Gaussian Process Classification Bandits

no code implementations • 26 Dec 2022 • Tatsuya Hayashi, Naoki Ito, Koji Tabata, Atsuyoshi Nakamura, Katsumasa Fujita, Yoshinori Harada, Tamiki Komatsuzaki

Classification bandits are multi-armed bandit problems whose task is to classify a given set of arms into either positive or negative class depending on whether the rate of the arms with the expected reward of at least h is not less than w for given thresholds h and w. We study a special classification bandit problem in which arms correspond to points x in d-dimensional real space with expected rewards f(x) which are generated according to a Gaussian process prior.

Active Learning Classification

Non-learning Stereo-aided Depth Completion under Mis-projection via Selective Stereo Matching

no code implementations • 4 Oct 2022 • Yasuhiro Yao, Ryoichi Ishikawa, Shingo Ando, Kana Kurata, Naoki Ito, Jun Shimamura, Takeshi Oishi

Moreover, under various LiDAR-camera calibration errors, the proposed method reduced the depth estimation MAE to 0. 34-0. 93 times from previous depth completion methods.

Camera Calibration Depth Completion +2