Minimizing Queue Length Regret Under Adversarial Network Models

Bmap/g/1 Queue under D-policy: Queue Length Analysis

Minimizing Regret with Multiple

We study the problem of computing and learning non-anonymous reserve prices to maximize revenue. We first define the MAXIMIZING MULTIPLE RESERVES (MMR) problem in single-parameter matroid environments, where the input is m valuation profiles v1, . . . ,vm, indexed by the same n bidders, and the goal is to compute the vector r of (non-anonymous) reserve prices that maximizes the total revenue ob...

Network Utility Maximization in Adversarial Environments

Stochastic models have been dominant in network optimization theory for over two decades, due to their analytical tractability. However, these models fail to capture non-stationary or even adversarial network dynamics which are of increasing importance for modeling the behavior of networks under malicious attacks or characterizing short-term transient behavior. In this paper, we consider the ne...

Regret-Minimizing Representative Databases

We propose the k-representative regret minimization query (k-regret) as an operation to support multi-criteria decision making. Like top-k, the k-regret query assumes that users have some utility or scoring functions; however, it never asks the users to provide such functions. Like skyline, it filters out a set of interesting points from a potentially large database based on the users’ criteria...

Poster: Regret Minimizing Audits

Audits complement access control and are essential for enforcing privacy and security policies in many situations. The importance of audit as an a posteriori enforcement mechanism has been recognized in the computer security literature. For example, Lampson [1] takes the position that audit logs that record relevant evidence during system execution can be used to detect violations of policy, es...