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.

In other words, there is no perfect voting rule that one domains may admit efficient algorithms for social choice could always use, independently of the circumstances. However, problems that are hard for general preferences. This observation this result holds under the assumption that there are no has recently led to a new wave of interest in constraints on the voters' preferences. Thus, a common strategy restricted domains within the computational social choice to circumvent Arrow's theorem is to consider restricted community (Conitzer 2009; Walsh 2007; Faliszewski et al. preference domains, i.e., assume that the voters' preferences 2011; Brandt et al. 2010; Faliszewski, Hemaspaandra, and have additional structure. It may then be possible to show Hemaspaandra 2011; Betzler, Slinko, and Uhlmann 2013; that various negative consequences of Arrow's theorem do Cornaz, Galand, and Spanjaard 2012; 2013; Skowron et al. not hold.

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.

In the Shift Bribery problem, we are given an election (based on preference orders), a preferred candidate p, and a budget. The goal is to ensure that p wins by shifting p higher in some voters' preference orders. However, each such shift request comes at a price (depending on the voter and on the extent of the shift) and we must not exceed the given budget. We study the parameterized computational complexity of Shift Bribery with respect to a number of parameters (pertaining to the nature of the solution sought and the size of the election) and several classes of price functions. When we parameterize Shift Bribery by the number of affected voters, then for each of our voting rules (Borda, Maximin, Copeland) the problem is W[2]-hard. If, instead, we parameterize by the number of positions by which p is shifted in total, then the problem is fixed-parameter tractable for Borda and Maximin, and is W[1]-hard for Copeland. If we parameterize by the budget for the cost of shifting, then the results depend on the price function class. We also show that Shift Bribery tends to be tractable when parameterized by the number of voters, but that the results for the number of candidates are more enigmatic.

Scoring systems are an extremely important class of election systems. A length-m (so-called) scoring vector applies only to m-candidate elections. To handle general elections, one must use a family of vectors, one per length. The most elegant approach to making sure such families are "family-like'' is the recently introduced notion of (polynomial-time uniform) pure scoring rules, where each scoring vector is obtained from its precursor by adding one new coefficient. We obtain the first dichotomy theorem for pure scoring rules for a control problem. In particular, for constructive control by adding voters (CCAV), we show that CCAV is solvable in polynomial time for k-approval with k<=3, k-veto with k<=2, every pure scoring rule in which only the two top-rated candidates gain nonzero scores, and a particular rule that is a "hybrid" of 1-approval and 1-veto. For all other pure scoring rules, CCAV is NP-complete. We also investigate the descriptive richness of different models for defining pure scoring rules, proving how more rule-generation time gives more rules, proving that rationals give more rules than do the natural numbers, and proving that some restrictions previously thought to be "w.l.o.g." in fact do lose generality.

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.

We perform the first study of the tradeoff space of access methods and replication to support statistical analytics using first-order methods executed in the main memory of a Non-Uniform Memory Access (NUMA) machine. Statistical analytics systems differ from conventional SQL-analytics in the amount and types of memory incoherence they can tolerate. Our goal is to understand tradeoffs in accessing the data in row- or column-order and at what granularity one should share the model and data for a statistical task. We study this new tradeoff space, and discover there are tradeoffs between hardware and statistical efficiency. We argue that our tradeoff study may provide valuable information for designers of analytics engines: for each system we consider, our prototype engine can run at least one popular task at least 100x faster. We conduct our study across five architectures using popular models including SVMs, logistic regression, Gibbs sampling, and neural networks.