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. [Coming Soon]
  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.