Text classification is an important problem with many applications. Traditional approaches represent text as a bag-of-words and build classifiers based on this representation. Rather than words, entity phrases, the relations between the entities, as well as the types of the entities and relations carry much more information to represent the texts. This paper presents a novel text as network classification framework, which introduces 1) a structured and typed heterogeneous information networks (HINs) representation of texts, and 2) a meta-path based approach to link texts. We show that with the new representation and links of texts, the structured and typed information of entities and relations can be incorporated into kernels. Particularly, we develop both simple linear kernel and indefinite kernel based on meta-paths in the HIN representation of texts, where we call them HIN-kernels. Using Freebase, a well-known world knowledge base, to construct HIN for texts, our experiments on two benchmark datasets show that the indefinite HIN kernel based on weighted meta-paths outperforms the state-of-the-art methods and other HIN-kernels.

Many real world applications in medicine, biology, communication networks, web mining, and economics, among others, involve modeling and learning structured stochastic processes that evolve over continuous time. Existing approaches, however, have focused on propositional domains only. Without extensive feature engineering, it is difficult-if not impossible-to apply them within relational domains where we may have varying number of objects and relations among them. We therefore develop the first relational representation called Relational Continuous-Time Bayesian Networks (RCTBNs) that can address this challenge. It features a nonparametric learning method that allows for efficiently learning the complex dependencies and their strengths simultaneously from sequence data. Our experimental results demonstrate that RCTBNs can learn as effectively as state-of-the-art approaches for propositional tasks while modeling relational tasks faithfully.

The biggest limitation of probabilistic graphical models is the complexity of inference, which is often intractable. An appealing alternative is to use tractable probabilistic models, such as arithmetic circuits (ACs) and sum-product networks (SPNs), in which marginal and conditional queries can be answered efficiently. In this paper, we present the first discriminative structure learning algorithm for ACs, DACLearn (Discriminative AC Learner), which optimizes conditional log-likelihood. Based on our experiments, DACLearn learns models that are more accurate and compact than other tractable generative and discriminative baselines.

In this paper, we propose principled weight learning algorithms for Markov logic networks that can easily scale to much larger datasets and application domains than existing algorithms. The main idea in our approach is to use approximate counting techniques to substantially reduce the complexity of the most computation intensive sub-step in weight learning: computing the number of groundings of a first-order formula that evaluate to true given a truth assignment to all the random variables. We derive theoretical bounds on the performance of our new algorithms and demonstrate experimentally that they are orders of magnitude faster and achieve the same accuracy or better than existing approaches.

We propose a simple but effective unsupervised learning algorithm to detect a common activity (co-activity) from a set of videos, which is formulated using absorbing Markov chain in a principled way. In our algorithm, a complete multipartite graph is first constructed, where vertices correspond to subsequences extracted from videos using a temporal sliding window and edges connect between the vertices originated from different videos; the weight of an edge is proportional to the similarity between the features of two end vertices. Then, we extend the graph structure by adding edges between temporally overlapped subsequences in a video to handle variable-length co-activities using temporal locality, and create an absorbing vertex connected from all other nodes. The proposed algorithm identifies a subset of subsequences as co-activity by estimating absorption time in the constructed graph efficiently. The great advantage of our algorithm lies in the properties that it can handle more than two videos naturally and identify multiple instances of a co-activity with variable lengths in a video. Our algorithm is evaluated intensively in a challenging dataset and illustrates outstanding performance quantitatively and qualitatively.

We study the societal tradeoffs problem, where a set of voters each submit their ideal tradeoff value between each pair of activities (e.g., "using a gallon of gasoline is as bad as creating 2 bags of landfill trash"), and these are then aggregated into the societal tradeoff vector using a rule. We introduce the family of distance-based rules and show that these can be justified as maximum likelihood estimators of the truth. Within this family, we single out the logarithmic distance-based rule as especially appealing based on a social-choice-theoretic axiomatization. We give an efficient algorithm for executing this rule as well as an approximate hill climbing algorithm, and evaluate these experimentally.

This paper introduces an extension of the target surveillance problem in which the surveillance agent is exposed to an adversarial ballistic threat. The problem is formulated as a mixed observability Markov decision process (MOMDP), which is a factored variant of the partially observable Markov decision process, to account for state and dynamic uncertainties. The control policy resulting from solving the MOMDP aims to optimize the frequency of target observations and minimize exposure to the ballistic threat. The adversary’s behavior is modeled with a level-k policy, which is used to construct the state transition of the MOMDP. The approach is empirically evaluated against a MOMDP adversary and against a human opponent in a target surveillance computer game. The empirical results demonstrate that, on average, level 3 MOMDP policies outperform lower level reasoning policies as well as human players.

Recently, there is an increasing interest in solving sequential The shortest path routing (SPR) problem entails finding the decision making problems under uncertainty, such shortest path between two given vertices of the graph that as Markov decision processes (MDPs), partially observable minimizes the total sum of weights of the involved edges MDPs (POMDPs) and its multiagent variants using in the path (Dijkstra 1959; Cormen et al. 2001). Shortest the probabilistic inference based viewpoint (Toussaint and path based approaches have found several applications in Storkey 2006; Toussaint, Charlin, and Poupart 2008; Kumar diverse fields such as transportation models (Pallottino and and Zilberstein 2010; Ghosh, Kumar, and Varakantham Scutella 1998), telecommunication network design (Pioro et 2015; Kumar, Zilberstein, and Toussaint 2015). Our al. 2002) and in ecology for analyzing landscape connectivity approach for SPDM is different from such previous applications for conservation planning (Bunn, Urban, and Keitt 2012; of the LM framework for planning.

Buoyed by the success of deep multilayer neural networks, there is renewed interest in scalable learning of Bayesian neural networks. Here, we study algorithms that utilize recent advances in Bayesian inference to efficiently learn distributions over network weights. In particular, we focus on recently proposed assumed density filtering based methods for learning Bayesian neural networks -- Expectation and Probabilistic backpropagation. Apart from scaling to large datasets, these techniques seamlessly deal with non-differentiable activation functions and provide parameter (learning rate, momentum) free learning. In this paper, we first rigorously compare the two algorithms and in the process develop several extensions, including a version of EBP for continuous regression problems and a PBP variant for binary classification. Next, we extend both algorithms to deal with multiclass classification and count regression problems. On a variety of diverse real world benchmarks, we find our extensions to be effective, achieving results competitive with the state-of-the-art.

Researchers often summarize their work in the form of posters. Posters provide a coherent and efficient way to convey core ideas from scientific papers. Generating a good scientific poster, however, is a complex and time consuming cognitive task, since such posters need to be readable, informative, and visually aesthetic. In this paper, for the first time, we study the challenging problem of learning to generate posters from scientific papers. To this end, a data-driven framework, that utilizes graphical models, is proposed. Specifically, given content to display, the key elements of a good poster, including panel layout and attributes of each panel, are learned and inferred from data. Then, given inferred layout and attributes, composition of graphical elements within each panel is synthesized. To learn and validate our model, we collect and make public a Poster-Paper dataset, which consists of scientific papers and corresponding posters with exhaustively labelled panels and attributes. Qualitative and quantitative results indicate the effectiveness of our approach.