Conferences
[NeurIPS’2022] 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’2022] 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’2022] 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’2022] 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.[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.[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)).[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%)[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%)[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))
Journals
[OSR’21] Scalable Graph Neural Network Training: The Case for Sampling. [PDF]
Marco Serafini, Hui Guan.
In ACM SIGOPS Operating Systems Review, 2021.[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.[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
Workshop & Poster Papers
[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
ICML2022 AI4Science Workshop[MCHPC’21] FreeLunch: Compression-based GPU Memory Management for Convolutional Neural Networks. [PDF]
Shaurya Patel, Tongping Liu, Hui Guan.
In MCHPC’21 Workshop.[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.[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.[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.