Hui Guan

Hui Guan

Assistant Professor
College of Information & Computer Sciences
University of Massachusetts Amherst

2024

  • [ASPLOS’24] Proteus: A High-Throughput Inference-Serving System with Accuracy Scaling. [PDF][Code]
    Sohaib Ahmad, Hui Guan, Brain D. Friedman, Thomas Williams, Ramesh K. Sitaraman, Thomas Woo.
    The 2024 ACM Conference on Architectural Support for Programming Languages and Operating Systems, April 27-May 1, 2024.

2023

  • [NeurIPS’23] Flow: Per-instance Personalized Federated Learning. [PDF][Code]
    Kunjal Panchal, Sunav Choudhary, Nisarg Parikh, Lijun Zhang, Hui Guan.
    The 2023 Conference on Neural Information Processing Systems, Dec. 10-16, 2023.

  • [PACT’23] GraphMini: Accelerating Graph Pattern Matching Using Auxiliary Graphs. [PDF][Code]
    Juelin Liu, Sandeep Polisetty, Hui Guan, Marco Serafini.
    The 32nd International Conference on Parallel Architectures and Compilation Techniques, Oct. 21-25, 2023.

  • [TNNLS’23] A Tree-Structured Multi-Task Model Architectures Recommendation System. [PDF][Code]
    Lijun Zhang, Xiao Liu, Hui Guan.
    IEEE Transactions on Neural Networks and Learning Systems, 2023.

  • [Manuscript] GSplit: Scaling Graph Neural Network Training on Large Graphs via Split-Parallelism. [PDF]
    Sandeep Polisetty, Juelin Liu, Kibi Falus, Yi Ren Fung, Seung-Hwan Lim, Hui Guan, Marco Serafini.
    Arxiv, 2023.

  • [ICML’23] Flash: Concept Drift Adaptation in Federated Learning. [PDF]
    Kunjal Panchal, Sunav Choudhary, Subrata Mitra, Koyel Mukherjee, Somdeb Sarkhel, Saayan Mitra, Hui Guan.
    40th International Conference on Machine Learning, Jul. 23-29, 2023

  • [ICML’23] Automatically marginalized MCMC in probabilistic programming. [PDF]
    Jinlin Lai, Javier Burroni, Hui Guan, Daniel Sheldon.
    40th International Conference on Machine Learning, Jul. 23-29, 2023

  • [ISMM’23] NUMAlloc: A Faster NUMA Memory Allocator. [PDF]
    Hanmei Yang, Xin Zhao, Jin Zhou, Wei Wang, Sandip Kundu, Bo Wu, Hui Guan, and Tongping Liu.
    ACM SIGPLAN International Symposium on Memory Management, 2023.

  • [MobiCom’23] Re-thinking computation offload for efficient inference on IoT devices with duty-cycled radios. [PDF]
    Jin Huang, Hui Guan, Deepak Ganesan.
    The 29th International Conference on Mobile Computing and Networking, Madrid, Spain, Oct. 2-6, 2023

  • [IEEE Access’23] An Alternative Hard-Parameter Sharing Paradigm for Multi-Domain Learning. [PDF]
    Lijun Zhang, Qizheng Yang, Xiao Liu, Hui Guan.
    In IEEE Access, 2023.

2022

  • [NeurIPS’22] AutoMTL: A Programming Framework for Automating Efficient Multi-Task Learning. [PDF][Code]
    Lijun Zhang, Xiao Liu, Hui Guan.
    36th Conference on Neural Information Processing Systems (NeurIPS 2022), November 28, 2022. (Acceptance rate: 25.6%)

  • [AutoML’22] A Tree-Structured Multi-Task Model Recommender. [PDF][Code][Teaser][Video]
    Lijun Zhang, Xiao Liu, Hui Guan.
    1st International Conference on Automated Machine Learning, July 25-27, 2022. (Acceptance rate: 19.2%)

  • [ICME’22] Rethinking Hard-Parameter Sharing in Multi-Domain Learning. [PDF]
    Lijun Zhang, Qizheng Yang, Xiao Liu, Hui Guan.
    IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME), Taipei, Taiwan, July 18-22, 2022. (Acceptance rate: 29%)

  • [CGO’22] Enabling Near Real-Time NLU-Driven Natural Language Programming through Dynamic Grammar Graph-Based Translation. [PDF]
    Zifan Nan, Xipeng Shen, Hui Guan.
    The 2022 International Symposium on Code Generation and Optimization (CGO), Seoul, South Korea, 2022.

  • [VLDB’22] COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression. [PDF]
    Sian Jin, Chengming Zhang, Xintong Jiang, Yunhe Feng, Hui Guan, Guanpeng Li, Shuaiwen Leon Song, and Dingwen Tao.
    In International Conference on Very Large Data Bases, 2022.

  • [CrossFL’22] Flow: Fine-grained Personalized Federated Learning through Dynamic Routing. [PDF][Poster]
    Kunjal Panchal, Hui Guan
    CrossFL 2022 Workshop @ MLSys’22

  • [AI4Science@ICML’22] Improving Subgraph Representation Learning via Multi-View Augmentation. [PDF][talk]
    Yili Shen, Jiaxu Yan, Cheng-Wei Ju, Jun Yi, Zhou Lin, Hui Guan
    ICML 2022 AI4Science Workshop

2021

  • [ICDM’21] Recurrent Neural Networks Meet Context-Free Grammar: Two Birds with One Stone. [PDF]
    Hui Guan, Umang Chaudhary, Yuanchao Xu, Lin Ning, Lijun Zhang, and Xipeng Shen.
    In IEEE International Conference on Data Mining, 2021 (short paper). (Acceptance rate: 20% (198/990))

  • [ICS’21] NumaPerf: Predictive and Comprehensive NUMA Profiling. [PDF]
    Xin Zhao, Jin Zhou, Hui Guan, Wei Wang, Xu Liu, Tongping Liu.
    In Proceedings of International Conference on Supercomputing, 2021. (Acceptance rate: 25% (39/157))

  • [CC’21] Deep NLP-Based Co-Evolvement for Synthesizing Code Analysis from Natural Language. [PDF]
    Zifan Nan, Hui Guan, Xipeng Shen, and Chunhua Liao.
    In The ACM SIGPLAN 2021 International Conference on Compiler Construction, 2021.

  • [OSR’21] Scalable Graph Neural Network Training: The Case for Sampling. [PDF]
    Marco Serafini, Hui Guan.
    In ACM SIGOPS Operating Systems Review, 2021.

  • [MCHPC’21] FreeLunch: Compression-based GPU Memory Management for Convolutional Neural Networks. [PDF]
    Shaurya Patel, Tongping Liu, Hui Guan.
    In MCHPC’21 Workshop.

  • [CACM’21] CoCoPIE: Enabling Real-Time AI on Off-the-Shelf Mobile Devices via Compression-Compilation Co-Design. [PDF]
    Hui Guan, Shaoshan Liu, Xiaolong Ma, Wei Niu, Bin Ren, Xipeng Shen, Yanzhi Wang, Pu Zhao. (Authors in Alphabetical Order)
    In Communications of the ACM, 2021.

  • [InformationSystems’21] Reuse-Centric K-Means Configuration. [PDF]]
    Lijun Zhang, Hui Guan, Yufei Ding, Xipeng Shen, Hamid Krim.
    Information Systems, 2021.

2020 and Before

  • [FSE’20] HISyn: Human Learning-Inspired Natural Language Programming. [PDF]
    Zifan Nan, Hui Guan, Xipeng Shen.
    In The ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Sacramento, California, United States, November 2020. (Acceptance rate: 101/360=28%)

  • [MLSys’20] FLEET: Flexible Efficient Ensemble Training for Heterogeneous Deep Neural Networks. [PDF]
    Hui Guan, Laxmikant Kishor Mokadam, Xipeng Shen, Robert Patton.
    MLSys’20. (Acceptance rate: 20.0% (34/170)).

  • [TPDS’20] An Automatic Synthesizer of Advising Tools for High Performance Computing. [PDF]
    Hui Guan, Xipeng Shen, and Hamid Krim.
    In IEEE Transactions on Parallel and Distributed Systems (TPDS), 2020

  • [NeurIPS’19] In-Place Zero-Space Memory Protection for CNN. [PDF]
    Hui Guan, Lin Ning, Zhen Lin, Xipeng Shen, Huiyang Zhou, and Seung-Hwan Lim.
    In Advances in Neural Information Processing Systems, pp. 5735-5744. 2019. (Acceptance rate: 21.2% (1428/6743))

  • [PLDI’19] Wootz: a Compiler-based Framework for Fast CNN Pruning via Composability. [PDF]
    Hui Guan, Xipeng Shen, and Seung-Hwan Lim.
    In Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, pp. 717-730. ACM, 2019. (Acceptance rate: 27.7% (76/274))

  • [ICDE’19] Adaptive Deep Reuse: Accelerating CNN Training on the Fly. [PDF]
    Lin Ning, Hui Guan, and Xipeng Shen.
    In 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 1538-1549. IEEE, 2019. (Acceptance rate: 18%)

  • [MLSys@NeurIPS’19] Post-Training 4-bit Quantization on Embedding Tables. [PDF]
    Hui Guan, Andrey Malevich, Jiyan Yang, Jongsoo Park, and Hector Yuen.
    MLSys Workshop on Systems for ML @ NeurIPS, 2019.

  • [SC’18] Exploring Flexible Communications for Streamlining DNN Ensemble Training Pipelines. [PDF]
    Randall Pittman, Hui Guan, Xipeng Shen, Seung-Hwan Lim, and Robert M. Patton.
    In Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, p. 64. IEEE, 2018. (Acceptance rate: 23%)

  • [ICDE’18] Reuse-Centric K-Means Configuration. [PDF]]
    Hui Guan, Yufei Ding, Xipeng Shen, and Hamid Krim.
    In 2018 IEEE 34th International Conference on Data Engineering (ICDE), pp. 1224-1227. IEEE, 2018. (short paper) (Acceptance rate: 23%)

  • [SysML’18] TOP: A Compiler-Based Framework for Optimizing Machine Learning Algorithms through Generalized Triangle Inequality. [PDF]
    Yufei Ding, Lin Ning, Hui Guan, Xipeng Shen, Madanlal Musuvathi, Todd Mytkowicz.
    SysML, Feb 16th, 2018, Stanford University, 2018.

  • [SC’17] Egeria: a Framework for Automatic Synthesis of HPC Advising Tools through Multi-Layered Natural Language Processing. [PDF]
    Hui Guan, Xipeng Shen, and Hamid Krim.
    In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, p. 10. ACM, 2017. (Acceptance rate: 18% (61/327))

  • [PLDI’17] “Generalizations of the Theory and Deployment of Triangular Inequality for Compiler-Based Strength Reduction. [PDF]
    Yufei Ding, Lin Ning, Hui Guan, and Xipeng Shen.
    In Proceedings of the 38th ACM SIGPLAN Conference on Programming Language Design and Implementation, pp. 33-48. ACM, 2017. (Acceptance rate: 15% (47/322))

  • [SPAWC’16] A topological collapse for document summarization. [PDF]
    Hui Guan, Wen Tang, Hamid Krim, James Keiser, Andrew Rindos, and Radmila Sazdanovic. In 2016 IEEE 17th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp. 1-5. IEEE, 2016.