An Online Minimax Optimal Algorithm for Adversarial Multiarmed Bandit Problem
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2018.2806006
Abstract: We investigate the adversarial multiarmed bandit problem and introduce an online algorithm that asymptotically achieves the performance of the best switching bandit arm selection strategy. Our algorithms are truly online such that we do not… read more here.
Keywords: bandit; adversarial multiarmed; bandit arm; minimax optimal ... See more keywords
Minimax Optimal Bandits for Heavy Tail Rewards.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3203035
Abstract: Stochastic multiarmed bandits (stochastic MABs) are a problem of sequential decision-making with noisy rewards, where an agent sequentially chooses actions under unknown reward distributions to minimize cumulative regret. The majority of prior works on stochastic… read more here.
Keywords: regret; proposed methods; minimax optimal; exploration ... See more keywords
Online Anomaly Detection With Minimax Optimal Density Estimation in Nonstationary Environments
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2017.2784390
Abstract: We introduce a truly online anomaly detection algorithm that sequentially processes data to detect anomalies in time series. In anomaly detection, while the anomalous data are arbitrary, the normal data have similarities and generally conforms… read more here.
Keywords: density estimation; anomaly detection; detection; minimax optimal ... See more keywords