TASK DEFINITION: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
PROBLEM: Model counting is the problem of computing the number of models that satisfy a given propositional theory. It has recently been applied to solving inference tasks in probabilistic logic programming, where the goal is to compute the probability of given queries being true provided a set of mutually independent random variables, a model (a logic program) and some evidence. The core of solving this inference task involves translating the logic program to a propositional theory and using a model counter. In this paper, we show that for some problems that involve inductive definitions like reachability in a graph, the translation of logic programs to SAT can be expensive for the purpose of solving inference tasks. For such problems, direct implementation of stable model semantics allows for more efficient solving. We present two implementation techniques, based on unfounded set detection, that extend a propositional model counter to a stable model counter. Our experiments show that for particular problems, our approach can outperform a state-of-the-art probabilistic logic programming solver by several orders of magnitude in terms of running time and space requirements, and can solve instances of significantly larger sizes on which the current solver runs out of time or memory.

SOLUTION: Stable Model Counting and Its Application in Probabilistic Logic Programming

PROBLEM: Self-paced learning (SPL) has been attracting increasing attention in machine learning and computer vision. Albeit empirically substantiated to be effective, the investigation on its theoretical insight is still a blank. It is even unknown that what objective a general SPL regime converges to. To this issue, this study attempts to initially provide some new insights under this “heuristic” learning scheme. Specifically, we prove that the solving strategy on SPL exactly accords with a majorization minimization algorithm, a well known technique in optimization and machine learning, implemented on a latent objective. A more interesting finding is that, the loss function contained in this latent objective has a similar configuration with non-convex regularized penalty, an attractive topic in statistics and machine learning. In particular, we show that the previous hard and linear self-paced regularizers are equivalent to the capped norm and minimax concave plus penalties, respectively, both being widely investigated in statistics. Such connections between SPL and previous known researches enhance new insightful comprehension on SPL, like convergence and parameter setting rationality. The correctness of the proposed theory is substantiated by experimental results on synthetic and UCI data sets.

SOLUTION: What Objective Does Self-paced Learning Indeed Optimize?

PROBLEM: Recommender systems leverage user demographic informa-<lb>tion, such as age, gender, etc., to personalize recommenda-<lb>tions and better place their targeted ads. Oftentimes, users<lb>do not volunteer this information due to privacy concerns, or<lb>due to a lack of initiative in filling out their online profiles.<lb>We illustrate a new threat in which a recommender learns<lb>private attributes of users who do not voluntarily disclose<lb>them. We design both passive and active attacks that so-<lb>licit ratings for strategically selected items, and could thus<lb>be used by a recommender system to pursue this hidden<lb>agenda. Our methods are based on a novel usage of Bayesian<lb>matrix factorization in an active learning setting. Evalua-<lb>tions on multiple datasets illustrate that such attacks are<lb>indeed feasible and use significantly fewer rated items than<lb>static inference methods. Importantly, they succeed without<lb>sacrificing the quality of recommendations to users.

SOLUTION:
Recommending with an Agenda: Active Learning of Private Attributes using Matrix Factorization