Hsu Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Temporal popularity prediction of locations for geographical placement of retail stores.

Hsun-Ping Hsieh, Fandel Lin, Cheng-Te Li, Ian En-Hsu Yen, Hsin-Yu Chen

Knowl. Inf. Syst., 2019

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

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

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

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Temporal Structure Mining for Weakly Supervised Action Detection.

Tan Yu, Zhou Ren, Yuncheng Li, Enxu Yan, Ning Xu, Junsong Yuan

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

2018

Sublinear-Time Learning and Inference for High-Dimensional Models.

Ian En-Hsu Yen

PhD thesis, 2018

Learning Tensor Latent Features.

Sung-En Chang, Xun Zheng, Ian En-Hsu Yen, Pradeep Ravikumar, Rose Yu

CoRR, 2018

D2KE: From Distance to Kernel and Embedding.

Lingfei Wu, Ian En-Hsu Yen, Fangli Xu, Pradeep Ravikumar, Michael Witbrock

CoRR, 2018

MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization.

Ian En-Hsu Yen, Wei-Cheng Lee, Kai Zhong, Sung-En Chang, Pradeep Ravikumar, Shou-De Lin

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Representer Point Selection for Explaining Deep Neural Networks.

Chih-Kuan Yeh, Joon Sik Kim, Ian En-Hsu Yen, Pradeep Ravikumar

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Scalable Spectral Clustering Using Random Binning Features.

Lingfei Wu, Pin-Yu Chen, Ian En-Hsu Yen, Fangli Xu, Yinglong Xia, Charu C. Aggarwal

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

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

Proceedings of the 35th International Conference on Machine Learning, 2018

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

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31, 2018

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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

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

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017

Latent Feature Lasso.

Ian En-Hsu Yen, Wei-Cheng Lee, Sung-En Chang, Arun Sai Suggala, Shou-De Lin, Pradeep Ravikumar

Proceedings of the 34th International Conference on Machine Learning, 2017

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

Proceedings of the 34th International Conference on Machine Learning, 2017

Scalable Convex Multiple Sequence Alignment via Entropy-Regularized Dual Decomposition.

Jiong Zhang, Ian En-Hsu Yen, Pradeep Ravikumar, Inderjit S. Dhillon

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

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

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Large-scale Submodular Greedy Exemplar Selection with Structured Similarity Matrices.

Dmitry Malioutov, Abhishek Kumar, Ian En-Hsu Yen

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain.

Ian En-Hsu Yen, Xiangru Huang, Kai Zhong, Ruohan Zhang, Pradeep Ravikumar, Inderjit S. Dhillon

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability.

Lingfei Wu, Ian En-Hsu Yen, Jie Chen, Rui Yan

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

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

Proceedings of the 33nd International Conference on Machine Learning, 2016

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

Proceedings of the 33nd International Conference on Machine Learning, 2016

Scalable Exemplar Clustering and Facility Location via Augmented Block Coordinate Descent with Column Generation.

Ian En-Hsu Yen, Dmitry Malioutov, Abhishek Kumar

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

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

Proceedings of the 24th International Conference on World Wide Web, 2015

Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent.

Ian En-Hsu Yen, Kai Zhong, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

A Dual Augmented Block Minimization Framework for Learning with Limited Memory.

Ian En-Hsu Yen, Shan-Wei Lin, Shou-De Lin

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

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

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators.

Kai Zhong, Ian En-Hsu Yen, Inderjit S. Dhillon, Pradeep Ravikumar

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space.

Ian En-Hsu Yen, Ting-Wei Lin, Shou-De Lin, Pradeep Ravikumar, Inderjit S. Dhillon

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings.

Ian En-Hsu Yen, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013

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

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013