Matrix completion as a common problem in many application domains has received increasing attention in the machine learning community. Previous matrix completion methods have mostly focused on exploiting the matrix low-rank property to recover missing entries. Recently, it has been noticed that side information that describes the matrix items can help to improve the matrix completion performance. In this paper, we propose a novel matrix completion approach that exploits side information within a principled co-embedding framework. This framework integrates a low-rank matrix factorization model and a label embedding based prediction model together to derive a convex co-embedding formulation with nuclear norm regularization. We develop a fast proximal gradient descent algorithm to solve this co-embedding problem. The effectiveness of the proposed approach is demonstrated on two types of real world application problems.

Named entity recognition (NER), which focuses on the extraction of semantically meaningful named entities and their semantic classes from text, serves as an indispensable component for several down-stream natural language processing (NLP) tasks such as relation extraction and event extraction. Dependency trees, on the other hand, also convey crucial semantic-level information. It has been shown previously that such information can be used to improve the performance of NER. In this work, we investigate on how to better utilize the structured information conveyed by dependency trees to improve the performance of NER. Specifically, unlike existing approaches which only exploit dependency information for designing local features, we show that certain global structured information of the dependency trees can be exploited when building NER models where such information can provide guided learning and inference. Through extensive experiments, we show that our proposed novel dependency-guided NER model performs competitively with models based on conventional semi-Markov conditional random fields, while requiring significantly less running time.

Belief Propagation (BP) is an iterative message-passing algorithm for performing inference in graphical models Convergent message-passing algorithms. There has (GMs), such as Markov Random Fields (MRFs). BP calculates been much research on finding variations to the update equations the marginal distribution for each unobserved node, of BP that guarantee convergence. These algorithms conditional on any observed nodes (Pearl 1988). It achieves are often similar in structure to the nonconvergent algorithms, this by propagating the information from a few observed yet it can be proven that the value of the variational nodes throughout the network by iteratively passing information problem (or its dual) improves at each iteration (Hazan and between neighboring nodes. It is known that when Shashua 2008; Heskes 2006; Meltzer, Globerson, and Weiss the graphical model has a tree structure, then BP converges 2009). Another body of recent papers have suggested to to the true marginals (according to exact probabilistic inference) solve the convergence problems of MMinference by linearizing after a finite number of iterations.

Imagining the future helps anticipate and prepare for what is coming. This has great importance to many, if not all, human endeavors. In this paper, we develop the Planning Projector system prototype, which applies plan-recognition-as-planning technique to both explain the observations derived from analyzing relevant news and social media, and project a range of possible future state trajectories for human review. Unlike the plan recognition problem, where a set of goals, and often a plan library must be given as part of the input, the Planning Projector system takes as input the domain knowledge, a sequence of observations derived from the news, a time horizon, and the number of trajectories to produce. It then computes the set of trajectories by applying a planner capable of finding a set of high-quality plans on a transformed planning problem. The Planning Projector prototype integrates several components including: (1) knowledge engineering: the process of encoding the domain knowledge from domain experts; (2) data transformation: the problem of analyzing and transforming the raw data into a sequence of observations; (3) trajectory computation: characterizing the future state projection problem and computing a set of trajectories; (4) user interface: clustering and visualizing the trajectories. We evaluate our approach qualitatively and conclude that the Planning Projector helps users understand future possibilities so that they can make more informed decisions.

In Chinese societies, superstition is of paramount importance, and vehicle license plates with desirable numbers can fetch very high prices in auctions. Unlike other valuable items, license plates are not allocated an estimated price before auction. I propose that the task of predicting plate prices can be viewed as a natural language processing (NLP) task, as the value depends on the meaning of each individual character on the plate and its semantics. I construct a deep recurrent neural network (RNN) to predict the prices of vehicle license plates in Hong Kong, based on the characters on a plate. I demonstrate the importance of having a deep network and of retraining. Evaluated on 13 years of historical auction prices, the deep RNN outperforms previous models by a significant margin.

With the advance of acquisition techniques, plentiful higherorder tensor data sets are built up in a great variety of fields such as computer vision, neuroscience, remote sensing and recommender systems. The real-world tensors often contain missing values, which makes tensor completion become a prerequisite to utilize them. Previous studies have shown that imposing a low-rank constraint on tensor completion produces impressive performances. In this paper, we argue that low-rank constraint, albeit useful, is not effective enough to exploit the local smooth and piecewise priors of visual data. We propose integrating total variation into low-rank tensor completion (LRTC) to address the drawback. As LRTC can be formulated by both tensor unfolding and tensor decomposition, we develop correspondingly two methods, namely LRTC-TV-I and LRTC-TVII, and their iterative solvers. Extensive experimental results on color image and medical image inpainting tasks show the effectiveness and superiority of the two methods against state-of-the-art competitors.

This paper and summary talk broadly survey computational sustainability research. Rather than a detailed treatment of the research projects in the area, which is beyond the scope of the paper and talk, the paper includes a meta-survey, pointing to edited collections and overviews in the literature for the interested reader. Computational sustainability research has been broadly characterized by AI methods employed, sustainability areas addressed, and contributions made to (typically, human) decision-making. The paper addresses these characterizations as well, which will facilitate a deeper synthesis later, to include the potential for developing sophisticated and holistic AI decision-making and advisory agents.

Today's operation of buildings is either based on simple dashboards that are not scalable to thousands of sensor data or on rules that provide very limited fault information only. In either case considerable manual effort is required for diagnosing building operation problems related to energy usage or occupant comfort. We present a Cognitive Building demo that uses (i) semantic reasoning to model physical relationships of sensors and systems, (ii) machine learning to predict and detect anomalies in energy flow, occupancy and user comfort, and (iii) speech-enabled Augmented Reality interfaces for immersive interaction with thousands of devices. Our demo analyzes data from more than 3,300 sensors and shows how we can automatically diagnose building operation problems.

Network and other computer administrators typically have access to a rich set of logs tracking actions by users. However, they often lack metadata such as user role, age, and gender that can provide valuable context for users' actions. Inferring user attributes automatically has wide ranging implications; among others, for customization (anticipating user needs and priorities), for managing resources (anticipating demand) and for security (interpreting anomalous behavior).

Researchers have used Distributed Constraint Optimization Problems (DCOPs) as a powerful approach to model various multi-agent coordination problems, taking into account their preferences and constraints. A core limitation of this model is the assumption that all agents’ preferences are specified a priori. However, in a number of application domains such knowledge become available only after being elicited from users in these domains. In this abstract, we explore the effects of preference elicitation in our motivating application of scheduling smart appliances with the aim of reducing users’ electricity bill cost as well as increasing their comfort.