1. Chen Yu, Bojan Karlas, Jie Zhong, Ce Zhang, Ji Liu. Multi-device, Multi-tenant Model Selection with GP-EI. arXiv:1803.06561.
  2. Hanlin Tang, Ce Zhang, Shaoduo Gan, Tong Zhang, Ji Liu. Decentralization Meets Quantization. arXiv:1803.06443.
  1. T Li, J Zhong, J Liu, W Wu, C Zhang. Towards Multi-tenant Resource Sharing for Machine Learning Workloads. VLDB 2018.
  2. Yu Liu, Hantian Zhang, Luyuan Zeng, Wentao Wu, Ce Zhang. MLBench: Benchmarking Machine Learning Services Against Human Experts. VLDB 2018.
  3. J Jiang, B Cui, C Zhang, F Fu. DimBoost: Boosting Gradient Boosting Tree to Higher Dimensions. SIGMOD 2018.
  4. Hanlin Tang, Xiangru Lian, Ming Yan, Ce Zhang, Ji Liu. D2: Decentralized Training over Decentralized Data. ICML 2018.
  5. X Lian, W Zhang, C Zhang, J Liu. Asynchronous Decentralized Parallel Stochastic Gradient Descent. ICML 2018.
  6. H Guo, K Kara, C Zhang. Layerwise Systematic Scan: Deep Boltzmann Machines and Beyond. AISTATS 2018.
  7. Jonathan Rotsztejn, Nora Hollenstein and Ce Zhang. ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction . SemEval 2018. (SemEval Task 7 Best Paper; Top Ranked System)
  8. D Grubic, L Tam, D Alistarh, C Zhang. Synchronous Multi-GPU Deep Learning with Low-Precision Communication: An Experimental Study. EDBT 2018.
  9. H Huang, C Zheng, J Zeng, W Zhou, S Zhu, P Liu, I Molloy, S Chari, C Zhang, Q Guan. A Large-scale Study of Android Malware Development Phenomenon on Public Malware Submission and Scanning Platform. IEEE Transactions on Big Data 2018.
  10. Dominic Stark, Barthelemy Launet, Kevin Schawinski, Ce Zhang, Michael Koss, M Dennis Turp, Lia F Sartori, Hantian Zhang, Yiru Chen, Anna K Weigel. PSFGAN: a generative adversarial network system for separating quasar point sources and host galaxy light. Monthly Notices of the Royal Astronomical Society 2018.
  11. Lia F Sartori, Kevin Schawinski, Benny Trakhtenbrot, Neven Caplar, Ezequiel Treister, Michael J Koss, C Megan Urry, Ce Zhang. A model for AGN variability on multiple time-scales. Monthly Notices of the Royal Astronomical Society 2018.
  12. Bojan Karlas, Ji Liu, Wentao Wu, Ce Zhang. in Action: Towards Multi-tenant Declarative Learning Services. VLDB (Demo) 2018.
  1. X Lian, C Zhang, H Zhang, CJ Hsieh, W Zhang, J Liu. Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent. NIPS 2017 (Oral Presentation: 40/3240 submissions).
  2. H Zhang, J Li, K Kara, D Alistarh, J Liu, C Zhang. The ZipML Framework for Training Models with End-to-End Low Precision: The Cans, the Cannots, and a Little Bit of Deep Learning. ICML 2017.
  3. L Yu, B Cui, C Zhang, Y Shao. LDA*: A Robust and Large-scale Topic Modeling System. VLDB 2017.
  4. Z Zhang, Y Shao, B Cui, C Zhang. An experimental evaluation of simrank-based similarity search algorithms. VLDB 2017.
  5. J Jiang, B Cui, C Zhang, L Yu. Heterogeneity-aware distributed parameter servers. SIGMOD 2017.
  6. M Owaida, H Zhang, G Alonso, C Zhang. Scalable Inference of Decision Tree Ensembles: Flexible Design for CPU-FPGA Platforms. FPL 2017.
  7. K Kara, D Alistarh, G Alonso, O Mutlu, C Zhang. FPGA-accelerated Dense Linear Machine Learning: A Precision-Convergence Trade-off. FCCM 2017.
  8. K Schawinski, C Zhang, H Zhang, L Fowler, GK Santhanam. Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limitMonthly Notices of the Royal Astronomical Society 2017.
  9. J Jiang, J Jiang, B Cui, C Zhang. TencentBoost: A Gradient Boosting Tree System with Parameter Server. ICDE (Industrial Track) 2017.
  10. X Li, B Cui, Y Chen, W Wu, C Zhang. MLog: Towards Declarative In-Database Machine Learning. VLDB (Demo) 2017.
  11. C Zhang, W Wu, T Li. An Overreaction to the Broken Machine Learning Abstraction: The Vision. HILDA 2017.
  1. H Huang, C Zheng, J Zeng, W Zhou, S Zhu, P Liu, S Chari, C Zhang. Android malware development on public malware scanning platforms: A large-scale data-driven study. IEEE Big Data 2016.