“Expert advice problem with noisy low rank loss”
September 7, 2022 | Time 13:00 CET
We consider the expert advice problem with a low rank but noisy loss sequence, where the loss vector on each round is composed by the low rank part and the noisy part. This is a generalization of the works of Hazan et al. (2016) and Barman et al. (2018), where the former one only treats noiseless loss and the latter one assumes that the low rank structure is known in advance. We propose an algorithm, where during the learning process we can re-construct the kernel of the low rank part under the assumptions, that the low rank loss is noised and there is no prior information about low rank structure. With this kernel, we obtain a satisfying regret bound. Moreover, even if in experiment, the proposed algorithm performs better than Hazan’s algorithm and the Hedge algorithm.
LIU, Yaxiong is currently a postdoctoral researcher in Computational Learning Team (leading by Prof. Hatano) AIP RIKEN. He received Dr. Sci. Degree from Kyushu University in 2022. His research interests include online learning and its applications to privacy etc.