Machine learning for data systems:
  • K. Rong, Y. Lu, P. Bailis, S. Kandula, P. Levis. Approximate Partition Selection for Big-Data Workloads using Summary Statistics. International Conference on Very Large Data Bases (VLDB). Tokyo, Japan. 2020. [PDF]

Data systems for machine learning:
  • Y. Lu. Building and Accelerating a Declarative Platform for Machine Learning Model Serving. Doctoral Dissertation. University of Washington. 2018. [PDF]

  • Y. Lu, A. Chowdhery, S. Kandula, S. Chaudhuri. Accelerating Machine Learning Inference with Probabilistic Predicates. ACM International Conference on Management of Data (SIGMOD). Houston, USA. 2018. [PDF][slides][Talk video][Errata]

  • Y. Lu, S. Kandula, S. Chaudhuri. Interactive Demonstration of Probabilistic Predicates. ACM International Conference on Management of Data (SIGMOD) Demo. Houston, USA. 2018. [PDF][Code]. Best Demonstration Award.

  • P. Chunduri, J. Bang, Y. Lu, J. Arulraj. Zeus: Efficiently Localizing Actions in Videos using Reinforcement Learning. arXiv preprint 2021. arXiv:2104.06142. [PDF]

Video systems and algorithms:
  • H. Qiu, Y. Zheng, H. Ye, Y. Lu, F. Wang, L. He. Precise Temporal Action Localization by Evolving Temporal Proposals. ACM International Conference on Multimedia Retrieval (ICMR). Yokohama, Japan. 2018; arXiv preprint. arXiv:1804.04803. [PDF]

  • L. Wang, W. Shao, Y. Lu, H. Ye, J. Pu, Y. Zheng. Crowd Counting with Density Adaption Networks. arXiv preprint 2018. arXiv:1806:10040. [Pdf]

  • S. Lyu, et al. UA-DETRAC 2017: Report of AVSS2017 & IWT4S Challenge on Advanced Traffic Monitoring. IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). Lecce, Italy. 2017. [PDF]

  • Y. Peng, H. Ye, Y. Lin, Y. Bao, Z. Zhao, H. Qiu, Y. Lu, L. Wang, Y. Zheng. Large-Scale Video Classification with Elastic Streaming Sequential Data Processing System. ACM Multimedia Workshop on Large-Scale Video Classification Challenge (LSVC). Mountain View, USA. 2017. [PDF]

  • L. Wang, Y. Lu, H. Wang, Y. Zheng, H. Ye, X. Xue. Evolving Boxes for Fast Vehicle Detection. IEEE International Conference on Multimedia and Expo (ICME). HongKong, China. 2017; arXiv preprint. arXiv:1702.00254. [PDF][Code&Results]Platinum Best Paper Award.

  • Y. Lu, A. Chowdhery, S. Kandula. Optasia: A Relational Platform for Efficient Large-Scale Video Analytics. ACM Symposium on Cloud Computing (SoCC). Santa Clara, USA. 2016. [PDF][Proj][Demo][Slides].

  • Y. Lu, X. Bai, L. Shapiro, J. Wang. Coherent Parametric Contours for Interactive Video Object Segmentation. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, USA. 2016. [PDF][Proj]

Prior work in computer vision:
  • Y. Lu, L. Shapiro. Closing the Loop for Object Proposals and Edge Detection. The Thirty-First AAAI Conference on Artificial Intelligence. (AAAI). San Fransisco, USA. 2017. [PDF][Slides]

  • Y. Lu, W. Zhang, K. Zhang, X. Xue. Semantic Context Learning with Large-Scale Weakly-Labeled Image Set. ACM Conference on Information and Knowledge Management (CIKM). Hawaii, USA, 2012. [PDF][Proj]

  • Y. Lu, W. Zhang, C. Jin, X. Xue. Learning Attention Map from Images. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). Providence, USA. 2012. [PDF][Proj]

  • W. Zhang, Y. Lu, X. Xue, J. Fan. Automatic Image Annotation with Weakly Labeled Datasets. ACM Multimedia. Scottsdale, USA. 2011.[PDF][Proj]

  • X. Xue, W. Zhang, J. Zhang, B. Wu, J. Fan, Y. Lu. Correlative Multi-Label Multi-Instance Image Annotation. 13th International Conference on Computer Vision (ICCV). Barcelona, Spain. 2011. [PDF]

  • Y. Lu, W. Zhang, H. Lu, X. Xue. Salient Object Detection using Concavity Context. 13th IEEE International Conference on Computer Vision (ICCV). Barcelona, Spain. 2011.[PDF][Proj]