Abstract: An information processing apparatus acquires a placement probability that expresses a probability of each of n items, where n is a natural number of 2 or higher, being placed at k positions, where k is a natural number of 2 or higher, converts the n items into m embedding vectors, where m is a natural number of 2 or higher, that express abstract representations of features of the n items, calculates an assignment probability that expresses a probability of assignment from the n items to the m embedding vectors, and derives, using a distribution of the placement probability and a distribution of the assignment probability, a probability expression of each of the m embedding vectors being placed at each of the k positions.

Abstract: To enable graph partitioning using a graph convolutional neural network without preparing training data, an information processing apparatus is configured to classify a plurality of delivery destinations into a plurality of groups, and includes: a memory storing a program; and at least one processor that, by executing the program stored in the memory, is configured to: perform unsupervised learning to train a graph convolutional neural network, which is determined using an adjacency matrix indicating a connection relationship of the plurality of delivery destinations, and receives as input a feature matrix indicating a feature of the plurality of delivery destinations, the learning unit performing unsupervised learning using a first loss function defined such that the smaller a value for distance between delivery destinations belonging to a same group and the smaller a difference in features between delivery destinations belonging to a same group, the less a loss; and output information about a group to which

Abstract: A policy evaluation device includes one or more processors and one or more memories. The memory stores history data that includes datasets, the history data being a record obtained when a first policy was implemented. The processor is configured to generate multiple pieces of segmented data by segmenting the datasets based on at least one requirement related to the first policy remaining unchanged, generate a learning model related to a second policy for each of segments by training a machine learning algorithm using each of the pieces of segmented data, and perform off-policy evaluation for the second policy using an estimated value approximated from each of the pieces of segmented data.