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Statistical inference for pairwise comparison models
Pairwise comparison models have been widely used for utility evaluation and ranking across various fields.
Probabilistic methods for approximate archetypal analysis
Archetypal analysis is an unsupervised learning method for exploratory data analysis.
A General Pairwise Comparison Model for Extremely Sparse Networks
Under this setup, we show that the maximum likelihood estimator for the latent score vector of the subjects is uniformly consistent under a near-minimal condition on network sparsity.
Curiosity-Driven Recommendation Strategy for Adaptive Learning via Deep Reinforcement Learning
The design of recommendations strategies in the adaptive learning system focuses on utilizing currently available information to provide individual-specific learning instructions for learners.
