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Publication details
Date: 1 December 2015
Type: Article
Yanjie Fu, Yong Ge, Yu Zheng, Yao, Yanchi Liu, Hui Xiong, and Nicholas Jing Yuan

Ranking residential real estates based on investment values can provide decision making support for home buyers and thus plays an important role in estate marketplace. In this paper, we aim to develop methods for ranking estates based on investment values by mining users opinions about estates from online user reviews and offline moving behaviors (e.g., taxi traces, smart card transactions, check-ins). While a variety of features could be extracted from these data, these features are intercorrelated and...

Publication details
Date: 1 December 2015
Type: Inproceeding
Publisher: IEEE – Institute of Electrical and Electronics Engineers
H. Lombaert, A. Criminisi, and N. Ayache

This paper presents a new method for classifying surface data via spectral representations of shapes. Our approach benefits classification problems that involve data living on surfaces, such as in cortical parcellation. For instance, current methods for labeling cortical points into surface parcels often involve a slow mesh deformation toward pre-labeled atlases, requiring as much as 4 hours with the established FreeSurfer. This may burden neuroscience studies involving region-specific measurements....

Publication details
Date: 1 October 2015
Type: Inproceeding
Publisher: Springer
Yu Zheng

The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles and animals. Many techniques have been proposed for processing, managing and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into trajectory data mining, providing a panorama of the field...

Publication details
Date: 1 September 2015
Type: Article
Publisher: ACM – Association for Computing Machinery
Bhaskar Mitra

Search logs contain examples of frequently occurring patterns of user reformulations of queries. Intuitively, the reformulation "san francisco" → "san francisco 49ers" is semantically similar to "detroit" →"detroit lions". Likewise, "london"→"things to do in london" and "new york"→"new york tourist attractions" can also be considered similar transitions in intent. The reformulation "movies" → "new movies" and "york" → "new york", however, are clearly different despite the lexical similarities in the two...

Publication details
Date: 1 August 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Gerard Pons-Moll, Jonathan Taylor, Jamie Shotton, Aaron Hertzmann, and Andrew Fitzgibbon

We present a new method for inferring dense data to model correspondences, focusing on the application of human pose estimation from depth images. Recent work proposed the use of regression forests to quickly predict correspondences between depth pixels and points on a 3D human mesh model. That work, however, used a proxy forest training objective based on the classification of depth pixels to body parts. In contrast, we introduce Metric Space Information Gain (MSIG), a new decision forest training...

Publication details
Date: 1 August 2015
Type: Article
Publisher: Springer
J. Valentin, V. Vineet, M.-M. Cheng, D. Kim, J. Shotton, P. Kohli, M. Niessner, A. Criminisi, S. Izadi, and P. Torr
Publication details
Date: 1 August 2015
Type: Article
Publisher: ACM – Association for Computing Machinery
Mohammad Raza, Sumit Gulwani, and Natasa Milic-Frayling
Publication details
Date: 1 July 2015
Type: Inproceeding
Publication details
Date: 1 July 2015
Type: Inproceeding
Publisher: ACL – Association for Computational Linguistics
Fuzheng Zhang, Nicholas Jing Yuan, David Wilkie, Yu Zheng, and Xing Xie

Urban transportation is an important factor in energy consumption and pollution, and is of increasing concern due to its complexity and economic significance. Its importance will only increase as urbanization continues around the world. In this paper, we explore drivers’ refueling behavior in urban areas. Compared to questionnaire-based methods of the past, we propose a complete data-driven system that pushes towards real-time sensing of individual refueling behavior and citywide petrol consumption. Our...

Publication details
Date: 1 June 2015
Type: Article
Publisher: ACM – Association for Computing Machinery
Jialu Liu, Jingbo Shang, Chi Wang, Xiang Ren, and Jiawei Han

Text data are ubiquitous and play an essential role in big data applications. However, text data are mostly unstructured. Transforming unstructured text into structured units (e.g., semantically meaningful phrases) will substantially reduce semantic ambiguity and enhance the power and efficiency at manipulating such data using database technology. Thus mining quality phrases is a critical research problem in the field of databases. In this paper, we propose a new framework that extracts quality...

Publication details
Date: 1 June 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Hao Fang, Saurabh Gupta, Forrest Iandola, Rupesh Srivastava, Li Deng, Piotr Dollar, Jianfeng Gao, Xiaodong He, Margaret Mitchell, John Platt, Lawrence Zitnick, and Geoffrey Zweig

This paper presents a novel approach for automatically generating image descriptions: visual detectors, language models, and multimodal similarity models learnt directly from a dataset of image captions. We use multiple instance learning to train visual detectors for words that commonly occur in captions, including many different parts of speech such as nouns, verbs, and adjectives. The word detector outputs serve as conditional inputs to a maximum-entropy language model. The language model learns from...

Publication details
Date: 1 June 2015
Type: Article
Publisher: IEEE – Institute of Electrical and Electronics Engineers
Alessandro Sordoni, Michel Galley, Michael Auli, Chris Brockett, Yangfeng Ji, Meg Mitchell, Jian-Yun Nie, and Bill Dolan
Publication details
Date: 1 June 2015
Type: Inproceeding
Publisher: Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL-HLT 2015)
Hyon Lim and Sudipta N. Sinha

In this paper, we propose a novel method to recover the 3D trajectory of a moving person from a monocular camera mounted on a quadrotor micro aerial vehicle (MAV). The key contribution is an integrated approach that simultaneously performs visual odometry (VO) and persistent tracking of a person automatically detected in the scene. All computation pertaining to VO, detection and tracking runs onboard the MAV from a front-facing monocular RGB camera. Given the gravity direction from an inertial sensor...

Publication details
Date: 27 May 2015
Type: Inproceeding
Publisher: IEEE – Institute of Electrical and Electronics Engineers
Kuansan Wang

Human is the only species on earth that has mastered the technologies in writing and printing to capture ephemeral thoughts and scientific discoveries. The capabilities to pass along knowledge, not only geographically but also generationally, have formed the bedrock of our civilizations. We are in the midst of a silent revolution driven by the technological advancements: no longer are computers just a fixture of our physical world but have they been so deeply woven into our daily routines that they are...

Publication details
Date: 18 May 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Varun Jampani, SM Ali Eslami, Daniel Tarlow, Pushmeet Kohli, and John Winn

Generative models provide a powerful framework for probabilistic reasoning. However, in many domains their use has been hampered by the practical difficulties of inference. This is particularly the case in computer vision, where models of the imaging process tend to be large, loopy and layered. For this reason bottom-up conditional models have traditionally dominated in such domains. We find that widely-used, general-purpose message passing inference algorithms such as Expectation Propagation (EP) and...

Publication details
Date: 1 May 2015
Type: Inproceeding
Yi Wu, David Wipf, and Jeong-Min Yun

Linear discriminant analysis (LDA) represents a simple yet powerful technique for partitioning a p-dimensional feature vector into one of K classes based on a linear projection learned from N labeled observations. However, it is well-established that in the high-dimensional setting (p > N) the underlying projection estimator degenerates. Moreover, any linear discriminate function involving a large number of features may be difficult to interpret. To ameliorate these issues, two general categories of...

Publication details
Date: 1 May 2015
Type: Inproceeding
Publisher: Artificial Intelligence and Statistics (AISTATS)
Ali Mamdouh Elkahky, Yang Song, and Xiaodong He

Recent online services rely heavily on automatic personalization to recommend relevant content to a large number of users. This requires systems to scale promptly to accommodate the stream of new users visiting the online services for the first time. In this work, we propose a content-based recommendation system to address both the recommendation quality and the system scalability. We propose to use a rich feature set to represent users, according to their web browsing history and search queries. We use...

Publication details
Date: 1 May 2015
Type: Inproceeding
Publisher: WWW – World Wide Web Consortium (W3C)
Lihong Li, Remi Munos, and Csaba Szepesvari

This paper studies the off-policy evaluation problem, where one aims to estimate the value of a target policy based on a sample of observations collected by another policy. We first consider the single-state, or multi-armed bandit case, establish a finite-time minimax risk lower bound, and analyze the risk of three standard estimators. For the so-called regression estimator, we show that while it is asymptotically optimal, for small sample sizes it may perform suboptimally compared to an ideal oracle up...

Publication details
Date: 1 May 2015
Type: Inproceeding
Publisher: JMLR: Workshop and Conference Proceedings
Lihong Li, Shunbao Chen, Jim Kleban, and Ankur Gupta

Optimizing an interactive system against a predefined online metric is particularly challenging, especially when the metric is computed from user feedback such as clicks and payments. The key challenge is the counterfactual nature: in the case of Web search, any change to a component of the search engine may result in a different search result page for the same query, but we normally cannot infer reliably from search log how users would react to the new result page. Consequently, it appears...

Publication details
Date: 1 May 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng

We consider learning representations of entities and relations in KBs using the neural-embedding approach. We show that most existing models, including NTN (Socher et al., 2013) and TransE (Bordes et al., 2013b), can be generalized under a unified learning framework, where entities are low-dimensional vectors learned from a neural network and relations are bilinear and/or linear mapping functions. Under this framework, we compare a variety of embedding models on the link prediction task. We show that a...

Publication details
Date: 1 May 2015
Type: Inproceeding
Huan Sun, Hao Ma, Wen-tau Yih, Chen-Tse Tsai, Jingjing Liu, and Ming-Wei Chang

Most recent question answering (QA) systems query large-scale knowledge bases (KBs) to answer a question, after parsing and transforming natural language questions to KBs-executable forms (e.g., logical forms). As a well-known fact, KBs are far from complete, so that information required to answer questions may not always exist in KBs. In this paper, we develop a new QA system that mines answers directly from the Web, and meanwhile employs KBs as a significant auxiliary to further boost the QA...

Publication details
Date: 1 May 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Publication details
Date: 1 May 2015
Type: Article
Publisher: NAACL
Sudip Roy, Arnd Christian König, Igor Dvorkin, and Manish Kumar

Cloud platforms involve multiple independently developed components, often executing on diverse hardware configurations and across multiple data centers. This complexity makes tracking various key performance indicators (KPIs) and manual diagnosing of anomalies in system behavior both difficult and expensive. In this paper, we describe Argus, an automated system for mining service logs to identify anomalies and help formulate data-driven hypotheses.

Argus includes a suite of efficient mining...

Publication details
Date: 15 April 2015
Type: Inproceeding
Publisher: IEEE
Saleema Amershi, Max Chickering, Steven M. Drucker, Bongshin Lee, Patrice Simard, and Jina Suh

Model building in machine learning is an iterative process. The performance analysis and debugging step typically involves a disruptive cognitive switch from model building to error analysis, discouraging an informed approach to model building. We present ModelTracker, an interactive visualization that subsumes information contained in numerous traditional summary statistics and graphs while displaying example-level performance and enabling direct error examination and debugging. Usage analysis from...

Publication details
Date: 1 April 2015
Type: Proceedings
Publisher: ACM – Association for Computing Machinery
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