We study the online allocation problem under a roommate market model introduced in [Chan et al., 2016]. Consider a fixed supply of n rooms and a list of 2n applicants arriving online in random order. The problem is to assign a room to each person upon her arrival, such that after the algorithm terminates, each room is shared by exactly two people. We focus on two objectives: (1) maximizing the ...

We study the online transportation problem. We show that the halfopt-competitive ratio for the greedy algorithm is (min(m; logC)), where m is the number of server sites, and C is the total number of servers. We also present an algorithm Balance that is a modi cation of the greedy algorithm and that has a halfopt-competitive ratio of O(1).

The competitive analysis of online algorithms has been criticized as being too crude and unrealistic. We propose refinements of competitive analysis in two directions: The first restricts the power of the adversary by allowing only certain input distributions, while the other allows for comparisons between information regimes for online decision-making. We illustrate the first with an applicati...

Recommender systems — systems that suggest to users in e-commerce sites items that might interest them — adopt a static view of the recommendation process and treat it as a prediction problem. In an earlier paper, we argued that it is more appropriate to view the problem of generating recommendations as a sequential decision problem and, consequently, that Markov decision processes (MDPs) provi...

We study a demand response problem from operator’s perspective with realistic settings, in which the operator faces uncertainty and limited communication. Specifically, the operator does not know the cost function of consumers and cannot have multiple rounds of information exchange with consumers. We formulate an optimization problem for the operator to minimize its operational cost considering...

The issue of QoS (quality of service) provisioning for adaptive multimedia in wireless communication networks is considered. A reinforcement learning based online adaptive bandwidth allocation optimization algorithm is proposed. First, an event-driven stochastic switching model is introduced to formulate the adaptive bandwidth allocation problem as a constrained continuous-time Markov decision ...

this commentary discusses pertinent issues from hyosun kim’s paper on online prescription drug promotion. the study is well-designed and the findings highlight some of the consequences of the food and drug administration’s (fda’s) decision to deregulate online advertising of prescription drugs. while kim’s findings confirm some of the early concerns, they also provide a perspective of implement...

Online reinforcement learning (RL) is increasingly popular for the personalized mobile health (mHealth) intervention. It is able to personalize the type and dose of interventions according to user’s ongoing statuses and changing needs. However, at the beginning of online learning, there are usually too few samples to support the RL updating, which leads to poor performances. A delay in good per...

In any online decision support system, the backbone is a data warehouse. In order to facilitate rapid response to complex business decision support queries, it is a common practice to materialize an appropriate set of the views at the data warehouse. However, it typically requires the solution of the Materialized View Selection (MVS) problem to select the right set of views to materialize in or...