Inferring the causal direction between two variables is a nontrivial problem in the subject of causal discovery from observed data. A method for errors-in-variables models where both the cause variable and the effect variable are observed with measurement errors is presented in this paper.

Algorithm's time complexity is an essential issue for time series prediction in numerous practices.A novel fast exact inference method for Gaussian process model is proposed in this paper to accelerate the task of non-stationary time series prediction. Experiment was done on the real world power load data.

L1-Graph has been proven to be effective in data clustering, which partitions the data space by using the sparse representation of the data as the similarity measure. However, the sparse representation is performed for each datum separately without taking into account the geometric structure of the data. Motivated by L1-Graph and manifold leaning, we propose Laplacian Regularized L1-Graph (LRℓ1-Graph) for data clustering. The sparse representations of LRℓ1-Graph are regularized by the geometric information of the data so that they vary smoothly along the geodesics of the data manifold by the graph Laplacian according to the manifold assumption. Moreover, we propose an iterative regularization scheme, where the sparse representation obtained from the previous iteration is used to build the graph Laplacian for the current iteration of regularization. The experimental results on real data sets demonstrate the superiority of our algorithm compared to L1-Graph and other competing clustering methods.

Similarity between words is becoming a generic problem for many applications of computational linguistics, and computing word similarities is determined by word representations. Inspired by the analogies between words and lymphocytes, a lymphocyte-style word representation is proposed. The word representation is built on the basis of dependency syntax of sentences and represent word context as head properties and dependent properties of the word. Lymphocyte-style word representations are evaluated by computing the similarities between words, and experiments are conducted on the Penn Chinese Treebank 5.1. Experimental results indicate that the proposed word representations are effective.

A key strategy for speeding up computation is to run in parallel on multiple cores. However, on hard combinatorial problems, exploiting parallelism has been surprisingly challenging. It appears that traditional divide-and-conquer strategies do not work well, due to the intricate non-local nature of the interactions between the problem variables. In this paper, we introduce a novel way in which parallelism can be used to exploit hidden structure of hard combinatorial problems. We demonstrate the success of this approach on minimal set basis problem, which has a wide range of applications in machine learning and system security, etc. We also show the effectiveness on a related application problem from materials discovery. In our approach, a large number of smaller sub-problems are identified and solved concurrently. We then aggregate the information from those solutions, and use this to initialize the search of a global, complete solver. We show that this strategy leads to a significant speed-up over a sequential approach. The strategy also greatly outperforms state-of-the-art incomplete solvers in terms of solution quality. Our work opens up a novel angle for using parallelism to solve hard combinatorial problems.

Instance checking is considered a central service for data retrieval from description logic (DL) ontologies. In this paper, we propose a revised most specific concept (MSC) method for DL SHI}, which converts instance checking into subsumption problems. This revised method can generate small concepts that are specific-enough to answer a given query, and allow reasoning to explore only a subset of the ABox data to achieve efficiency. Experiments show effectiveness of our proposed method in terms of concept size reduction and the improvement in reasoning efficiency.

Robots performing service tasks such as cooking and cleaning in human-centric environments require knowledge of certain environmental states in order to complete tasks successfully. While much effort has gone into developing various estimators for deriving distributions on values of unknown states, less attention has been placed on why the particular estimation problem arises. In this work, I argue that state estimation should no longer be treated as a black box. Estimating large sets of variables is computationally costly; just because a technique exists to estimate the values of certain variables does not justify its application. For robots whose ultimate mission is to complete tasks, only variables that are relevant to successful completion should be estimated. I propose to initially only track a minimal set of directly-relevant variables (attention), and gradually increase the sophistication of models on-demand (refinement), in a local fashion. This estimator refinement process is triggered by violations in expectations of task success (mismatch). This model selection framework is demonstrated through a proof-of-concept case study.

We describe a data complexity approach to kernel selection based on the behavior of polynomial and Gaussian kernels. Our resultsshow how the use of a Gaussian kernel produces a gram matrix with useful local information that has no equivalent counterpart inpolynomial kernels.By exploiting neighborhood information embedded by data complexity measures, we are able to carry out a form of meta-generalization.Our goal is to predict which data sets are more favorable to particular kernels (Gaussian or polynomial).The end result is a framework to improve the model selection process in Support Vector Machines.

We develop an online multi-task formulation of model-based gradient temporal-difference (GTD) reinforcement learning. Our approach enables an autonomous RL agent to accumulate knowledge over its lifetime and efficiently share this knowledge between tasks to accelerate learning. Rather than learning a policy for a reinforcement learning task tabula rasa, as in standard GTD, our approach rapidly learns a high performance policy by building upon the agent's previously learned knowledge. Our preliminary results on controlling different mountain car tasks demonstrates that GTD-ELLA significantly improves learning over standard GTD(0).

Hybrid reasoners combine multiple types of reasoning, usually subsumption and Prolog-style resolution. We outline a system which combines natural deduction and subsumption reasoning using Inference Graphs implementing a Logic of Arbitrary and Indefinite Objects.