Considering two decision-making tasks $ A $ and $ B $, each of which wishes to compute
an effective decision $ Y $ for a given query $ X $, can we solve task $ B $ by using query …

Influence maximization (IM) aims to find a set of seed nodes in a social network that
maximizes the influence spread. While most IM problems focus on classical influence …

Modeling the spread of social contagions is central to various applications in social
computing. In this paper, we study the learnability of the competitive threshold model from a …

This paper discusses a reinforcement learning interpretation of A Marketing Game, a model
of socially-contingent decision-making that includes marketing by companies, which …

Motivated by online reputation systems, we investigate social learning in a network where
agents interact on a time dependent graph to estimate an underlying state of nature. Agents …

This paper considers models and algorithms for interactive sensing in social networks in
which individuals act as sensors and the information exchange between individuals is …

Given a combinatorial optimization problem taking an input, can we learn a strategy to solve
it from the examples of input-solution pairs without knowing its objective function? In this …

Sequential decision making techniques hold great promise to improve the performance of
many real-world systems, but computational complexity hampers their principled application …

Many cybersecurity problems that require real-time decision-making based on temporal
observations can be abstracted as a sequence modeling problem, eg, network intrusion …

This article investigates the cost function learning in social information networks, wherein
human memory and cognitive bias are explicitly taken into account. We first propose a …