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In a linear stochastic bandit model, each arm corresponds to a vector in Euclidean space, and the expected return observed at each time step is determined by an unknown linear function of the selected arm. This paper addresses the challenge of identifying the optimal arm in a linear stochastic bandit model, where latent biases corrupt each arm's expected reward. Unlike traditional linear bandit problems, where the observed return directly represents the reward, this paper considers a scenario where the unbiased reward at each time step remains unobservable. This model is particularly relevant in situations where the observed return is influenced by latent biases that need to be carefully excluded from the learning model. For example, in recommendation systems designed to prevent racially discriminatory suggestions, it is crucial to ensure that the users' race does not influence the system. However, the observed return, such as click-through rates, may have already been influenced by racial attributes. In the case where there are finitely many arms, we develop a strategy to achieve O (| D | log ⁡ n) regret, where | D | is the number of arms and n is the number of time steps. In the case where each arm is chosen from an infinite compact set, our strategy achieves O (n 2 / 3 (log ⁡ n) 1 / 2) regret. Experiments verify the efficiency of our strategy. [ABSTRACT FROM AUTHOR]

• This paper is to find the truth Z of events in social sensing with information flows. • A truth discovery model is presented which considers the individuals' reliability R. • The model also considers the dependency D between individuals' observations. • An iterative expectation maximization is presented to infer both Z, R and D. • Extensive experiments demonstrate the effectiveness of the presented algorithm. Social sensing relies on a large number of observations reported by different, possibly unreliable, agents to determine if an event has occurred or not. In this paper, we consider the truth discovery problem in social sensing, in which an agent may receive another agent's observation (known as an information flow), and may change its observation to match the observation it receives. If an agent's observation is influenced by another agent, we say that the former is a dependent agent. We propose an Iterative Expectation Maximization algorithm for Truth Discovery (IEMTD) in social sensing with dependent agents. Compared with other popular truth discovery approaches, which assume either the agents' observations are independent, or their dependency is known a priori, IEMTD allows to infer each agent's reliability, the observations' dependency and the events' truth jointly. Simulation results on synthetic data and three real world data sets demonstrate that in almost all our experiments, IEMTD achieves a higher truth discovery accuracy than the existing algorithms when dependencies exist between agents' observations. [ABSTRACT FROM AUTHOR]