Jiong Zhang

According to our database, Jiong Zhang authored at least 20 papers between 2016 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

Retrieval-augmented Encoders for Extreme Multi-label Text Classification.

Yau-Shian Wang, Wei-Cheng Chang, Jyun-Yu Jiang, Jiong Zhang, Hsiang-Fu Yu, S. V. N. Vishwanathan

CoRR, February, 2025

2024

Entity Disambiguation with Extreme Multi-label Ranking.

Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh, Hsiang-Fu Yu

Proceedings of the ACM on Web Conference 2024, 2024

PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models.

Wei-Cheng Chang, Jyun-Yu Jiang, Jiong Zhang, Mutasem Al-Darabsah, Choon Hui Teo, Cho-Jui Hsieh, Hsiang-Fu Yu, S. V. N. Vishwanathan

Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 2024

2023

Uncertainty Quantification for Extreme Classification.

Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh, Hsiang-Fu Yu

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 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

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the International Conference on Machine Learning, 2023

Build Faster with Less: A Journey to Accelerate Sparse Model Building for Semantic Matching in Product Search.

Jiong Zhang, Yau-Shian Wang, Wei-Cheng Chang, Wei Li, Jyun-Yu Jiang, Cho-Jui Hsieh, Hsiang-Fu Yu

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023

2022

PECOS: Prediction for Enormous and Correlated Output Spaces.

Hsiang-Fu Yu, Kai Zhong, Jiong Zhang, Wei-Cheng Chang, Inderjit S. Dhillon

J. Mach. Learn. Res., 2022

Uncertainty in Extreme Multi-label Classification.

Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh, Hsiang-Fu Yu

CoRR, 2022

Relevance under the Iceberg: Reasonable Prediction for Extreme Multi-label Classification.

Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh, Hsiang-Fu Yu

Proceedings of the SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11, 2022

PECOS: Prediction for Enormous and Correlated Output Spaces.

Hsiang-Fu Yu, Jiong Zhang, Wei-Cheng Chang, Jyun-Yu Jiang, Wei Li, Cho-Jui Hsieh

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 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

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification.

Jiong Zhang, Wei-Cheng Chang, Hsiang-Fu Yu, Inderjit S. Dhillon

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Extreme Multi-label Learning for Semantic Matching in Product Search.

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

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

2019

AutoAssist: A Framework to Accelerate Training of Deep Neural Networks.

Jiong Zhang, Hsiang-Fu Yu, Inderjit S. Dhillon

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 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 S. Dhillon

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Learning Long Term Dependencies via Fourier Recurrent Units.

Jiong Zhang, Yibo Lin, Zhao Song, Inderjit S. Dhillon

Proceedings of the 35th International Conference on Machine Learning, 2018

Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization.

Jiong Zhang, Qi Lei, Inderjit S. Dhillon

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Scalable Convex Multiple Sequence Alignment via Entropy-Regularized Dual Decomposition.

Jiong Zhang, Ian En-Hsu Yen, Pradeep Ravikumar, Inderjit S. Dhillon

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery.

Ian En-Hsu Yen, Xin Lin, Jiong Zhang, Pradeep Ravikumar, Inderjit S. Dhillon

Proceedings of the 33nd International Conference on Machine Learning, 2016