Change in abstract argumentation frameworks (AFs) is a very active topic. Especially, the problem of enforcing a set E of arguments, i.e., ensuring that E is an extension (or a subset of an extension) of a given AF F, has received a particular attention in the recent years. In this paper, we define a new family of enforcement operators, for which enforcement can be achieved by adding new arguments (and attacks) to F (as in previous approaches to enforcement), but also by questioning some attacks (and non-attacks) of F. This family includes previous enforcement operators, but also new ones for which the success of the enforcement operation is guaranteed. We show how the enforcement problem for the operators of the family can be modeled as a pseudo-Boolean optimization problem. Intensive experiments show that the method is practical and that it scales up well.

For particular sparse optimization problems, among them is the popular tasks, its low computation cost of linear subproblem proximal gradient (PG) based approach ([Beck and Teboulle, evaluation on each iteration leads to superior 2009]; [Nesterov, 2013]). This kind of methods can achieve practical performance. However, the inferior iteration the optimal rate of convergence under certain problem settings, complexity incurs excess number of gradient hence it enjoys low iteration complexity. The periteration evaluations, which can counteract the efficiency cost mainly comes from gradient evaluation and a gained by solving low cost linear subproblem. In proximal map (PM) related to the type of the regularizer. On this paper, we therefore propose a novel algorithm the one hand, due to the optimal iteration complexity, the that requires optimal graduate evaluations as proximal number of gradient evaluations is optimal for PG method.

Covariate shift is a fundamental problem for learning in non-stationary environments where the conditional distribution p(y|x) is the same between training and test data while their marginal distributions p tr (x) and p te (x) are different. Although many covariate shift correction techniques remain effective for real world problems, most do not scale well in practice. In this paper, using inspiration from recent optimization techniques, we apply the Frank-Wolfe algorithm to two well-known covariate shift correction techniques, Kernel Mean Matching (KMM) and Kullback-Leibler Importance Estimation Procedure (KLIEP), and identify an important connection between kernel herding and KMM. Our complexity analysis shows the benefits of the Frank-Wolfe approach over projected gradient methods in solving KMM and KLIEP. An empirical study then demonstrates the effectiveness and efficiency of the Frank-Wolfe algorithm for correcting covariate shift in practice.

Person re-identification plays an important role in many safety-critical applications. Existing works mainly focus on extracting patch-level features or learning distance metrics. However, the representation power of extracted features might be limited, due to the various viewing conditions of pedestrian images in reality. To improve the representation power of features, we learn discriminative and robust representations via dictionary learning in this paper. First, we propose a cross-view projective dictionary learning (CPDL) approach, which learns effective features for persons across different views. CPDL is a general framework for multi-view dictionary learning. Secondly, by utilizing the CPDL framework, we design two objectives to learn low-dimensional representations for each pedestrian in the patch-level and the image-level, respectively. The proposed objectives can capture the intrinsic relationships of different representation coefficients in various settings. We devise efficient optimization algorithms to solve the objectives. Finally, a fusion strategy is utilized to generate the similarity scores. Experiments on the public VIPeR and CUHK Campus datasets show that our approach achieves the state-of-the-art performance.

Extractive document summarization aims to conclude given documents by extracting some salient sentences. Often, it faces two challenges: 1) how to model the information redundancy among candidate sentences; 2) how to select the most appropriate sentences. This paper attempts to build a strong summarizer DivSelect+CNNLM by presenting new algorithms to optimize each of them. Concretely, it proposes CNNLM, a novel neural network language model (NNLM) based on convolutional neural network (CNN), to project sentences into dense distributed representations, then models sentence redundancy by cosine similarity. Afterwards, it formulates the selection process as an optimization problem, constructing a diversified selection process (DivSelect) with the aim of selecting some sentences which have high prestige, meantime, are dis-similar with each other. Experimental results on DUC2002 and DUC2004 benchmark data sets demonstrate the effectiveness of our approach.

We study the stochastic multiple-choice knapsack problem, where a set of Kitems, whose value and weight are random variables, arrive to the system at each time step, and a decision maker has to choose at most one item to put into the knapsack without exceeding its capacity. The goal is the decision-maker is to maximise the total expected value of chosen items with respect to the knapsack capacity and a finite time horizon.We provide the first comprehensive theoretical analysis of the problem. In particular, we propose OPT-S-MCKP, the first algorithm that achieves optimality when the value-weight distributions are known. This algorithm also enjoys O(sqrt{T}) performance loss, where T is the finite time horizon, in the unknown value-weight distributions scenario.We also further develop two novel approximation methods, FR-S-MCKP and G-S-MCKP, and we prove that FR-S-MCKP achieves O(sqrt{T}) performance loss in both known and unknown value-weight distributions cases, while enjoying polynomial computational complexity per time step.On the other hand, G-S-MCKP does not have theoretical guarantees, but it still provides good performance in practice with linear running time.

The aim of this paper is to propose a new approach interweaving preference elicitation and search to solve multiobjective optimization problems. We present an interactive search procedure directed by an aggregation function, possibly non-linear (e.g. an additive disutility function, a Choquet integral), defining the overall cost of solutions. This function is parameterized by weights that are initially unknown. Hence, we insert comparison queries in the search process to obtain useful preference information that will progressively reduce the uncertainty attached to weights. The process terminates by recommending a near-optimal solution ensuring that the gap to optimality is below the desired threshold. Our approach is tested on multiobjective state space search problems and appears to be quite efficient both in terms of number of queries and solution times.

Community detection on social media is a classic and challenging task. In this paper, we study the problem of detecting communities by combining social relations and user generated content in social networks. We propose a nonnegative matrix tri-factorization (NMTF) based clustering framework with three types of graph regularization. The NMTF based clustering framework can combine the relations and content seamlessly and the graph regularization can capture user similarity, message similarity and user interaction explicitly. In order to design regularization components, we further exploit user similarity and message similarity in social networks. A unified optimization problem is proposed by integrating the NMTF framework and the graph regularization. Then we derive an iterative learning algorithm for this optimization problem. Extensive experiments are conducted on three real-world data sets and the experimental results demonstrate the effectiveness of the proposed method.

Computing the set of optimal solutions for a multi-objective constraint optimisation problem can be computationally very challenging. Also, when solutions are only partially ordered, there can be a number of different natural notions of optimality, one of the most important being the notion of Possibly Optimal, i.e., optimal in at least one scenario compatible with the inter-objective tradeoffs. We develop an AND/OR Branch-and-Bound algorithm for computing the set of Possibly Optimal solutions, and compare variants of the algorithm experimentally.

Graph-based video segmentation has demonstrated its influential impact from recent works. However, most of the existing approaches fail to make a semantic segmentation of the foreground objects, i.e. all the segmented objects are treated as one class. In this paper, we propose an approach to semantically segment the multi-class foreground objects from a single video sequence. To achieve this, we firstly generate a set of proposals for each frame and score them based on motion and appearance features. With these scores, the similarities between each proposal are measured. To tackle the vulnerability of the graph-based model, low-rank representation with l21-norm regularizer outlier detection is proposed to discover the intrinsic structure among proposals. With the "clean" graph representation, objects of different classes are more likely to be grouped into separated clusters. Two open public datasets MOViCS and ObMiC are used for evaluation under both intersection-over-union and F-measure metrics. The superior results compared with the state-of-the-arts demonstrate the effectiveness of the proposed method.