Infinite SVM (iSVM) is a Dirichlet process (DP) mixture of large-margin classifiers. Though flexible in learning nonlinear classifiers and discovering latent clustering structures, iSVM has a difficult inference task and existing methods could hinder its applicability to large-scale problems. This paper presents a small-variance asymptotic analysis to derive a simple and efficient algorithm, which monotonically optimizes a max-margin DP-means (M2DPM) problem, an extension of DP-means for both predictive learning and descriptive clustering. Our analysis is built on Gibbs infinite SVMs, an alternative DP mixture of large-margin machines, which admits a partially collapsed Gibbs sampler without truncation by exploring data augmentation techniques. Experimental results show that M2DPM runs much faster than similar algorithms without sacrificing prediction accuracies.

In this paper, we propose a new learning method sim- ulation adjusting that adjusts simulation policy to im- prove the move decisions of the Monte Carlo method. We demonstrated simulation adjusting for 4 × 4 board Go problems. We observed that the rate of correct an- swers moderately increased.

There has been growing interest in stochastic methods to natural language generation (NLG). While most NLG pipelines separate morphological generation and syntactic linearization, the two tasks are closely related. In this paper, we study joint morphological generation and linearization, making use of word order and inflections information for both tasks and reducing error propagation. Experiments show that the joint method significantly outperforms a strong pipelined baseline (by 1.1 BLEU points). It also achieves the best reported result on the Generation Challenge 2011 shared task.

Multi-Instance Multi-Label (MIML) is a learning framework where an example is associated with multiple labels and represented by a set of feature vectors (multiple instances). In the formalization of MIML learning, instances come from a single source (single view). To leverage multiple information sources (multi-view), we develop a multi-view MIML framework based on hierarchical Bayesian Network, and derive an effective learning algorithm based on variational inference. The model can naturally deal with examples in which some views could be absent (partial examples). On multi-view datasets, it is shown that our method is better than other multi-view and single-view approaches particularly in the presence of partial examples. On single-view benchmarks, extensive evaluation shows that our method is highly competitive or better than other MIML approaches on labeling examples and instances. Moreover, our method can effectively handle datasets with a large number of labels.

In this paper we propose a novel method for image semantic segmentation using multiple graphs. The multiview affinity graph is constructed by leveraging the consistency between semantic space and multiple visualspaces. With block-diagonal constraints, we enforce the affinity matrix to be sparse such that the pairwise potential for dissimilar superpixels is close to zero. By a divide-and-conquer strategy, the optimizationfor learning affinity matrix is decomposed into several subproblems that can be solved in parallel. Using the neighborhood relationship between superpixels and the consistency between affinity matrix and labelconfidencematrix, we infer the semantic label for each superpixel of unlabeled images by minimizing an objective whose closed form solution can be easily obtained. Experimental results on two real-world image datasetsdemonstrate the effectiveness of our method.

The main focus of our work is the use of classical planning algorithms in service of more complex problems of planning under uncertainty. In particular, we are exploring compilation techniques that allow us to reduce some probabilistic planning problems into variants of classical planning, such as metric planning,resource-bounded planning, and cost-bounded suboptimal planning. Currently, our initial work focuses on \emph{conformant probabilistic planning}. We intend toimprove our current methods by improving our compilation methods, but also by improving the ability of current planners to handle the special features ofour compiled problems. Then, we hope to extend these techniques to handle more complex probabilistic settings, such as problems with stochastic actions andpartial observability.

Relation inference between concepts in knowledge base has been extensively studied in recent years. Previous methods mostly apply the relations in the knowledge base, without fully utilizing the contents, i.e., the attributes of concepts in knowledge base. In this paper, we propose a content-structural relation inference method (CSRI) which integrates the content and structural information between concepts for relation inference. Experiments on data sets show that CSRI obtains 15% improvement compared with the state-of-the-art methods.

Subspace clustering is an important unsupervised learning problem with wide applications in computer vision and data analysis. However, the state-of-the-art methods for this problem suffer from high time complexity---quadratic or cubic in $n$ (the number of data instances). In this paper we exploit a data selection algorithm to speedup computation and the robust principal component analysis to strengthen robustness. Accordingly, we devise a scalable and robust subspace clustering method which costs time only linear in $n$. We prove theoretically that under certain mild assumptions our method solves the subspace clustering problem exactly even for grossly corrupted data. Our algorithm is based on very simple ideas, yet it is the only linear time algorithm with noiseless or noisy recovery guarantee. Finally, empirical results verify our theoretical analysis.

Classical approaches to visualization directly reduce a document's high-dimensional representation into visualizable two or three dimensions, using techniques such as multidimensional scaling. More recent approaches consider an intermediate representation in topic space, between word space and visualization space, which preserves the semantics by topic modeling. We call the latter semantic visualization problem, as it seeks to jointly model topic and visualization. While previous approaches aim to preserve the global consistency, they do not consider the local consistency in terms of the intrinsic geometric structure of the document manifold. We therefore propose an unsupervised probabilistic model, called Semafore, which aims to preserve the manifold in the lower-dimensional spaces. Comprehensive experiments on several real-life text datasets of news articles and web pages show that Semafore significantly outperforms the state-of-the-art baselines on objective evaluation metrics.

One challenge of link prediction in online social networks is the large scale of many such networks. The measures used by existing work lack a computational consideration in the large scale setting. We propose the notion of social distance in a multi-dimensional form to measure the closeness among a group of people in Microblogs. We proposed a fast hashing approach called Locality-sensitive Social Distance Hashing (LSDH), which works in an unsupervised setup and performs approximate near neighbor search without high-dimensional distance computation. Experiments were applied over a Twitter dataset and the preliminary results testified the effectiveness of LSDH in predicting the likelihood of future associations between people.