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Found 5 papers, 2 papers with code
Online Learning with Costly Features in Non-stationary Environments
Maximizing long-term rewards is the primary goal in sequential decision-making problems.
Non-stationary Delayed Combinatorial Semi-Bandit with Causally Related Rewards
We develop a policy that learns the structural dependencies from delayed feedback and utilizes that to optimize the decision-making while adapting to drifts.
Linear Combinatorial Semi-Bandit with Causally Related Rewards
The objective is to maximize the long-term average payoff, which is a linear function of the base arms' rewards and depends strongly on the network topology.
Bayesian Non-stationary Linear Bandits for Large-Scale Recommender Systems
Our proposed recommender system employs this policy to learn the users' item preferences online while minimizing runtime.
Multi-Armed Bandit for Energy-Efficient and Delay-Sensitive Edge Computing in Dynamic Networks with Uncertainty
The full potential of edge computing becomes realized only if a smart device selects the most appropriate server in terms of the latency and energy consumption, among many available ones.
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