1 code implementation • 10 Jul 2023 • Xiaotong Ji, Antonio Filieri
We demonstrate our method's effectiveness in reducing safety violations during online exploration in preliminary experiments by an average of 40. 3% compared with QL and DQN standard algorithms and 29. 1% compared with previous related work, while achieving comparable cumulative rewards with respect to unrestricted exploration and alternative approaches.
no code implementations • 17 Mar 2022 • Xiaotong Ji, Yuchen Zheng, Daiki Suehiro, Seiichi Uchida
The highlights of LwR are: (1) the rejection strategy is not heuristic but has a strong background from a machine learning theory, and (2) the rejection function can be trained on an arbitrary feature space which is different from the feature space for classification.
no code implementations • 17 Mar 2022 • Xiaotong Ji, Yan Zheng, Daiki Suehiro, Seiichi Uchida
Signature verification, as a crucial practical documentation analysis task, has been continuously studied by researchers in machine learning and pattern recognition fields.