In signal analysis and synthesis, linear approximation theory considers a linear decomposition of any given signal in a set of atoms, collected into a so-called dictionary. Relevant sparse representations are obtained by relaxing the orthogonality condition of the atoms, yielding overcomplete dictionaries with an extended number of atoms. More generally than the linear decomposition, overcomplete kernel dictionaries provide an elegant nonlinear extension by defining the atoms through a mapping kernel function (e.g., the gaussian kernel). Models based on such kernel dictionaries are used in neural networks, gaussian processes and online learning with kernels. The quality of an overcomplete dictionary is evaluated with a diversity measure the distance, the approximation, the coherence and the Babel measures. In this paper, we develop a framework to examine overcomplete kernel dictionaries with the entropy from information theory. Indeed, a higher value of the entropy is associated to a further uniform spread of the atoms over the space. For each of the aforementioned diversity measures, we derive lower bounds on the entropy. Several definitions of the entropy are examined, with an extensive analysis in both the input space and the mapped feature space.

A contract algorithm is an algorithm which is given, as part of the input, a specified amount of allowable computation time. The algorithm must then complete its execution within the allotted time. An interruptible algorithm, in contrast, can be interrupted at an arbitrary point in time, at which point it must report its currently best solution. It is known that contract algorithms can simulate interruptible algorithms using iterative deepening techniques. This simulation is done at a penalty in the performance of the solution, as measured by the so-called acceleration ratio. In this paper we give matching (i.e., optimal) upper and lower bounds for the acceleration ratio under such a simulation. We assume the most general setting in which n problem instances must be solved by means of scheduling executions of contract algorithms in $m$ identical parallel processors. This resolves an open conjecture of Bernstein, Filkenstein, and Zilberstein who gave an optimal schedule under the restricted setting of round robin and length-increasing schedules, but whose optimality in the general unrestricted case remained open. Lastly, we show how to evaluate the average acceleration ratio of the class of exponential strategies in the setting of n problem instances and m parallel processors. This is a broad class of schedules that tend to be either optimal or near-optimal, for several variants of the basic problem.

One of the most popular uses of crowdsourcing is to provide training data for supervised machine learning algorithms. Since human annotators often make errors, requesters commonly ask multiple workers to label each example.  But is this strategy always the most cost effective use of crowdsourced workers? We argue "No" --- often classifiers can achieve higher accuracies when trained with noisy "unilabeled" data. However, in some cases relabeling is extremely important.  We discuss three factors that may make relabeling an effective strategy: classifier expressiveness, worker accuracy, and budget.

We present theoretical and empirical results demonstrating the usefulness of social choice functions in crowdsourcing for participatory democracies. First, we demonstrate the scalability of social choice functions by defining a natural notion of epsilon-approximation, and giving algorithms which efficiently elicit such approximations for two prominent social choice functions: the Borda rule and the Condorcet winner. This result circumvents previous prohibitive lower bounds and is surprisingly strong: even if the number of ideas is as large as the number of participants, each participant will only have to make a logarithmic number of comparisons, an exponential improvement over the linear number of comparisons previously needed. Second, we apply these ideas to Finland's recent off-road traffic law reform, an experiment on participatory democracy in real life. This allows us to verify the scaling predicted in our theory and show that the constant involved is also not large. In addition, by collecting data on the time that users take to complete rankings of varying sizes, we observe that eliciting partial rankings can further decrease elicitation time as compared to the common method of eliciting pairwise comparisons.

We investigate the problem of large-scale mobile crowd-tasking, where a large pool of citizen crowd-workers are used to perform a variety of location-specific urban logistics tasks. Current approaches to such mobile crowd-tasking are very decentralized: a crowd-tasking platform usually provides each worker a set of available tasks close to the worker's current location; each worker then independently chooses which tasks she wants to accept and perform. In contrast, we propose TRACCS, a more coordinated task assignment approach, where the crowd-tasking platform assigns a sequence of tasks to each worker, taking into account their expected location trajectory over a wider time horizon, as opposed to just instantaneous location. We formulate such task assignment as an optimization problem, that seeks to maximize the total payoff from all assigned tasks, subject to a maximum bound on the detour (from the expected path) that a worker will experience to complete her assigned tasks. We develop credible computationally-efficient heuristics to address this optimization problem (whose exact solution requires solving a complex integer linear program), and show, via simulations with realistic topologies and commuting patterns, that a specific heuristic (called Greedy-ILS) increases the fraction of assigned tasks by more than 20%, and reduces the average detour overhead by more than 60%, compared to the current decentralized approach.

In today's age of big data, websites are collecting an increasingly wide variety of information about their users. The texts of websites' privacy policies, which serve as legal agreements between service providers and users, are often long and difficult to understand. Automated analysis of those texts has the potential to help users better understand the implications of agreeing to such policies. In this work, we present a technique that combines machine learning and crowdsourcing to semi-automatically extract key aspects of website privacy policies that is scalable, fast, and cost-effective.

The past decade has witnessed the rapid emergence of the We consider a central task in combinatorial materials discovery, field of human computation, along with numerous successful namely the problem of identifying the crystalline applications. Human computation is motivated by problems phases of inorganic compounds based on an analysis of for which automated algorithms cannot yet exceed high-intensity X-ray patterns. In our approach, we integrate human performance (Von Ahn 2005). Indeed, some tasks a state-of-the-art optimization framework based on are naturally and truly easy for humans, while they remain constraint reasoning with a human computation component.

In online labor platforms such as Amazon Mechanical Turk, a good strategy to obtain quality answers is to take aggregate answers submitted by multiple workers, exploiting the wisdom of the crowd. However, human computation is susceptible to systematic biases which cannot be corrected by using multiple workers. We investigate a game-theoretic bonus scheme, called Peer Truth Serum (PTS), to overcome this problem. We report on the design and outcomes of a set of experiments to validate this scheme. Results show Peer Truth Serum can indeed correct the biases and increase the answer accuracy by up to 80%.

Lingo is an emerging language on the Internet. Providing a standardized definition remains difficult due to continuous changes made to its nature. We proposed Tranzzl!n9o, a crossword puzzle game for engaging crowds to translate Internet lingo. Players provide explanations for lingo in parallel and iteratively verify the explanations from other players. Crowd-sourced translations are very informative containing explanations as well as lingo usage.

Human computation applications that involve planning and scheduling are gaining popularity, and the existing literature on such systems shows that any automated oversight on human contributors improves the effectiveness of the crowd. In this paper, we present our ongoing work on the AI-MIX system, which is a first step towards using an automated planning and scheduling system in a crowdsourced planning application. In order to address the mismatch between the capabilities of the crowd and the automated planner, we identify two major challenges -- interpretation, and steering. We also present preliminary empirical results over the tour planning domain, and show how using an automated planner can help improve the quality of plans.