KDD 2020- Learning by Exploration-Part 2

KDD 2020- Learning by Exploration-Part 2

Explore the second part of a conference talk from KDD 2020 focusing on learning by exploration. Delve into advanced concepts such as graph Laplacian regularization, social influence modeling, adaptive user clustering, and Particle Thompson Sampling. Examine techniques for addressing cold start challenges, encoding user dependencies, and leveraging historical data for model warm-starting. Investigate context-dependent clustering methods, probabilistic matrix factorization, and online Bayesian parameter estimation. Consider the problem-related regret lower bounds and evaluate the effectiveness of current algorithms in utilizing problem structure information.