Publications

Manuscripts
  1. T Li, J Zhong, J Liu, W Wu, C Zhang. Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads. arXiv:1708.07308.
  2. H Zhang, L Zeng, W Wu, C Zhang. mlbench: How Good Are Machine Learning Clouds for Binary Classification with Good Features? arXiv:1707.09562.
  3. H Guo, K Kara, C Zhang. Layerwise Systematic Scan: Deep Boltzmann Machines and BeyondarXiv:1705.05154.
2017
  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 ease.ml Vision. HILDA 2017.
2016
  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.