Generalized Contextual Bandits With Latent Features: Algorithms and Applications.

Contextual bandit is a popular sequential decision-making framework to balance the exploration and exploitation tradeoff in many applications such as recommender systems, search engines, etc. Motivated by two important factors in real-world applications: 1) latent contexts (or features) often exist and 2) feedbacks often have humans in the loop leading to human biases, we formulate a generalized contextual bandit framework with latent contexts. Our proposed framework includes a two-layer probabilistic interpretable model for the feedbacks from human with latent features.

A Reinforcement Learning Approach to Price Cloud Resources With Provable Convergence Guarantees.

How to generate more revenues is crucial to cloud providers. Evidences from the Amazon cloud system indicate that "dynamic pricing" would be more profitable than "static pricing." The challenges are: How to set the price in real-time so to maximize revenues? How to estimate the price dependent demand so to optimize the pricing decision? We first design a discrete-time based dynamic pricing scheme and formulate a Markov decision process to characterize the evolving dynamics of the price-dependent demand.

Efficient spread-size approximation of opinion spreading in general social networks.

In contemporary society, understanding how information, such as trends and viruses, spreads in various social networks is an important topic in many areas. However, it is difficult to mathematically measure how widespread the information is, especially for a general network structure. There have been studies on opinion spreading, but many studies are limited to specific spreading models such as the susceptible-infected-recovered model and the independent cascade model, and it is difficult to apply these studies to various situations.

Publisher Correction: A Unified Framework for Complex Networks with Degree Trichotomy Based on Markov Chains.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A Unified Framework for Complex Networks with Degree Trichotomy Based on Markov Chains.

This paper establishes a Markov chain model as a unified framework for describing the evolution processes in complex networks. The unique feature of the proposed model is its capability in addressing the formation mechanism that can reflect the "trichotomy" observed in degree distributions, based on which closed-form solutions can be derived. Important special cases of the proposed unified framework are those classical models, including Poisson, Exponential, Power-law distributed networks.