We propose a new framework for single-channel source separation that liesbetween the fully supervised and unsupervised setting. Instead of supervision,we provide input features for each source signal and use convex methods toestimate the correlations between these features and the unobserved signaldecomposition. Contextually supervised source separation is a natural fit fordomains with large amounts of data but no explicit supervision; our motivatingapplication is energy disaggregation of hourly smart meter data (the separationof whole-home power signals into different energy uses). Here contextualsupervision allows us to provide itemized energy usage for thousands homes, a taskpreviously impossible due to the need for specialized data collection hardware.On smaller datasets which include labels, we demonstrate that contextualsupervision improves significantly over a reasonable baseline and existingunsupervised methods for source separation. Finally, we analyze the case of$\ell_2$ loss theoretically and show that recovery of the signal componentsdepends only on cross-correlation between features for different signals, not oncorrelations between features for the same signal.

Postoperative atrial fibrillation (PAF) occurs in 10% to 65% of the patients undergoing cardiothoracic surgery. It is associated with increased post-surgical mortality and morbidity, and results in longer and more expensive hospital stays. Accurately stratifying patients for PAF allows for selective use of prophylactic therapies (e.g., amiodarone). Unfortunately, existing tools to stratify patients for PAF fail to provide clinically adequate discrimination. Our research addresses this situation through the development of novel electrocardiographic(ECG) markers to identify patients at risk of PAF. As a first step, we explore an eigen-decomposition approach that partitions ECG signals into atrial and ventricular components by exploiting knowledge of the underlying cardiac cycle. We then quantify electrical instability in the myocardium manifesting as probabilistic variations in atrial ECG morphology to assess therisk of PAF. When evaluated on 385 patients undergoing cardiac surgery, this approach of stratifying patients for PAF through an analysis of morphologic variability within decoupled atrial ECG demonstrated substantial promise and improved net reclassification by over 53% relative to the use of baseline clinical characteristics.

Stackelberg security games (SSGs) have been deployed in a number of real-world domains. One key challenge in these applications is the assessment of attacker payoffs, which may not be perfectly known. Previous work has studied SSGs with uncertain payoffs modeled by interval uncertainty and provided maximin-based robust solutions. In contrast, in this work we propose the use of the less conservative minimax regret decision criterion for such payoff-uncertain SSGs and present the first algorithms for computing minimax regret for SSGs. We also address the challenge of preference elicitation, using minimax regret to develop the first elicitation strategies for SSGs. Experimental results validate the effectiveness of our approaches.

We introduce the simultaneous model for cake cutting (the fair allocation of a divisible good), in which agents simultaneously send messages containing a sketch of their preferences over the cake. We show that this model enables the computation of divisions that satisfy proportionality -- a popular fairness notion -- using a protocol that circumvents a standard lower bound via parallel information elicitation. Cake divisions satisfying another prominent fairness notion, envy-freeness, are impossible to compute in the simultaneous model, but admit arbitrarily good approximations.

The probabilistic serial rule is one of the most well-established and desirable rules for the random assignment problem. We present the egalitarian simultaneous reservation social decision scheme – an extension of probabilistic serial to the more general setting of randomized social choice. We consider various desirable fairness, efficiency, and strategic properties of social decision schemes and show that egalitarian simultaneous reservation compares favorably against existing rules. Finally, we define a more general class of social decision schemes called simultaneous reservation, that contains egalitarian simultaneous reservation as well as the serial dictatorship rules. We show that outcomes of simultaneous reservation characterize efficiency with respect to a natural refinement of stochastic dominance.

Researchers have introduced the Dynamic Distributed Constraint Optimization Problem (Dynamic DCOP) formulation to model dynamically changing multi-agent coordination problems, where a dynamic DCOP is a sequence of (static canonical) DCOPs, each partially different from the DCOP preceding it. Existing work typically assumes that the problem in each time step is decoupled from the problems in other time steps, which might not hold in some applications. Therefore, in this paper, we make the following contributions: (i) We introduce a new model, called Markovian Dynamic DCOPs (MD-DCOPs), where the DCOP in the next time step is a function of the value assignments in the current time step; (ii) We introduce two distributed reinforcement learning algorithms, the Distributed RVI Q-learning algorithm and the Distributed R-learning algorithm, that balance exploration and exploitation to solve MD-DCOPs in an online manner; and (iii) We empirically evaluate them against an existing multi-arm bandit DCOP algorithm on dynamic DCOPs.

Recent advances in Programming by Example (PBE) have supported new applications to text editing, but existing approaches are limited to simple text strings. In this paper we address transformations in richly formatted documents, using an approach based on the idea of least general generalizations from inductive inference, which avoids the scalability issues faced by state-of-the-art PBE methods. We describe a novel domain specific language (DSL) that expresses transformations over XML structures describing richly formatted content, and a synthesis algorithm that generates a minimal program with respect to a natural subsumption ordering in our DSL. We present experimental results on tasks collected from online help forums, showing an average of 4.17 examples required for task completion.

"Fraudulent support phones" refers to the misleading telephone numbers placed on Web pages or other media that claim to provide services with which they are not associated. Most fraudulent support phone information is found on search engine result pages (SERPs), and such information substantially degrades the search engine user experience. In this paper, we propose an approach to identify fraudulent support telephone numbers on the Web based on the co-occurrence relations between telephone numbers that appear on SERPs. We start from a small set of seed official support phone numbers and seed fraudulent numbers. Then, we construct a co-occurrence graph according to the co-occurrence relationships of the telephone numbers that appear on Web pages. Additionally, we take the page layout information into consideration on the assumption that telephone numbers that appear in nearby page blocks should be regarded as more closely related. Finally, we develop a propagation algorithm to diffuse the trust scores of seed official support phone numbers and the distrust scores of the seed fraudulent numbers on the co-occurrence graph to detect additional fraudulent numbers. Experimental results based on over 1.5 million SERPs produced by a popular Chinese commercial search engine indicate that our approach outperforms TrustRank, Anti-TrustRank and Good-Bad Rank algorithms by achieving an AUC value of over 0.90.

Social influence has been widely accepted to explain people's cascade behaviors and further utilized in many related applications. However, few of existing work studied the direct, microscopic and temporal impact of social influence on people's behaviors in detail. In this paper we concentrate on the behavior modeling and systematically formulate the family of behavior propagation models (BPMs) including the static models (BP and IBP), and their discrete temporal variants (DBP and DIBP). To address the temporal dynamics of behavior propagation over continuous time, we propose a continuous temporal interest-aware behavior propagation model, called CIBP. As a new member of the BPM family, CIBP exploits the continuous-temporal functions (CTFs) to model the fully-continuous dynamic variance of social influence over time. Experiments on real-world datasets evaluated the family of BPMs and demonstrated the effectiveness of our proposed approach.

The explosive use of social media also makes it a popular platform for malicious users, known as social spammers, to overwhelm normal users with unwanted content. One effective way for social spammer detection is to build a classifier based on content and social network information. However, social spammers are sophisticated and adaptable to game the system with fast evolving content and network patterns. First, social spammers continually change their spamming content patterns to avoid being detected. Second, reflexive reciprocity makes it easier for social spammers to establish social influence and pretend to be normal users by quickly accumulating a large number of "human" friends. It is challenging for existing anti-spamming systems based on batch-mode learning to quickly respond to newly emerging patterns for effective social spammer detection. In this paper, we present a general optimization framework to collectively use content and network information for social spammer detection, and provide the solution for efficient online processing. Experimental results on Twitter datasets confirm the effectiveness and efficiency of the proposed framework.