3 papers indexed
Yuta Natsubori, Masataka Ushiku, Yuta Saito
Off-Policy Evaluation and Learning (OPE/L) in contextual bandits is rapidly gaining popularity in real systems because new policies can be evaluated and learned securely using only historical logged data. However, existing methods in OPE/L cannot handle many challenging but preva…
Kento Koizumi, Tomoaki Ohba, Yuta Saito, Takeya Morito, Kenji Suzuki
This study proposes a reconfigurable rocker-bogie mechanism that achieves efficient turning motion with a small number of actuators while maintaining high step-climbing capability. By installing motors at the bogie joints and actively swinging up and down bogies, the system enabl…
Koki Konishi, Masataka Ushiku, Yuta Saito
A/B testing is the gold standard for selecting the better algorithm in online services. While offline evaluation has attracted attention as a safer alternative due to the high experimental costs and the potential risk of degrading user experience and revenue in A/B testing, it is…