Control scheduling refers to the problem of assigning agents or actuators act upon a dynamical system at specific times so as minimize quadratic control cost, such objectives linear-quadratic-Gaussian (LQG) linear regulator problems. When budget operational constraints are imposed on schedule, this is in general NP-hard and its solution can therefore only be approximated even for moderately siz...

Online mechanism design (OMD) addresses the problem of sequential decision making in a stochastic environment with multiple self-interested agents. The goal in OMD is to make value-maximizing decisions despite this self-interest. In previous work we presented a Markov decision process (MDP)-based approach to OMD in large-scale problem domains. In practice the underlying MDP needed to solve OMD ...

We study fairness in machine learning. A learning algorithm, given a training set drawn from an underlying population, learns a classifier that will be used to make decisions about individuals. The concern is that this classifier’s decisions might be discriminatory, favoring certain subpopulations over others. The seminal work of Dwork et al. [ITCS 2012] introduced fairness through awareness, p...

In general, constructing a locally-optimal structure is a little harder than constructing an arbitrary structure, but significantly easier than constructing a globally-optimal structure. A similar situation arises in listing. In counting, most problems are #P-complete, but in approximate counting we observe an interesting reversal of the pattern. Assuming that #BIS is not equivalent to #SAT und...