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Ian En-Hsu Yen
Ian En-Hsu Yen
PhD, Machine Learning Department, Carnegie Mellon University
Verified email at cs.cmu.edu - Homepage
Title
Cited by
Cited by
Year
PD-Sparse: A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification
IEH Yen, X Huang, K Zhong, P Ravikumar, IS Dhillon
International Conference on Machine Learning, 2016
1962016
Representer point selection for explaining deep neural networks
CK Yeh, J Kim, IEH Yen, PK Ravikumar
Advances in neural information processing systems 31, 2018
1932018
Ppdsparse: A parallel primal-dual sparse method for extreme classification
IEH Yen, X Huang, W Dai, P Ravikumar, I Dhillon, E Xing
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge …, 2017
1372017
Word mover's embedding: From word2vec to document embedding
L Wu, IEH Yen, K Xu, F Xu, A Balakrishnan, PY Chen, P Ravikumar, ...
arXiv preprint arXiv:1811.01713, 2018
1202018
Sparse Random Features Algorithm as Coordinate Descent in Hilbert Space
IEH Yen, TW Lin, SD Lin, P Ravikumar, IS Dhillon
Advances in Neural Information Processing Systems (NIPS), 2014
642014
Random warping series: A random features method for time-series embedding
L Wu, IEH Yen, J Yi, F Xu, Q Lei, M Witbrock
International Conference on Artificial Intelligence and Statistics, 793-802, 2018
492018
Scalable spectral clustering using random binning features
L Wu, PY Chen, IEH Yen, F Xu, Y Xia, C Aggarwal
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018
372018
Revisiting Random Binning Feature: Fast Convergence and Strong Parallelizability
L Wu, IEH Yen, J Chen, R Yan
ACM SIGKDD international conference on Knowledge discovery and data mining., 2016
372016
Sparse linear programming via primal and dual augmented coordinate descent
IEH Yen, K Zhong, CJ Hsieh, PK Ravikumar, IS Dhillon
Advances in Neural Information Processing Systems 28, 2015
362015
Scalable global alignment graph kernel using random features: From node embedding to graph embedding
L Wu, IEH Yen, Z Zhang, K Xu, L Zhao, X Peng, Y Xia, C Aggarwal
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019
332019
On convergence rate of concave-convex procedure
IEH Yen, N Peng, PW Wang, SD Lin
proceedings of the NIPS 2012 optimization workshop, 31-35, 2012
332012
Minimizing flops to learn efficient sparse representations
B Paria, CK Yeh, IEH Yen, N Xu, P Ravikumar, B Póczos
arXiv preprint arXiv:2004.05665, 2020
302020
D2ke: From distance to kernel and embedding
L Wu, IEH Yen, F Xu, P Ravikumar, M Witbrock
arXiv preprint arXiv:1802.04956, 2018
292018
Optimal tests of treatment effects for the overall population and two subpopulations in randomized trials, using sparse linear programming
M Rosenblum, H Liu, EH Yen
Journal of the American Statistical Association 109 (507), 1216-1228, 2014
292014
Proximal quasi-newton for computationally intensive l1-regularized m-estimators
K Zhong, IEH Yen, IS Dhillon, PK Ravikumar
Advances in Neural Information Processing Systems 27, 2014
282014
Rethinking Network Pruning--under the Pre-train and Fine-tune Paradigm
D Xu, IEH Yen, J Zhao, Z Xiao
arXiv preprint arXiv:2104.08682, 2021
232021
Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings
IEH Yen, CJ Hsieh, P Ravikumar, I Dhillon
Advances in Neural Information Processing Systems, 2014
212014
Loss decomposition for fast learning in large output spaces
IEH Yen, S Kale, F Yu, D Holtmann-Rice, S Kumar, P Ravikumar
International Conference on Machine Learning, 5640-5649, 2018
192018
Doubly greedy primal-dual coordinate descent for sparse empirical risk minimization
Q Lei, IEH Yen, C Wu, IS Dhillon, P Ravikumar
International Conference on Machine Learning, 2034-2042, 2017
162017
Sparse progressive distillation: Resolving overfitting under pretrain-and-finetune paradigm
S Huang, D Xu, IEH Yen, Y Wang, SE Chang, B Li, S Chen, M Xie, ...
arXiv preprint arXiv:2110.08190, 2021
142021
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