Full list [Google Scholar]. * indicates equal contributions
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Preprint AutoL2S: Auto Long-Short Reasoning for Efficient Large Language Models
Feng Luo*, Yu-Neng Chuang*, Guanchu Wang, Hoang Anh Duy Le, Shaochen Zhong, Hongyi Liu, Jiayi Yuan, Yang Sui, Vladimir Braverman, Vipin Chaudhary, Xia Hu
[Summary] [Paper] [Code]
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Preprint Assessing and Enhancing Large Language Models in Rare Disease Question-answering
Guanchu Wang*, M.S., Junhao Ran∗, B.Eng., Ruixiang Tang, Ph.D., Chia-Yuan Chang, M.S., Yu-Neng Chuang, M.S., Zirui Liu, Ph.D., Vladimir Braverman, Ph.D., Zhandong Liu, Ph.D., Xia Hu, Ph.D.
[Summary] [Paper] [Code]
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Preprint Understanding Different Design Choices in Training Large Time Series Models.
Yu-Neng Chuang*, Songchen Li*, Jiayi Yuan*, Guanchu Wang*, Kwei-Herng Lai, Leisheng Yu, Sirui Ding, Chia-Yuan Chang, Qiaoyu Tan, Daochen Zha, Xia Hu
[Summary] [Paper] [Code]
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Preprint FaithLM: Towards Faithful Explanations for Large Language Models.
Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, Ruixiang Tang, Shaochen Zhong, Fan Yang, Mengnan Du, Xuanting Cai, and Xia Hu
[Summary] [Paper] [Code]
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NeurIPS 2025 Breaking the Frozen Subspace: Importance Sampling for Low-Rank Optimization in LLM Pretraining
Haochen Zhang, Junze Yin, Guanchu Wang, Zirui Liu, Lin Yang, Tianyi Zhang, Anshumali Shrivastava, Vladimir Braverman
Neural Information Processing Systems, NeurIPS 2023.
[Summary] [Paper] [Code]
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EMNLP Finding Self-ensemble: Mitigating Confidence Distortion for Large Language Models
Zicheng Xu*, Guanchu Wang*, Guangyao Zheng, Yu-Neng Chuang, Alexander Szalay, Xia Hu, Vladimir Braverman
Finding of Empirical Methods in Natural Language Processing, EMNLP 2025.
[Summary] [Paper] [Code]
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EMNLP Finding A Decoupled Multi-Agent Framework for Complex Text Style Transfer
LingXi Zhang, Yu-Neng Chuang, Guanchu Wang, Ruixiang Tang, Xuanting Cai, Rajesh Shenoy, Xia Hu
Finding of Empirical Methods in Natural Language Processing, EMNLP 2025.
[Summary] [Paper] [Code]
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Bioinform. Adv. Survey and Improvement Strategies for Gene Prioritization with Large Language Models
Matthew B. Neeley*, B.S., Guantong Qi*, B.S., Guanchu Wang*, M.S., Ruixiang Tang, Ph.D., Dongxue Mao, Ph.D., Chaozhong Liu, Ph.D., Sasidhar Pasupuleti, M.S., Bo Yuan, Ph.D., Fan Xia, Ph.D., Hugo Bellen, D.V.M, Ph.D., Pengfei Liu, Ph.D., Zhandong Liu, Ph.D., Xia Hu, Ph.D.
Bioinformatics Advances, 2025.
[Summary] [Paper] [Code]
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TMLR Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models
Yang Sui, Yu-Neng Chuang, Guanchu Wang, Jiamu Zhang, Tianyi Zhang, Jiayi Yuan, Hongyi Liu, Andrew Wen, Shaochen (Henry) Zhong, Hanjie Chen, Xia Hu
Transactions on Machine Learning Research, 2025.
[Summary] [Paper] [Code]
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KDD-Expo 2025 Advancing Table Understanding of Large Language Models via Feature Re-ordering.
Guanchu Wang, Yuzhong Chen, Huiyuan Chen, Xiran Fan, Junpeng Wang, Xiaoting Li, Mingzhi Hu, Chia-Yuan Chang, Xia Hu
KDD Exploration, June issue, 2025.
[Summary] [Paper] [Code] [Abstract]
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ACL 2025 Quantized Can Still Be Calibrated: A Unified Framework to Calibration in Quantized Large Language Models
Mingyu Zhong, Guanchu Wang, Yu-Neng Chuang, Na Zou
Association for Computational Linguistics, ACL 2025.
[Summary] [Paper] [Code]
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ACL 2025 MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation
Chia-Yuan Chang, Zhimeng Jiang, Vineeth Rakesh, Menghai Pan, Chin-Chia Michael Yeh, Guanchu Wang, Mingzhi Hu, Zhichao Xu, Yan Zheng, Mahashweta Das, Na Zou
Association for Computational Linguistics, ACL 2025.
[Summary] [Paper] [Code]
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AISTATS 2025 Personalizing Low-rank Bayesian Neural Networks Via Federated Learning.
Boning Zhang, Dongzhu Liu, Osvaldo Simeone, Guanchu Wang, Dimitrios Pezaros, Guangxu Zhu
Artificial Intelligence and Statistics, AISTATS 2025.
[Summary] [Paper] [Code]
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EMNLP 2024 TaylorUnswift: Secured Weight Release for Large Language Models via Taylor Expansion.
Guanchu Wang*, Yu-Neng Chuang*, Ruixiang Tang, Shaochen Zhong, Jiayi Yuan, Hongye Jin, Zirui Liu, Vipin Chaudhary, Shuai Xu, James Caverlee, Xia Hu
Empirical Methods in Natural Language Processing, EMNLP 2024.
[Summary] [Paper] [Code]
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EMNLP 2024 Findings KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approaches.
Jiayi Yuan*, Hongyi Liu*, Shaochen Zhong*, Yu-Neng Chuang*, Songchen Li, Guanchu Wang, Duy Le, Hongye Jin, Vipin Chaudhary, Zhaozhuo Xu, Zirui Liu, Xia Hu
Finding of Empirical Methods in Natural Language Processing, EMNLP 2024.
[Summary] [Paper] [Code]
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TKDD 2024 Efficient GNN Explanation via Learning Removal-based Attribution.
Yao Rong, Guanchu Wang, Qizhang Feng, Ninghao Liu, Zirui Liu, Enkelejda Kasneci and Xia Hu
Transactions on Knowledge Discovery from Data.
[Summary] [Paper] [Code]
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ICML 2024 TVE: Learning Meta-attribution for Transferable Vision Explainer.
Guanchu Wang, Yu-Neng Chuang, Fan Yang, Mengnan Du, Chia-Yuan Chang, Shaochen Zhong, Zirui Liu, Zhaozhuo Xu, Kaixiong Zhou, Xuanting Cai, and Xia Hu
International Conference on Machine Learning, ICML 2024.
[Summary] [Paper] [Code]
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NeurIPS 2023 Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model.
Zirui Liu*, Guanchu Wang*, Shaochen Zhong, Zhaozhuo Xu, Daochen Zha, Ruixiang Tang, Zhimeng Jiang, Kaixiong Zhou, Vipin Chaudhary, Shuai Xu and Xia Hu
Neural Information Processing Systems, NeurIPS 2023.
[Summary] [Paper] [Code]
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CIKM 2023 DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research.
Yu-neng Chuang, Guanchu Wang, Chia-Yuan Zhang, Kwei-Herng Lai, Ruixiang Tang, Fan Yang, Alfredo Costilla-Reyes, Kaixiong Zhou, Xiaoqian Jiang and Xia Hu
International Conference on Information and Knowledge Management, CIKM 2023, Demo Track, Best Paper Honorable Mention.
[Summary] [Paper] [Code] [Demo] [FrontEnd]
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ECML-PKDD 2023 Mitigating Algorithmic Bias with Limited Annotations.
Guanchu Wang, Mengnan Du, Ninghao Liu, Na Zou and Xia Hu
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
ECML-PKDD 2023
[Summary] [Paper] [Code]
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ICML 2023 DIVISION: Memory Efficient Training via Dual Activation Precision.
Guanchu Wang, Zirui Liu, Zhimeng Jiang, Ninghao Liu, Na Zou and Xia Hu
International Conference on Machine Learning, ICML 2023.
[Summary] [Paper] [Code]
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ICLR 2023 CoRTX: Contrastive Learning for Real-time Explanations.
Yu-Neng Chuang*, Guanchu Wang*, Fan Yang, Quan Zhou, Pushkar Tripathi, Xuanting Cai and Xia Hu
(* Equal contribution) International Conference on Learning Representations, ICLR 2023.
[Summary] [Paper] [Code]
Applied to Meta Ads Transparency [News]
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CIKM 2022 BED: A Real-Time Object Detection System for Edge Devices.
Guanchu Wang*, Zaid Pervaiz Bhat∗, Zhimeng Jiang∗, Yi-Wei Chen∗, Daochen Zha∗, Alfredo Costilla Reyes∗, Afshin Niktash, Gorkem Ulkar, Erman Okman, Xuanting Cai, Xia Hu
(* Equal contribution) International Conference on Information and Knowledge Management, Demo Track, Best Paper Award.
[Summary] [Video] [Talk] [Paper] [Slide] [Code]
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ICML 2022 Accelerating Shapley Explanation via Contributive Cooperator Selection.
Guanchu Wang*, Yu-Neng Chuang*, Mengnan Du, Fan Yang, Quan Zhou, Pushkar Tripathi, Xuanting Cai and Xia Hu
(* Equal contribution) International Conference on Machine Learning, Spotlight of ICML 2022.
[Summary] [Paper] [Code] [Slide]
Applied to Meta Ads Transparency [Link]
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NeurIPS 2021 Fairness via Representation Neutralization.
Mengnan Du, Subhabrata Mukherjee, Guanchu Wang, Ruixiang Tang, Ahmed Hassan Awadallah, and Xia Hu
Neural Information Processing Systems, NeurIPS 2021.
[Summary] [Paper] [Code] [Poster]
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NeurIPS 2021 Revisiting Time Series Outlier Detection: Definitions and Benchmarks.
Kwei-Herng Lai, Daochen Zha, Junjie Xu, Yue Zhao, Guanchu Wang, and Xia Hu
Neural Information Processing Systems, NeurIPS 2021.
[Summary] [Paper] [Code] [Poster]
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AAAI 2021 TODS: An Automated Time Series Outlier Detection System.
Kwei-Herng Lai*, Daochen Zha*, Guanchu Wang, Junjie Xu, Yue Zhao, Devesh Kumar, Yile Chen, Purav Zumkhawaka, Minyang Wan, Diego Martinez, and Xia Hu
AAAI Conference on Artificial Intelligence, AAAI 2021, Demo Track.
[Summary] [Video][Paper] [Code]
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IJCAI 2020 Independent Skill Transfer for Deep Reinforcement Learning.
Qiangxing Tian, Guanchu Wang, Jinxin Liu, and Donglin Wang
International Joint Conference on Artificial Intelligence, IJCAI 2020 (Accepted rate 12.6%).
[Summary] [Paper] [Code] [Poster]
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