Yu, Hsiang-Fu

25 publications

ICML 2023 PINA: Leveraging Side Information in eXtreme Multi-Label Classification via Predicted Instance Neighborhood Aggregation Eli Chien, Jiong Zhang, Cho-Jui Hsieh, Jyun-Yu Jiang, Wei-Cheng Chang, Olgica Milenkovic, Hsiang-Fu Yu

ICML 2023 Representer Point Selection for Explaining Regularized High-Dimensional Models Che-Ping Tsai, Jiong Zhang, Hsiang-Fu Yu, Eli Chien, Cho-Jui Hsieh, Pradeep Kumar Ravikumar

NeurIPS 2022 ELIAS: End-to-End Learning to Index and Search in Large Output Spaces Nilesh Gupta, Patrick Chen, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon

ICLR 2022 Node Feature Extraction by Self-Supervised Multi-Scale Neighborhood Prediction Eli Chien, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Jiong Zhang, Olgica Milenkovic, Inderjit S Dhillon

JMLR 2022 PECOS: Prediction for Enormous and Correlated Output Spaces Hsiang-Fu Yu, Kai Zhong, Jiong Zhang, Wei-Cheng Chang, Inderjit S. Dhillon

NeurIPS 2021 DRONE: Data-Aware Low-Rank Compression for Large NLP Models Patrick Chen, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh

NeurIPS 2021 Fast Multi-Resolution Transformer Fine-Tuning for Extreme Multi-Label Text Classification Jiong Zhang, Wei-Cheng Chang, Hsiang-Fu Yu, Inderjit S. Dhillon

NeurIPS 2021 Label Disentanglement in Partition-Based Extreme Multilabel Classification Xuanqing Liu, Wei-Cheng Chang, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon

ICML 2020 Extreme Multi-Label Classification from Aggregated Labels Yanyao Shen, Hsiang-Fu Yu, Sujay Sanghavi, Inderjit Dhillon

AISTATS 2020 Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering Liwei Wu, Hsiang-Fu Yu, Nikhil Rao, James Sharpnack, Cho-Jui Hsieh

ICML 2020 Learning to Encode Position for Transformer with Continuous Dynamical Model Xuanqing Liu, Hsiang-Fu Yu, Inderjit Dhillon, Cho-Jui Hsieh

AISTATS 2019 A Fast Sampling Algorithm for Maximum Inner Product Search Qin Ding, Hsiang-Fu Yu, Cho-Jui Hsieh

NeurIPS 2019 AutoAssist: A Framework to Accelerate Training of Deep Neural Networks Jiong Zhang, Hsiang-Fu Yu, Inderjit S Dhillon

AISTATS 2019 Extreme Stochastic Variational Inference: Distributed Inference for Large Scale Mixture Models Jiong Zhang, Parameswaran Raman, Shihao Ji, Hsiang-Fu Yu, S.V.N. Vishwanathan, Inderjit Dhillon

AISTATS 2019 Parallel Asynchronous Stochastic Coordinate Descent with Auxiliary Variables Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon

NeurIPS 2019 Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting Rajat Sen, Hsiang-Fu Yu, Inderjit S Dhillon

NeurIPS 2017 A Greedy Approach for Budgeted Maximum Inner Product Search Hsiang-Fu Yu, Cho-Jui Hsieh, Qi Lei, Inderjit S Dhillon

AAAI 2017 A Unified Algorithm for One-Cass Structured Matrix Factorization with Side Information Hsiang-Fu Yu, Hsin-Yuan Huang, Inderjit S. Dhillon, Chih-Jen Lin

NeurIPS 2016 Asynchronous Parallel Greedy Coordinate Descent Yang You, Xiangru Lian, Ji Liu, Hsiang-Fu Yu, Inderjit S Dhillon, James Demmel, Cho-Jui Hsieh

NeurIPS 2016 Temporal Regularized Matrix Factorization for High-Dimensional Time Series Prediction Hsiang-Fu Yu, Nikhil Rao, Inderjit S Dhillon

NeurIPS 2015 Collaborative Filtering with Graph Information: Consistency and Scalable Methods Nikhil Rao, Hsiang-Fu Yu, Pradeep K Ravikumar, Inderjit S Dhillon

ICML 2015 PASSCoDe: Parallel ASynchronous Stochastic Dual Co-Ordinate Descent Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit Dhillon

ICML 2014 Large-Scale Multi-Label Learning with Missing Labels Hsiang-Fu Yu, Prateek Jain, Purushottam Kar, Inderjit Dhillon

MLJ 2011 Dual Coordinate Descent Methods for Logistic Regression and Maximum Entropy Models Hsiang-Fu Yu, Fang-Lan Huang, Chih-Jen Lin

IJCAI 2011 Large Linear Classification When Data Cannot Fit in Memory Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin