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Hsiang-Fu Yu
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Publications
- 2024
- [c54]Wei-Cheng Chang, Jyun-Yu Jiang, Jiong Zhang, Mutasem Al-Darabsah, Choon Hui Teo, Cho-Jui Hsieh, Hsiang-Fu Yu, S. V. N. Vishwanathan:
PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models. WSDM 2024: 77-86 - 2023
- [c53]Jiong Zhang, Yau-Shian Wang, Wei-Cheng Chang, Wei Li, Jyun-Yu Jiang, Cho-Jui Hsieh, Hsiang-Fu Yu:
Build Faster with Less: A Journey to Accelerate Sparse Model Building for Semantic Matching in Product Search. CIKM 2023: 4960-4966 - [c52]Eli Chien, Jiong Zhang, Cho-Jui Hsieh, Jyun-Yu Jiang, Wei-Cheng Chang, Olgica Milenkovic, Hsiang-Fu Yu:
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation. ICML 2023: 5616-5630 - [c51]Che-Ping Tsai, Jiong Zhang, Hsiang-Fu Yu, Eli Chien, Cho-Jui Hsieh, Pradeep Kumar Ravikumar:
Representer Point Selection for Explaining Regularized High-dimensional Models. ICML 2023: 34469-34490 - [c50]Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh, Hsiang-Fu Yu:
Uncertainty Quantification for Extreme Classification. SIGIR 2023: 1649-1659 - [c49]Patrick H. Chen, Wei-Cheng Chang, Jyun-Yu Jiang, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh:
FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search. WWW 2023: 3225-3235 - [i30]Eli Chien, Jiong Zhang, Cho-Jui Hsieh, Jyun-Yu Jiang, Wei-Cheng Chang, Olgica Milenkovic, Hsiang-Fu Yu:
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation. CoRR abs/2305.12349 (2023) - [i29]Che-Ping Tsai, Jiong Zhang, Eli Chien, Hsiang-Fu Yu, Cho-Jui Hsieh, Pradeep Ravikumar:
Representer Point Selection for Explaining Regularized High-dimensional Models. CoRR abs/2305.20002 (2023) - [i28]Xiusi Chen, Jyun-Yu Jiang, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Wei Wang:
MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering. CoRR abs/2310.05007 (2023) - [i27]Wei-Cheng Chang, Jyun-Yu Jiang, Jiong Zhang, Mutasem Al-Darabsah, Choon Hui Teo, Cho-Jui Hsieh, Hsiang-Fu Yu, S. V. N. Vishwanathan:
PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models. CoRR abs/2312.02429 (2023) - 2022
- [c48]Eli Chien, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Jiong Zhang, Olgica Milenkovic, Inderjit S. Dhillon:
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction. ICLR 2022 - [c47]Hsiang-Fu Yu, Jiong Zhang, Wei-Cheng Chang, Jyun-Yu Jiang, Wei Li, Cho-Jui Hsieh:
PECOS: Prediction for Enormous and Correlated Output Spaces. KDD 2022: 4848-4849 - [c46]Yuanhao Xiong, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit S. Dhillon:
Extreme Zero-Shot Learning for Extreme Text Classification. NAACL-HLT 2022: 5455-5468 - [c45]Nilesh Gupta, Patrick H. Chen, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon:
ELIAS: End-to-End Learning to Index and Search in Large Output Spaces. NeurIPS 2022 - [c44]Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh, Hsiang-Fu Yu:
Relevance under the Iceberg: Reasonable Prediction for Extreme Multi-label Classification. SIGIR 2022: 1870-1874 - [i25]Patrick H. Chen, Wei-Cheng Chang, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh:
FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search. CoRR abs/2206.11408 (2022) - [i24]Nilesh Gupta, Patrick H. Chen, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon:
End-to-End Learning to Index and Search in Large Output Spaces. CoRR abs/2210.08410 (2022) - [i23]Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh, Hsiang-Fu Yu:
Uncertainty in Extreme Multi-label Classification. CoRR abs/2210.10160 (2022) - 2021
- [c40]Xuanqing Liu, Wei-Cheng Chang, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon:
Label Disentanglement in Partition-based Extreme Multilabel Classification. NeurIPS 2021: 15359-15369 - [c39]Patrick H. Chen, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh:
DRONE: Data-aware Low-rank Compression for Large NLP Models. NeurIPS 2021: 29321-29334 - [i20]Xuanqing Liu, Wei-Cheng Chang, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon:
Label Disentanglement in Partition-based Extreme Multilabel Classification. CoRR abs/2106.12751 (2021) - [i17]Eli Chien, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Jiong Zhang, Olgica Milenkovic, Inderjit S. Dhillon:
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction. CoRR abs/2111.00064 (2021) - [i16]Yuanhao Xiong, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit S. Dhillon:
Extreme Zero-Shot Learning for Extreme Text Classification. CoRR abs/2112.08652 (2021) - 2020
- [c38]Liwei Wu, Hsiang-Fu Yu, Nikhil Rao, James Sharpnack, Cho-Jui Hsieh:
Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering. AISTATS 2020: 776-787 - [c37]Xuanqing Liu, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh:
Learning to Encode Position for Transformer with Continuous Dynamical Model. ICML 2020: 6327-6335 - [i15]Xuanqing Liu, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh:
Learning to Encode Position for Transformer with Continuous Dynamical Model. CoRR abs/2003.09229 (2020) - 2019
- [c33]Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon:
Parallel Asynchronous Stochastic Coordinate Descent with Auxiliary Variables. AISTATS 2019: 2641-2649 - [c32]Qin Ding, Hsiang-Fu Yu, Cho-Jui Hsieh:
A Fast Sampling Algorithm for Maximum Inner Product Search. AISTATS 2019: 3004-3012 - [i9]Liwei Wu, Hsiang-Fu Yu, Nikhil Rao, James Sharpnack, Cho-Jui Hsieh:
Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering. CoRR abs/1905.12217 (2019) - 2018
- [c29]Chao Jiang, Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang:
Learning Word Embeddings for Low-Resource Languages by PU Learning. NAACL-HLT 2018: 1024-1034 - [i7]Chao Jiang, Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang:
LearningWord Embeddings for Low-resource Languages by PU Learning. CoRR abs/1805.03366 (2018) - 2017
- [c27]Hsiang-Fu Yu, Cho-Jui Hsieh, Qi Lei, Inderjit S. Dhillon:
A Greedy Approach for Budgeted Maximum Inner Product Search. NIPS 2017: 5453-5462 - 2016
- [j31]Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun, S. V. N. Vishwanathan, Inderjit S. Dhillon:
Nomadic Computing for Big Data Analytics. Computer 49(4): 52-60 (2016) - [c24]Yang You, Xiangru Lian, Ji Liu, Hsiang-Fu Yu, Inderjit S. Dhillon, James Demmel, Cho-Jui Hsieh:
Asynchronous Parallel Greedy Coordinate Descent. NIPS 2016: 4682-4690 - [i6]Hsiang-Fu Yu, Cho-Jui Hsieh, Qi Lei, Inderjit S. Dhillon:
A Greedy Approach for Budgeted Maximum Inner Product Search. CoRR abs/1610.03317 (2016) - 2015
- [c23]Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit S. Dhillon:
PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent. ICML 2015: 2370-2379 - [c21]Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun, S. V. N. Vishwanathan, Inderjit S. Dhillon:
A Scalable Asynchronous Distributed Algorithm for Topic Modeling. WWW 2015: 1340-1350 - [i5]Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit S. Dhillon:
PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent. CoRR abs/1504.01365 (2015) - 2014
- [j28]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) - [j26]Hyokun Yun, Hsiang-Fu Yu, Cho-Jui Hsieh, S. V. N. Vishwanathan, Inderjit S. Dhillon:
NOMAD: Nonlocking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion. Proc. VLDB Endow. 7(11): 975-986 (2014) - [i3]Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun, S. V. N. Vishwanathan, Inderjit S. Dhillon:
A Scalable Asynchronous Distributed Algorithm for Topic Modeling. CoRR abs/1412.4986 (2014) - 2013
- [i1]Hyokun Yun, Hsiang-Fu Yu, Cho-Jui Hsieh, S. V. N. Vishwanathan, Inderjit S. Dhillon:
NOMAD: Non-locking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion. CoRR abs/1312.0193 (2013) - 2012
- [j16]Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin:
Large Linear Classification When Data Cannot Fit in Memory. ACM Trans. Knowl. Discov. Data 5(4): 23:1-23:23 (2012) - [c18]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 - 2011
- [c17]Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin:
Large Linear Classification When Data Cannot Fit in Memory. IJCAI 2011: 2777-2782 - 2010
- [c16]Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin:
Large linear classification when data cannot fit in memory. KDD 2010: 833-842 - 2009
- [c15]Hung-Yi Lo, Kai-Wei Chang, Shang-Tse Chen, Tsung-Hsien Chiang, Chun-Sung Ferng, Cho-Jui Hsieh, Yi-Kuang Ko, Tsung-Ting Kuo, Hung-Che Lai, Ken-Yi Lin, Chia-Hsuan Wang, Hsiang-Fu Yu, Chih-Jen Lin, Hsuan-Tien Lin, Shou-De Lin:
An Ensemble of Three Classifiers for KDD Cup 2009: Expanded Linear Model, Heterogeneous Boosting, and Selective Naive Bayes. KDD Cup 2009: 57-64
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last updated on 2024-04-25 02:47 CEST by the dblp team
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