Efficiently Monitoring Small Data Modification Effect for Large-Scale Learning in Changing Environment

Authors

  • Hiroyuki Hanada Nagoya Institute of Technology
  • Atsushi Shibagaki Nagoya Institute of Technology
  • Jun Sakuma University of Tsukuba; Core Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency; Center for Advanced Intelligence Project, RIKEN
  • Ichiro Takeuchi Nagoya Institute of Technology; Center for Advanced Intelligence Project, RIKEN; Center for Materials Research by Information Integration, National Institute for Materials Science

Abstract

We study large-scale machine learning problems in changing environments where a small part of the dataset is modified, and the effect of the data modification must be monitored in order to know how much the modification changes the optimal model. When the entire dataset is large, even if the amount of the data modification is fairly small, the computational cost for re-training the model would be prohibitively large. In this paper, we propose a novel method, called the optimal solution bounding (OSB), for monitoring such a data modification effect on the optimal model by efficiently evaluating (without actually re-training) it. The proposed method provides bounds on the unknown optimal model with the cost proportional only to the size of the data modification.

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Published

2018-04-25

How to Cite

Hanada, H., Shibagaki, A., Sakuma, J., & Takeuchi, I. (2018). Efficiently Monitoring Small Data Modification Effect for Large-Scale Learning in Changing Environment. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11516

Issue

Section

AAAI Technical Track: Heuristic Search and Optimization