We consider voting situations in which some candidates may turn out to be unavailable. When determining availability is costly (e.g., in terms of money, time, or computation), voting prior to determining candidate availability and testing the winner's availability after the vote may be beneficial. However, since few voting rules are robust to candidate deletion, winner determination requires a number of such availability tests. We outline a model for analyzing such problems, defining robust winners relative to potential candidate unavailability. We assess the complexity of computing robust winners for several voting rules. Assuming a distribution over availability, and costs for availability tests/queries, we describe algorithms for computing optimal query policies, which minimize the expected cost of determining true winners.

Gambles in casinos are usually set up so that the casino makes a profit in expectation -- as long as gamblers play honestly. However, some gamblers are able to cheat, reducing the casino’s profit. How should the casino address this? A common strategy is to selectively kick gamblers out, possibly even without being sure that they were cheating. In this paper, we address the following question: Based solely on a gambler’s track record,when is it optimal for the casino to kick the gambler out? Because cheaters will adapt to the casino’s policy, this is a game-theoretic question. Specifically, we model the problem as a Bayesian game in which the casino is a Stackelberg leader that can commit to a (possibly randomized) policy for when to kick gamblers out, and we provide efficient algorithms for computing the optimal policy. Besides being potentially useful to casinos, we imagine that similar techniques could be useful for addressing related problems -- for example, illegal trades in financial markets.

We investigate synergy, or lack thereof, between agents in cooperative games, building on the popular notion of Shapley value. We think of a pair of agents as synergistic (resp., antagonistic) if the Shapley value of one agent when the other agent participates in a joint effort is higher (resp. lower) than when the other agent does not participate. Our main theoretical result is that any graph specifying synergistic and antagonistic pairs can arise even from a restricted class of cooperative games. We also study the computational complexity of determining whether a given pair of agents is synergistic. Finally, we use the concepts developed in the paper to uncover the structure of synergies in two real-world organizations, the European Union and the International Monetary Fund.

Incomplete preferences are likely to arise in real-world preference aggregation and voting systems. This paper deals with determining whether an incomplete preference profile is single-peaked. This is essential information since many intractable voting problems become tractable for single-peaked profiles. We prove that for incomplete profiles the problem of determining single-peakedness is NP-complete. Despite this computational hardness result, we find four polynomial-time algorithms for reasonably restricted settings.

Positional scoring rules in voting compute the score of an alternative by summing the scores for the alternative induced by every vote. This summation principle ensures that all votes contribute equally to the score of an alternative. We relax this assumption and, instead, aggregate scores by taking into account the rank of a score in the ordered list of scores obtained from the votes. This defines a new family of voting rules, rank-dependent scoring rules (RDSRs), based on ordered weighted average (OWA) operators, which, include all scoring rules, and many others, most of which of new. We study some properties of these rules, and show, empirically, that certain RDSRs are less manipulable than Borda voting, across a variety of statistical cultures.

Examples range from multiagent planning, That is, we look for the most representative voter and return to crowdsourcing and human computation, to collaborative her ballot as the outcome. In our example, a natural choice filtering for recommender systems, to rank aggregation would be any of the voters voting (0, 1, 1). The distance of for search engines, to coordination and resource allocation this choice to the individual ballots is 42 (21 voters disagree in multiagent systems. Several frameworks have been on 2 issues each), i.e., this solution is only marginally worse proposed in the literature on computational social choice than the solution returned by the distance-based rule--and it (Chevaleyre et al. 2007; Brandt, Conitzer, and Endriss 2013) is optimal in case (1, 1, 1) is infeasible.

Newly-discovered materials have been central to recent technological advances. They have contributed significantly to breakthroughs in electronics, renewable energy and green buildings, and overall, have promoted the advancement of global human welfare. Yet, only a fraction of all possible materials have been explored. Accelerating the pace of discovery of materials would foster technological innovations, and would potentially address pressing issues in sustainability, such as energy production or consumption. The bottleneck of this discovery cycle lies, however, in the analysis of the materials data. As materials scientists have recently devised techniques to efficiently create thousands of materials and experimentalists have developed new methods and tools to characterize these materials, the limiting factor has become the data analysis itself. Hence, the goal of this paper is to stimulate the development of new computational techniques for the analysis of materials data, by bringing together the complimentary expertise of materials scientists and computer scientists. In collaboration with two major research laboratories in materials science, we provide the first publicly available dataset for the phase map identification problem. In addition, we provide a parameterized synthetic data generator to assess the quality of proposed approaches, as well as tools for data visualization and solution evaluation.

The problem of low-rank matrix completion has recently generated a lot of interest leading to several results that offer exact solutions to the problem. However, in order to do so, these methods make assumptions that can be quite restrictive in practice. More specifically, the methods assume that: a) the observed indices are sampled uniformly at random, and b) for every new matrix, the observed indices are sampled afresh. In this work, we address these issues by providing a universal recovery guarantee for matrix completion that works for a variety of sampling schemes. In particular, we show that if the set of sampled indices come from the edges of a bipartite graph with large spectral gap (i.e. gap between the first and the second singular value), then the nuclear norm minimization based method exactly recovers all low-rank matrices that satisfy certain incoherence properties. Moreover, we also show that under certain stricter incoherence conditions, $O(nr^2)$ uniformly sampled entries are enough to recover any rank-$r$ $n\times n$ matrix, in contrast to the $O(nr\log n)$ sample complexity required by other matrix completion algorithms as well as existing analyses of the nuclear norm method.

We introduce a new sequential Monte Carlo algorithm we call the particle cascade . The particle cascade is an asynchronous, anytime alternative to traditional particle filtering algorithms. It uses no barrier synchronizations which leads to improved particle throughput and memory efficiency. It is an anytime algorithm in the sense that it can be run forever to emit an unbounded number of particles while keeping within a fixed memory budget. We prove that the particle cascade is an unbiased marginal likelihood estimator which means that it can be straightforwardly plugged into existing pseudomarginal methods.

We develop a technique for generalising from data in which models are samplers represented as program text. We establish encouraging empirical results that suggest that Markov chain Monte Carlo probabilistic programming inference techniques coupled with higher-order probabilistic programming languages are now sufficiently powerful to enable successful inference of this kind in nontrivial domains. We also introduce a new notion of probabilistic program compilation and show how the same machinery might be used in the future to compile probabilistic programs for efficient reusable predictive inference.