Jiong Zhang

29 papers A* 13A 4Journal 12
YearRankTypeTitle / Venue / Authors
2025 J jnl
Retrieval-augmented Encoders for Extreme Multi-label Text Classification.
CoRR
Yau-Shian Wang, Wei-Cheng Chang, Jyun-Yu Jiang, Jiong Zhang, Hsiang-Fu Yu, S. V. N. Vishwanathan
2024 A* conf
Entity Disambiguation with Extreme Multi-label Ranking.
WWW
Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh, Hsiang-Fu Yu
2024 A conf
PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models.
WSDM
Wei-Cheng Chang, Jyun-Yu Jiang, Jiong Zhang, Mutasem Al-Darabsah, Choon Hui Teo, Cho-Jui Hsieh, Hsiang-Fu Yu, S. V. N. Vishwanathan
2023 A conf
Build Faster with Less: A Journey to Accelerate Sparse Model Building for Semantic Matching in Product Search.
CIKM
Jiong Zhang, Yau-Shian Wang, Wei-Cheng Chang, Wei Li, Jyun-Yu Jiang, Cho-Jui Hsieh, Hsiang-Fu Yu
2023 J jnl
PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models.
CoRR
Wei-Cheng Chang, Jyun-Yu Jiang, Jiong Zhang, Mutasem Al-Darabsah, Choon Hui Teo, Cho-Jui Hsieh, Hsiang-Fu Yu, S. V. N. Vishwanathan
2023 A* conf
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation.
ICML
Eli Chien, Jiong Zhang, Cho-Jui Hsieh, Jyun-Yu Jiang, Wei-Cheng Chang, Olgica Milenkovic, Hsiang-Fu Yu
2023 J jnl
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation.
CoRR
Eli Chien, Jiong Zhang, Cho-Jui Hsieh, Jyun-Yu Jiang, Wei-Cheng Chang, Olgica Milenkovic, Hsiang-Fu Yu
2023 A* conf
Representer Point Selection for Explaining Regularized High-dimensional Models.
ICML
Che-Ping Tsai, Jiong Zhang, Hsiang-Fu Yu, Eli Chien, Cho-Jui Hsieh, Pradeep Kumar Ravikumar
2023 J jnl
Representer Point Selection for Explaining Regularized High-dimensional Models.
CoRR
Che-Ping Tsai, Jiong Zhang, Eli Chien, Hsiang-Fu Yu, Cho-Jui Hsieh, Pradeep Ravikumar
2023 A* conf
Uncertainty Quantification for Extreme Classification.
SIGIR
Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh, Hsiang-Fu Yu
2022 A* conf
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction.
ICLR
Eli Chien, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Jiong Zhang, Olgica Milenkovic, Inderjit S. Dhillon
2022 A* conf
PECOS: Prediction for Enormous and Correlated Output Spaces.
KDD
Hsiang-Fu Yu, Jiong Zhang, Wei-Cheng Chang, Jyun-Yu Jiang, Wei Li, Cho-Jui Hsieh
2022 J jnl
PECOS: Prediction for Enormous and Correlated Output Spaces.
J. Mach. Learn. Res.
Hsiang-Fu Yu, Kai Zhong, Jiong Zhang, Wei-Cheng Chang, Inderjit S. Dhillon
2022 A* conf
Relevance under the Iceberg: Reasonable Prediction for Extreme Multi-label Classification.
SIGIR
Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh, Hsiang-Fu Yu
2022 J jnl
Uncertainty in Extreme Multi-label Classification.
CoRR
Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh, Hsiang-Fu Yu
2021 A* conf
Extreme Multi-label Learning for Semantic Matching in Product Search.
KDD
Wei-Cheng Chang, Daniel L. Jiang, Hsiang-Fu Yu, Choon-Hui Teo, Jiong Zhang, Kai Zhong, Kedarnath Kolluri, Qie Hu, Nikhil Shandilya, Vyacheslav Ievgrafov, Japinder Singh, Inderjit S. Dhillon
2021 J jnl
Extreme Multi-label Learning for Semantic Matching in Product Search.
CoRR
Wei-Cheng Chang, Daniel L. Jiang, Hsiang-Fu Yu, Choon-Hui Teo, Jiong Zhang, Kai Zhong, Kedarnath Kolluri, Qie Hu, Nikhil Shandilya, Vyacheslav Ievgrafov, Japinder Singh, Inderjit S. Dhillon
2021 A* conf
Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification.
NeurIPS
Jiong Zhang, Wei-Cheng Chang, Hsiang-Fu Yu, Inderjit S. Dhillon
2021 J jnl
Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification.
CoRR
Jiong Zhang, Wei-Cheng Chang, Hsiang-Fu Yu, Inderjit S. Dhillon
2021 J jnl
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction.
CoRR
Eli Chien, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Jiong Zhang, Olgica Milenkovic, Inderjit S. Dhillon
2019 A* conf
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks.
NeurIPS
Jiong Zhang, Hsiang-Fu Yu, Inderjit S. Dhillon
2019 J jnl
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks.
CoRR
Jiong Zhang, Hsiang-Fu Yu, Inderjit S. Dhillon
2019 A conf
Extreme Stochastic Variational Inference: Distributed Inference for Large Scale Mixture Models.
AISTATS
Jiong Zhang, Parameswaran Raman, Shihao Ji, Hsiang-Fu Yu, S. V. N. Vishwanathan, Inderjit S. Dhillon
2018 A* conf
Learning Long Term Dependencies via Fourier Recurrent Units.
ICML
Jiong Zhang, Yibo Lin, Zhao Song, Inderjit S. Dhillon
2018 J jnl
Learning Long Term Dependencies via Fourier Recurrent Units.
CoRR
Jiong Zhang, Yibo Lin, Zhao Song, Inderjit S. Dhillon
2018 A* conf
Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization.
ICML
Jiong Zhang, Qi Lei, Inderjit S. Dhillon
2018 J jnl
Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization.
CoRR
Jiong Zhang, Qi Lei, Inderjit S. Dhillon
2017 A conf
Scalable Convex Multiple Sequence Alignment via Entropy-Regularized Dual Decomposition.
AISTATS
Jiong Zhang, Ian En-Hsu Yen, Pradeep Ravikumar, Inderjit S. Dhillon
2016 A* conf
A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery.
ICML
Ian En-Hsu Yen, Xin Lin, Jiong Zhang, Pradeep Ravikumar, Inderjit S. Dhillon