We study the problem of identifying unreliable and adversarial workers in crowdsourcing systems where workers (or users) provide labels for tasks (or items). Most existing studies assume that worker responses follow specific probabilistic models; however, recent evidence shows the presence of workers adopting non-random or even malicious strategies. To account for such workers, we suppose that workers comprise a mixture of honest and adversarial workers. Honest workers may be reliable or unreliable, and they provide labels according to an unknown but explicit probabilistic model. Adversaries adopt labeling strategies different from those of honest workers, whether probabilistic or not. We propose two reputation algorithms to identify unreliable honest workers and adversarial workers from only their responses. Our algorithms assume that honest workers are in the majority, and they classify workers with outlier label patterns as adversaries. Theoretically, we show that our algorithms successfully identify unreliable honest workers, workers adopting deterministic strategies, and worst- case sophisticated adversaries who can adopt arbitrary labeling strategies to degrade the accuracy of the inferred task labels. Empirically, we show that filtering out outliers using our algorithms can significantly improve the accuracy of several state-of-the-art label aggregation algorithms in real-world crowdsourcing datasets.

Many socio-economic indicators are sensitive to real-world events. Proper characterization of the events can help to identify the relevant events that drive fluctuations in these indicators. In this paper, we propose a novel generative model of real-world events and employ it to extract events from a large corpus of news articles. We introduce the notion of an event class, which is an abstract grouping of similarly themed events. These event classes are manifested in news articles in the form of event triggers which are specific words that describe the actions or incidents reported in any article. We use the extracted events to predict fluctuations in different socio-economic indicators. Specifically, we focus on food prices and predict the price of 12 different crops based on real-world events that potentially influence food price volatility, such as transport strikes, festivals etc. Our experiments demonstrate that incorporating event information in the prediction tasks reduces the root mean square error (RMSE) of prediction by 22% compared to the standard ARIMA model. We also predict sudden increases in the food prices (i.e. spikes) using events as features, and achieve an average 5-10% increase in accuracy compared to baseline models, including an LDA topic-model based predictive model.

We consider the assortment optimization problem when customer preferences follow a mixture of Mallows distributions. The assortment optimization problem focuses on determining the revenue/profit maximizing subset of products from a large universe of products; it is an important decision that is commonly faced by retailers in determining what to offer their customers. There are two key challenges: (a) the Mallows distribution lacks a closed-form expression (and requires summing an exponential number of terms) to compute the choice probability and, hence, the expected revenue/profit per customer; and (b) finding the best subset may require an exhaustive search. Our key contributions are an efficiently computable closed-form expression for the choice probability under the Mallows model and a compact mixed integer linear program (MIP) formulation for the assortment problem.

Recommendation engines today suggest one product to another, e.g., an accessory to a product. However, intent to buy often precedes a user's appearance in a commerce vertical: someone interested in buying a skateboard may have earlier searched for {varial heelflip}, a trick performed on a skateboard. This paper considers how a search engine can provide early warning of commercial intent. The naive algorithm of counting how often an interest precedes a commercial query is not sufficient due to the number of related ways of expressing an interest. Thus, methods are needed for finding sets of queries where all pairs are related, what we call a query community, and this is the technical contribution of the paper. We describe a random model by which we obtain relationships between search queries and then prove general conditions under which we can reconstruct query communities. We propose two complementary approaches for inferring recommendations that utilize query communities in order to magnify the recommendation signal beyond what an individual query can provide. An extensive series of experiments on real search logs shows that the query communities found by our algorithm are more interesting and unexpected than a baseline of clustering the query-click graph. Also, whereas existing query suggestion algorithms are not designed for making commercial recommendations, we show that our algorithms do succeed in forecasting commercial intent. Query communities increase both the quantity and quality of recommendations.

We consider the problem of designing a fair scheduling algorithm for discrete-time constrained queuing networks. Each queue has dedicated exogenous packet arrivals. There are constraints on which queues can be served simultaneously. This model effectively describes important special instances like network switches, interference in wireless networks, bandwidth sharing for congestion control and traffic scheduling in road roundabouts. Fair scheduling is required because it provides isolation to different traffic flows; isolation makes the system more robust and enables providing quality of service. Existing work on fairness for constrained networks concentrates on flow based fairness. As a main result, we describe a notion of packet based fairness by establishing an analogy with the ranked election problem: packets are voters, schedules are candidates, and each packet ranks the schedules based on its priorities. We then obtain a scheduling algorithm that achieves the described notion of fairness by drawing upon the seminal work of Goodman and Markowitz (1952). This yields the familiar Maximum Weight (MW) style algorithm. As another important result, we prove that the algorithm obtained is throughput optimal. There is no reason a priori why this should be true, and the proof requires nontraditional methods.

We consider the problem of recovering a function over the space of permutations (or, the symmetric group) over$n$elements from given partial information; the partial information we consider is related to the group theoretic Fourier Transform of the function. This problem naturally arises in several settings such as ranked elections, multi-object tracking, ranking systems, and recommendation systems. Inspired by the work of Donoho and Stark in the context of discrete-time functions, we focus on non-negative functions with a sparse support (support size$\ll$domain size). Our recovery method is based on finding the sparsest solution (through$\ell_0$optimization) that is consistent with the available information. As the main result, we derive sufficient conditions for functions that can be recovered exactly from partial information through$\ell_0$optimization. Under a natural random model for the generation of functions, we quantify the recoverability conditions by deriving bounds on the sparsity (support size) for which the function satisfies the sufficient conditions with a high probability as$n \to \infty$.$\ell_0$optimization is computationally hard. Therefore, the popular compressive sensing literature considers solving the convex relaxation,$\ell_1$optimization, to find the sparsest solution. However, we show that$\ell_1$optimization fails to recover a function (even with constant sparsity) generated using the random model with a high probability as$n \to \infty$. In order to overcome this problem, we propose a novel iterative algorithm for the recovery of functions that satisfy the sufficient conditions. Finally, using an Information Theoretic framework, we study necessary conditions for exact recovery to be possible.

We visit the following fundamental problem: For a `generic model of consumer choice (namely, distributions over preference lists) and a limited amount of data on how consumers actually make decisions (such as marginal preference information), how may one predict revenues from offering a particular assortment of choices? This problem is central to areas within operations research, marketing and econometrics. We present a framework to answer such questions and design a number of tractable algorithms (from a data and computational standpoint) for the same.

In this paper, we consider the problem of designing a scheduling algorithm for input queued switches, that is both fair as well as throughput optimal. Most of the existing literature on input-queued switch fairness criteria concentrates on flow-based fairness. Since a large fraction of network traffic is about "short- flows" there is a need for packet-based fairness criterion. The significant body of literature developed over the past two decades for packet-based scheduling algorithms is primarily concerned with throughput and delay, but not fairness. One of the reasons for such a state of affairs is the lack of a proper definition for packet-based fairness. The difficulty in defining fair stems from the fact that any reasonable notion of fairness must combine the well-known notion of fairness for a single-queue with the scheduling constraint of an input queued switch in an appropriate manner. As one of the main results of this paper, we define a notion of packet-based fair scheduling by identifying it as the selection of a winner in the following ranked election: packets are voters; schedules are candidates and each packet ranks different schedules based on their priorities. Drawing upon the seminal work of Goodman and Markowitz (1952) on ranked elections, we obtain a unique characterization of the fair schedule. Another important contribution of this paper is proving that the thus obtained fair scheduling algorithm is throughput optimal. There is no a priori reason why this should be true, and we introduce some non-standard proof techniques to prove the result. Our results suggest a framework for defining fair scheduling algorithm for a constrained packet network; a nonstandard method to prove throughput stability for algorithms, such as ours, that are not based on queue-sizes.

Motivated by applications like elections, web-page ranking, revenue maximization etc., we consider the question of inferring popular rankings using constrained data. More specifically, we consider the problem of inferring a probability distribution over the group of permutations using its first order marginals. We first prove that it is not possible to recover more than O(n) permutations over n elements with the given information. We then provide a simple and novel algorithm that can recover up to O(n) permutations under a natural stochastic model; in this sense, the algorithm is optimal. In certain applications, the interest is in recovering only the most popular (or mode) ranking. As a second result, we provide an algorithm based on the Fourier Transform over the symmetric group to recover the mode under a natural majority condition; the algorithm turns out to be a maximum weight matching on an appropriately defined weighted bipartite graph. The questions considered are also thematically related to Fourier Transforms over the symmetric group and the currently popular topic of compressed sensing.

We consider the problem of designing an online scheduling scheme for a multi-hop wireless packet network with arbitrary topology and operating under arbitrary scheduling constraints. The objective is to design a scheme that achieves high throughput and low delay simultaneously. We propose a scheduling scheme that - for networks operating under primary interference constraints - guarantees a per-flow end-to-end packet delay bound of 5dj/(1-ρj), at a factor 5 loss of throughput, where dj is the path length (number of hops) of flow j and ρj is the effective loading along the route of flow j. Clearly, dj is a universal lower bound on end-to-end packet delay for flow j. Thus, our result is essentially optimal. To the best of our knowledge, our result is the first one to show that it is possible to achieve a per-flow end-to-end delay bound of O(# of hops) in a constrained network.

We consider the point-to-point link scheduling problem in Spatial Time Division Multiple Access (STDMA) wireless ad hoc networks, motivate the use of spatial reuse as performance metric and provide an explicit characterization of spatial reuse. We assume uniform transmission power at all nodes and propose an algorithm based on a graph model of the network as well as Signal to Interference and Noise Ratio (SINR) computations. Our algorithm achieves higher spatial reuse than existing algorithms, without compromising on computational complexity.