We consider problems of sequential robot manipulation (aka. combined task and motion planning) where the objective is primarily given in terms of a cost function over the final geometric state, rather than a symbolic goal description. In this case we should leverage optimization methods to inform search over potential action sequences. We propose to formulate the problem holistically as a 1st-order logic extension of a mathematical program: a non-linear constrained program over the full world trajectory where the symbolic state-action sequence defines the (in-)equality constraints. We tackle the challenge of solving such programs by proposing three levels of approximation: The coarsest level introduces the concept of the effective end state kinematics, parametrically describing all possible end state configurations conditional to a given symbolic action sequence. Optimization on this level is fast and can inform symbolic search. The other two levels optimize over interaction keyframes and eventually over the full world trajectory across interactions. We demonstrate the approach on a problem of maximizing the height of a physically stable construction from an assortment of boards, cylinders and blocks.

Distributed word representations have a rising interest in NLP community. Most of existing models assume only one vector for each individual word, which ignores polysemy and thus degrades their effectiveness for downstream tasks. To address this problem, some recent work adopts multi-prototype models to learn multiple embeddings per word type. In this paper, we distinguish the different senses of each word by their latent topics. We present a general architecture to learn the word and topic embeddings efficiently, which is an extension to the Skip-Gram model and can model the interaction between words and topics simultaneously. The experiments on the word similarity and text classification tasks show our model outperforms state-of-the-art methods.

We consider problems of sequential robot manipulation (aka. combined task and motion planning) where the objective is primarily given in terms of a cost function over the final geometric state, rather than a symbolic goal description. In this case we should leverage optimization methods to inform search over potential action sequences. We propose to formulate the problem holistically as a 1st-order logic extension of a mathematical program: a non-linear constrained program over the full world trajectory where the symbolic state-action sequence defines the (in-)equality constraints. We tackle the challenge of solving such programs by proposing three levels of approximation: The coarsest level introduces the concept of the effective end state kinematics, parametrically describing all possible end state configurations conditional to a given symbolic action sequence. Optimization on this level is fast and can inform symbolic search. The other two levels optimize over interaction keyframes and eventually over the full world trajectory across interactions. We demonstrate the approach on a problem of maximizing the height of a physically stable construction from an assortment of boards, cylinders and blocks.

Distributed word representations have a rising interest in NLP community. Most of existing models assume only one vector for each individual word, which ignores polysemy and thus degrades their effectiveness for downstream tasks. To address this problem, some recent work adopts multi-prototype models to learn multiple embeddings per word type. In this paper, we distinguish the different senses of each word by their latent topics. We present a general architecture to learn the word and topic embeddings efficiently, which is an extension to the Skip-Gram model and can model the interaction between words and topics simultaneously. The experiments on the word similarity and text classification tasks show our model outperforms state-of-the-art methods.

Probabilistic Classifiers Chains (PCC) offers interesting properties to solve multi-label classification tasks due to its ability to estimate the joint probability of the labels. However, PCC presents the major drawback of having a high computational cost in the inference process required to predict new samples. Lately, several approaches have been proposed to overcome this issue, including beam search and an epsilon-Approximate algorithm based on uniform-cost search. Surprisingly, the obvious possibility of using heuristic search has not been considered yet. This paper studies this alternative and proposes an admisible heuristic that, applied in combination with A* algorithm, guarantees, not only optimal predictions in terms of subset 0/1 loss, but also that it always explores less nodes than epsilon-Approximate algorithm. In the experiments reported, the number of nodes explored by our method is less than two times the number of labels for all datasets analyzed. But, the difference in explored nodes must be large enough to compensate the overhead of the heuristic in order to improve prediction time. Thus, our proposal may be a good choice for complex multi-label problems.

The semantic class (i.e., type) of an entity plays a vital role in many natural language processing tasks, such as question answering. However, most of existing type classification systems extensively rely on hand-crafted features. This paper introduces a hybrid neural model which classifies entity mentions to a wide-coverage set of 22 types derived from DBpedia. It consists of two parts. The mention model uses recurrent neural networks to recursively obtain the vector representation of an entity mention from the words it contains. The context model, on the other hand, employs multilayer perceptrons to obtain the hidden representation for contextual information of a mention. Representations obtained by the two parts are used together to predict the type distribution. Using automatically generated data, these two parts are jointly learned. Experimental studies illustrate that the proposed approach outperforms baseline methods. Moreover, when type information provided by our method is used in a question answering system, we observe a 14.7% relative improvement for the top-1 accuracy of answers.

Cross-lingual sentiment classification aims to automatically predict sentiment polarity (e.g., positive or negative) of data in a label-scarce target language by exploiting labeled data from a label-rich language. The fundamental challenge of cross-lingual learning stems from a lack of overlap between the feature spaces of the source language data and that of the target language data. To address this challenge, previous work in the literature mainly relies on the large amount of bilingual parallel corpora to bridge the language gap. In many real applications, however, it is often the case that we have some partial parallel data but it is an expensive and time-consuming job to acquire large amount of parallel data on different languages. In this paper, we propose a novel subspace learning framework by leveraging the partial parallel data for cross-lingual sentiment classification. The proposed approach is achieved by jointly learning the document-aligned review data and un-aligned data from the source language and the target language via a non-negative matrix factorization framework. We conduct a set of experiments with cross-lingual sentiment classification tasks on multilingual Amazon product reviews. Our experimental results demonstrate the efficacy of the proposed cross-lingual approach.

Multi-view outlier detection is a challenging problem due to the inconsistent behaviors and complicated distributions of samples across different views. The existing approaches are designed to identify the outlier exhibiting inconsistent characteristics across different views. However, due to the inevitable system errors caused by data-captured sensors or others, there always exists another type of outlier, which consistently behaves abnormally in individual view. Unfortunately, this kind of outlier is neglected by all the existing multi-view outlier detection methods, consequently their outlier detection performances are dramatically harmed.In this paper, we propose a novel Dual-regularized Multi-view Outlier Detection method (DMOD) to detect both kinds of anomalies simultaneously. By representing the multi-view data with latent coefficients and sample-specific errors, we characterize each kind of outlier explicitly. Moreover, an outlier measurement criterion is well-designed to quantify the inconsistency. To solve the proposed non-smooth model, a novel optimization algorithm is proposed in an iterative manner. We evaluate our method on five datasets with different outlier settings. The consistent superior results to other state-of-the-art methods demonstrate the effectiveness of our approach.

Stable matchings can be computed by deferred acceptance (DA) algorithms. However such algorithms become incomplete when complementarities exist among the agent preferences: they can fail to find a stable matching even when one exists. In this paper we examine stable matching problems arising from labour market with couples (SMP-C). The classical problem of matching residents into hospital programs is an example. Couples introduce complementarities under which DA algorithms become incomplete. In fact, SMP-C is NP-complete. Inspired by advances in SAT and integer programming (IP) solvers we investigate encoding SMP-C into SAT and IP and then using state-of-the-art SAT and IP solvers to solve it. We also implemented two previous DA algorithms. After comparing the performance of these different solution methods we find that encoding to SAT can be surprisingly effective, but that our encoding to IP does not scale as well. Using our SAT encoding we are able to determine that the DA algorithms fail on a non-trivial number of cases where a stable matching exists. The SAT and IP encodings also have the property that they can verify that no stable matching exists, something that the DA algorithms cannot do.

In this paper, we propose a Weakly Supervised Matrix Factorization (WSMF) approach to the problem of image parsing with noisy tags, i.e., segmenting noisily tagged images and then classifying the regions only with image-level labels. Instead of requiring clean but expensive pixel-level labels as strong supervision in the traditional image parsing methods, we take noisy image-level labels as weakly-supervised constraints. Specifically, we first over-segment all the images into multiple regions which are initially labeled based upon the image-level labels. Moreover, from a low-rank matrix factorization viewpoint, we formulate noisily tagged image parsing as a weakly supervised matrix factorization problem. Finally, we develop an efficient algorithm to solve the matrix factorization problem. Experimental results show the promising performance of the proposed WSMF algorithm in comparison with the state-of-the-arts.