Towards Feature-Based Analysis of the Machine Learning Development Lifecycle
The safety and trustworthiness of systems with components that are based on Machine Learning (ML) require an in-depth understanding and analysis of all stages in its Development Lifecycle (MLDL). High-level abstractions of desired functionalities, model behavior, and data are called \emph{features}, and they have been studied by different communities across all MLDL stages. In this paper, we propose to support Software Engineering analysis of the MLDL through features, calling it \emph{feature-based analysis of the MLDL}. First, to achieve a shared understanding of features among different experts, we establish a taxonomy of existing feature definitions currently used in various MLDL stages. Through this taxonomy, we map features from different stages to each other, discover gaps and future research directions and identify areas of collaboration between Software Engineering and other MLDL experts.
Tue 5 DecDisplayed time zone: Pacific Time (US & Canada) change
16:00 - 18:00 | Machine Learning IIResearch Papers / Ideas, Visions and Reflections at Golden Gate C2 Chair(s): Iftekhar Ahmed University of California at Irvine | ||
16:00 15mTalk | [Remote] Compatibility Issues in Deep Learning Systems: Problems and Opportunities Research Papers Jun Wang Nanjing University of Aeronautics and Astronautics, Nanjing, China, Guanping Xiao Nanjing University of Aeronautics and Astronautics, China, Shuai Zhang Nanjing University of Aeronautics and Astronautics, China, Huashan Lei Nanjing University of Aeronautics and Astronautics, China, Yepang Liu Southern University of Science and Technology, Yulei Sui University of New South Wales, Australia DOI Pre-print Media Attached | ||
16:15 15mTalk | [Remote] An Extensive Study on Adversarial Attack against Pre-trained Models of Code Research Papers Xiaohu Du Huazhong University of Science and Technology, Ming Wen Huazhong University of Science and Technology, Zichao Wei Huazhong University of Science and Technology, Shangwen Wang National University of Defense Technology, Hai Jin Huazhong University of Science and Technology Media Attached | ||
16:30 15mTalk | Can Machine Learning Pipelines Be Better Configured? Research Papers Yibo Wang Northeastern University, Ying Wang Northeastern University, Tingwei Zhang Northeastern University, Yue Yu National University of Defense Technology, Shing-Chi Cheung Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hai Yu Software College, Northeastern University, Zhiliang Zhu Software College, Northeastern University Media Attached | ||
16:45 15mTalk | Towards Feature-Based Analysis of the Machine Learning Development Lifecycle Ideas, Visions and Reflections Media Attached | ||
17:00 15mTalk | Fix Fairness, Don’t Ruin Accuracy: Performance Aware Fairness Repair using AutoML Research Papers Giang Nguyen Dept. of Computer Science, Iowa State University, Sumon Biswas Carnegie Mellon University, Hridesh Rajan Dept. of Computer Science, Iowa State University Pre-print Media Attached | ||
17:15 15mTalk | BiasAsker: Measuring the Bias in Conversational AI System Research Papers Yuxuan Wan The Chinese University of Hong Kong, Wenxuan Wang Chinese University of Hong Kong, Pinjia He The Chinese University of Hong Kong, Shenzhen, Jiazhen Gu Chinese University of Hong Kong, Haonan Bai The Chinese University of Hong Kong, Michael Lyu The Chinese University of Hong Kong Media Attached | ||
17:30 15mTalk | Pitfalls in Experiments with DNN4SE: An Analysis of the State of the Practice Research Papers Media Attached | ||
17:45 15mTalk | DecompoVision: Reliability Analysis of Machine Vision Components Through Decomposition and Reuse Research Papers Boyue Caroline Hu University of Toronto, Lina Marsso University of Toronto, Nikita Dvornik Waabi, Huakun Shen University of Toronto, Marsha Chechik University of Toronto Media Attached | ||