Widespread accounts of the harmful effects of invasive species have stimulated both practical and theoretical studies on how the spread of these destructive agents can be contained. In practice, a widely used method is the deployment of biological control agents, that is, the release of an additional species (which may also spread) that creates a hostile environment for the invader. Seeding colonies of these protective biological control agents can be used to build a kind of living barrier against the spread of the harmful invader, but the ecological literature documents that attempts to establish colonies of biological control agents often fail (opening gaps in the barrier). Further, the supply of the protective species is limited, and the full supply may not be available immediately. This problem has a natural temporal component: biological control is deployed as the extent of the harmful invasion grows. How can a limited supply of unreliable biological control agents best be deployed over time to protect the landscape against the spread of a harmful invasive species? To explore this question we introduce a new family of stochastic graph vaccination problems that generalizes ideas from social networks and multistage graph vaccination. We point out a deterministic (1 - 1/e)-approximation algorithm for a deterministic base case studied in the social networks literature (matching the previous best randomized (1 -1/e) guarantee for that problem). Next, we show that the randomized (1 -1/e) guarantee (and a deterministic 1/2 guarantee) can be extended to our much more general family of stochastic graph vaccination problems in which vaccinations (a.k.a. biological control colonies) spread but may be unreliable. For the non-spreading vaccination case with unreliable vaccines, we give matching results in trees. Qualitatively, our extension is from computing “cuts over time” to computing “robust cuts over time.” Our new family of problems captures the key tensions we identify for containing invasive species spread with unreliable biological control agents: a robust barrier is built over time with unreliable resources to contain an expanding invasion.

Exemplar-based clustering methods have been extensively shown to be effective in many clustering problems. They adaptively determine the number of clusters and hold the appealing advantage of not requiring the estimation of latent parameters, which is otherwise difficult in case of complicated parametric model and high dimensionality of the data. However, modeling arbitrary underlying distribution of the data is still difficult for existing exemplar-based clustering methods. We present Pairwise Exemplar Clustering (PEC) to alleviate this problem by modeling the underlying cluster distributions more accurately with non-parametric kernel density estimation. Interpreting the clusters as classes from a supervised learning perspective, we search for an optimal partition of the data that balances two quantities: 1 the misclassification rate of the data partition for separating the clusters; 2 the sum of within-cluster dissimilarities for controlling the cluster size. The broadly used kernel form of cut turns out to be a special case of our formulation. Moreover, we optimize the corresponding objective function by a new efficient algorithm for message computation in a pairwise MRF. Experimental results on synthetic and real data demonstrate the effectiveness of our method.

We consider symbolic dynamic programming (SDP) for solving Markov Decision Processes (MDP) with factored state and action spaces, where both states and actions are described by sets of discrete variables. Prior work on SDP has considered only the case of factored states and ignored structure in the action space, causing them to scale poorly in terms of the number of action variables. Our main contribution is to present the first SDP-based planning algorithm for leveraging both state and action space structure in order to compute compactly represented value functions and policies. Since our new algorithm can potentially require more space than when action structure is ignored, our second contribution is to describe an approach for smoothly trading-off space versus time via recursive conditioning. Finally, our third contribution is to introduce a novel SDP approximation that often significantly reduces planning time with little loss in quality by exploiting action structure in weakly coupled MDPs. We present empirical results in three domains with factored action spaces that show that our algorithms scale much better with the number of action variables as compared to state-of-the-art SDP algorithms.

This study considers the problem of feature selection in incomplete data. The intuitive approach is to first impute the missing values, and then apply a standard feature selection method to select relevant features. In this study, we show how to perform feature selection directly, without imputing missing values. We define the objective function of the uncertainty margin-based feature selection method to maximize each instance’s uncertainty margin in its own relevant subspace. In optimization, we take into account the uncertainty of each instance due to the missing values. The experimental results on synthetic and 6 benchmark data sets with few missing values (less than 25%) provide evidence that our method can select the same accurate features as the alternative methods which apply an imputation method first. However, when there is a large fraction of missing values (more than 25%) in data, our feature selection method outperforms the alternatives, which impute missing values first.

We introduce a linear distance relaxation of the n-team Traveling Tournament Problem (TTP), a simple yet powerful heuristic that temporarily "assumes"' the n teams are located on a straight line, thereby reducing the n ( n –1)/2 pairwise distance parameters to just n –1 variables. The modified problem then becomes easier to analyze, from which we determine an approximate solution for the actual instance on n teams. We present combinatorial techniques to solve the Linear Distance TTP (LD-TTP) for n = 4 and n = 6, without any use of computing, generating the complete set of optimal distances regardless of where the n teams are located. We show that there are only 295 non-isomorphic schedules that can be a solution to the 6-team LD-TTP, and demonstrate that in all previously-solved benchmark TTP instances on 6 teams, the distance-optimal schedule appears in this list of 295, even when the six teams are arranged in a circle or located in three-dimensional space. We then extend the LD-TTP to multiple rounds, and apply our theory to produce a nearly-optimal regular-season schedule for the Nippon Pro Baseball league in Japan. We conclude the paper by generalizing our theory to the n -team LD-TTP, producing a feasible schedule whose total distance is guaranteed to be no worse than 4/3 times the optimal solution.

Scientists use two forms of knowledge in the construction ofexplanatory models: generalized entities and processes that relatethem; and constraints that specify acceptable combinations of thesecomponents. Previous research on inductive process modeling, whichconstructs models from knowledge and time-series data, has relied onhandcrafted constraints. In this paper, we report an approach todiscovering such constraints from a set of models that have beenranked according to their error on observations. Our approach adaptsinductive techniques for supervised learning to identify processcombinations that characterize accurate models. We evaluate themethod's ability to reconstruct known constraints and to generalizewell to other modeling tasks in the same domain. Experiments with synthetic data indicate that the approach can successfully reconstructknown modeling constraints. Another study using natural data suggests that transferring constraints acquired from one modeling scenario to another within the same domain considerably reduces the amount of search for candidate model structures while retaining the most accurate ones.

In music, boundaries may occur because scientific domains, including artificial intelligence (Keogh of multiple changes, such as a change in instrumentation, 2011). Research on time series has a long tradition, but a change in harmony, or a change in tempo. The seminal its application to real-world datasets requires to cope with approach by Foote (2000) estimated these changes by new relevant issues, such as the multiple dimensionality of means of a so-called novelty curve, obtained by sliding a data or limited computational resources. Specifically, dealing short-time checkerboard kernel over the diagonal of a selfsimilarity with large-scale data, (1) algorithms must be efficient, matrix of pairwise sample comparisons. Works inspired i.e. they have to scale, (2) supervised approaches may become by Foote's approach explicitly make use of the concept unfeasible, and (3) solutions must use general techniques, of novelty curves (Paulus et al. 2010). Other musictargeted i.e. they should be as independent of the domain as approaches exploit homogeneities in a time series possible (see Mueen and Keogh 2010 for a more detailed by employing more refined techniques like hidden Markov discussion).

Current Coreference Resolution systems utilize a broad range of general knowledge features to make resolutions in a general setting. These approaches ignore coreference knowledge found in domain specific collections and how coreferent entities interact in different domains. This research addresses these issues by developing knowledge bases of coreference characteristics drawn from annotated and unannotated domain texts and utilizing lexical and discourse information to improve resolution.

Causal inference uses observations to infer the causal structure of the data generating system. We study a class of functional models that we call Time Series Models with Independent Noise (TiMINo). These models require independent residual time series, whereas traditional methods like Granger causality exploit the variance of residuals. There are two main contributions: (1) Theoretical: By restricting the model class (e.g. to additive noise) we can provide a more general identifiability result than existing ones. This result incorporates lagged and instantaneous effects that can be nonlinear and do not need to be faithful, and non-instantaneous feedbacks between the time series. (2) Practical: If there are no feedback loops between time series, we propose an algorithm based on non-linear independence tests of time series. When the data are causally insufficient, or the data generating process does not satisfy the model assumptions, this algorithm may still give partial results, but mostly avoids incorrect answers. An extension to (non-instantaneous) feedbacks is possible, but not discussed. It outperforms existing methods on artificial and real data. Code can be provided upon request.

The constraints arising from DAG models with latent variables can be naturally represented by means of acyclic directed mixed graphs (ADMGs). Such graphs contain directed and bidirected arrows, and contain no directed cycles. DAGs with latent variables imply independence constraints in the distribution resulting from a 'fixing' operation, in which a joint distribution is divided by a conditional. This operation generalizes marginalizing and conditioning. Some of these constraints correspond to identifiable 'dormant' independence constraints, with the well known 'Verma constraint' as one example. Recently, models defined by a set of the constraints arising after fixing from a DAG with latents, were characterized via a recursive factorization and a nested Markov property. In addition, a parameterization was given in the discrete case. In this paper we use this parameterization to describe a parameter fitting algorithm, and a search and score structure learning algorithm for these nested Markov models. We apply our algorithms to a variety of datasets.