In recent years, many recommender systems using network embedding (NE) such as graph neural networks (GNNs) have been extensively studied in the sense of improving recommendation accuracy.
Approximate Policy Iteration (API) algorithms alternate between (approximate) policy evaluation and (approximate) greedification.
Federated learning has emerged as a new paradigm of collaborative machine learning; however, many prior studies have used global aggregation along a star topology without much consideration of the communication scalability or the diurnal property relied on clients' local time variety.
SSumM not only merges nodes together but also sparsifies the summary graph, and the two strategies are carefully balanced based on the minimum description length principle.
Databases Social and Information Networks H.2.8
Information gathering in a partially observable environment can be formulated as a reinforcement learning (RL), problem where the reward depends on the agent's uncertainty.
A common strategy has been to restrict the functional form of the action-values to be concave in the actions, to simplify the optimization.