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Si Si
Publications
- 2023
- [c28]Si Si, Felix X. Yu, Ankit Singh Rawat, Cho-Jui Hsieh, Sanjiv Kumar:
Serving Graph Compression for Graph Neural Networks. ICLR 2023 - [c27]Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh:
Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory. ICML 2023: 6565-6590 - [i22]Cho-Jui Hsieh, Si Si, Felix X. Yu, Inderjit S. Dhillon:
Automatic Engineering of Long Prompts. CoRR abs/2311.10117 (2023) - 2022
- [c26]Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh:
DC-BENCH: Dataset Condensation Benchmark. NeurIPS 2022 - [i21]Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh:
DC-BENCH: Dataset Condensation Benchmark. CoRR abs/2207.09639 (2022) - [i20]Yihan Wang, Si Si, Daliang Li, Michal Lukasik, Felix X. Yu, Cho-Jui Hsieh, Inderjit S. Dhillon, Sanjiv Kumar:
Preserving In-Context Learning ability in Large Language Model Fine-tuning. CoRR abs/2211.00635 (2022) - [i19]Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh:
Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory. CoRR abs/2211.10586 (2022) - 2021
- [c25]Yang Li, Si Si, Gang Li, Cho-Jui Hsieh, Samy Bengio:
Learnable Fourier Features for Multi-dimensional Spatial Positional Encoding. NeurIPS 2021: 15816-15829 - [i18]Yang Li, Si Si, Gang Li, Cho-Jui Hsieh, Samy Bengio:
Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding. CoRR abs/2106.02795 (2021) - 2020
- [c24]Xuanqing Liu, Tesi Xiao, Si Si, Qin Cao, Sanjiv Kumar, Cho-Jui Hsieh:
How Does Noise Help Robustness? Explanation and Exploration under the Neural SDE Framework. CVPR 2020: 279-287 - [c23]Hongge Chen, Si Si, Yang Li, Ciprian Chelba, Sanjiv Kumar, Duane S. Boning, Cho-Jui Hsieh:
Multi-Stage Influence Function. NeurIPS 2020 - [i16]Hongge Chen, Si Si, Yang Li, Ciprian Chelba, Sanjiv Kumar, Duane S. Boning, Cho-Jui Hsieh:
Multi-Stage Influence Function. CoRR abs/2007.09081 (2020) - [i15]Yuanhao Xiong, Xuanqing Liu, Li-Cheng Lan, Yang You, Si Si, Cho-Jui Hsieh:
How much progress have we made in neural network training? A New Evaluation Protocol for Benchmarking Optimizers. CoRR abs/2010.09889 (2020) - 2019
- [c21]Patrick H. Chen, Si Si, Sanjiv Kumar, Yang Li, Cho-Jui Hsieh:
Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks. ICLR (Poster) 2019 - [c19]Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh:
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. KDD 2019: 257-266 - [c18]Xuanqing Liu, Si Si, Jerry Zhu, Yang Li, Cho-Jui Hsieh:
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning. NeurIPS 2019: 9777-9787 - [c17]Hongge Chen, Huan Zhang, Si Si, Yang Li, Duane S. Boning, Cho-Jui Hsieh:
Robustness Verification of Tree-based Models. NeurIPS 2019: 12317-12328 - [i14]Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh:
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. CoRR abs/1905.07953 (2019) - [i13]Xuanqing Liu, Tesi Xiao, Si Si, Qin Cao, Sanjiv Kumar, Cho-Jui Hsieh:
Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise. CoRR abs/1906.02355 (2019) - [i12]Hongge Chen, Huan Zhang, Si Si, Yang Li, Duane S. Boning, Cho-Jui Hsieh:
Robustness Verification of Tree-based Models. CoRR abs/1906.03849 (2019) - [i11]Xuanqing Liu, Si Si, Xiaojin Zhu, Yang Li, Cho-Jui Hsieh:
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning. CoRR abs/1910.14147 (2019) - 2018
- [c16]Patrick H. Chen, Si Si, Yang Li, Ciprian Chelba, Cho-Jui Hsieh:
GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking. NeurIPS 2018: 11011-11021 - [i9]Patrick H. Chen, Si Si, Yang Li, Ciprian Chelba, Cho-Jui Hsieh:
GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking. CoRR abs/1806.06950 (2018) - [i7]Patrick H. Chen, Si Si, Sanjiv Kumar, Yang Li, Cho-Jui Hsieh:
Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks. CoRR abs/1810.12406 (2018) - 2017
- [j6]Si Si, Cho-Jui Hsieh, Inderjit S. Dhillon:
Memory Efficient Kernel Approximation. J. Mach. Learn. Res. 18: 20:1-20:32 (2017) - [c15]Si Si, Huan Zhang, S. Sathiya Keerthi, Dhruv Mahajan, Inderjit S. Dhillon, Cho-Jui Hsieh:
Gradient Boosted Decision Trees for High Dimensional Sparse Output. ICML 2017: 3182-3190 - [c14]Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon:
Communication-Efficient Distributed Block Minimization for Nonlinear Kernel Machines. KDD 2017: 245-254 - [i5]Huan Zhang, Si Si, Cho-Jui Hsieh:
GPU-acceleration for Large-scale Tree Boosting. CoRR abs/1706.08359 (2017) - 2016
- [c13]Si Si, Cho-Jui Hsieh, Inderjit S. Dhillon:
Computationally Efficient Nyström Approximation using Fast Transforms. ICML 2016: 2655-2663 - [c12]Si Si, Kai-Yang Chiang, Cho-Jui Hsieh, Nikhil Rao, Inderjit S. Dhillon:
Goal-Directed Inductive Matrix Completion. KDD 2016: 1165-1174 - [i3]Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon:
Communication-Efficient Parallel Block Minimization for Kernel Machines. CoRR abs/1608.02010 (2016) - 2014
- [j5]Hsiang-Fu Yu, Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon:
Parallel matrix factorization for recommender systems. Knowl. Inf. Syst. 41(3): 793-819 (2014) - [c11]Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon:
A Divide-and-Conquer Solver for Kernel Support Vector Machines. ICML 2014: 566-574 - [c10]Si Si, Cho-Jui Hsieh, Inderjit S. Dhillon:
Memory Efficient Kernel Approximation. ICML 2014: 701-709 - [c8]Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon:
Fast Prediction for Large-Scale Kernel Machines. NIPS 2014: 3689-3697 - 2013
- [i2]Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon:
A Divide-and-Conquer Solver for Kernel Support Vector Machines. CoRR abs/1311.0914 (2013) - 2012
- [c4]Hsiang-Fu Yu, Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon:
Scalable Coordinate Descent Approaches to Parallel Matrix Factorization for Recommender Systems. ICDM 2012: 765-774
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last updated on 2024-01-09 23:10 CET by the dblp team
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