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cs.LG2022
Regret Analysis for Hierarchical Experts Bandit Problem
Qihan Guo, Siwei Wang, Jun Zhu
We study an extension of standard bandit problem in which there are R layers of experts. Multi-layered experts make selections layer by layer and only the experts in the last layer…
cs.LG2022★ 1 cited
Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Data
Wenkai Li, Cheng Feng, Ting Chen +1
Time series anomaly detection (TSAD) is an important data mining task with numerous applications in the IoT era. In recent years, a large number of deep neural network-based method…
cs.LG2022★ 1 cited
DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization
Wentse Chen, Shiyu Huang, Yuan Chiang +4
Most reinforcement learning algorithms seek a single optimal strategy that solves a given task. However, it can often be valuable to learn a diverse set of solutions, for instance,…