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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
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
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
Nathan Wiebe, Ashish Kapoor, and Krysta M. Svore

We present several quantum algorithms for performing nearest-neighbor learning. At the core of our algorithms are fast and coherent quantum methods for computing distance metrics such as the inner product and Euclidean distance. We prove upper bounds on the number of queries to the input data required to compute these metrics. In the worst case, our quantum algorithms lead to polynomial reductions in query complexity relative to the corresponding classical algorithm. In certain cases, we show...

Publication details
Date: 1 March 2015
Type: Article
Publisher: Rinton Press
Number: 3&4
Publication details
Date: 1 February 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Jason D. Williams, Nobal B. Niraula, Pradeep Dasigi, Aparna Lakshmiratan, Carlos Garcia Jurado Suarez, Mouni Reddy, and Geoff Zweig

In personal assistant dialog systems, intent models are classifiers that identify the intent of a user utterance, such as to add a meeting to a calendar, or get the director of a stated movie. Rapidly adding intents is one of the main bottlenecks to scaling — adding functionality to — personal assistants. In this paper we show how interactive learning can be applied to the creation of statistical intent models. Interactive learning [10] combines model definition, labeling, model...

Publication details
Date: 11 January 2015
Type: Inproceeding
Kevin Schelten, Sebastian Nowozin, Jeremy Jancsary, Carsten Rother, and Stefan Roth

Publication details
Date: 6 January 2015
Type: Inproceeding
Publisher: IEEE – Institute of Electrical and Electronics Engineers
Gao Huang, Jianwen Zhang, Shiji Song, and Zheng Chen

This paper proposes a new approach for discriminative clustering. The intuition is, for a good clustering, one should be able to learn a classifier from the clustering labels with high generalization accuracy. Thus we define a novel metric to evaluate the quality of a clustering labeling, named Minimum Separation Probability (MSP), which is a lower bound of the generalization accuracy of a classifier learnt from the clustering labeling. We take MSP as the objective to maximize and propose our...

Publication details
Date: 1 January 2015
Type: Inproceeding
Publisher: AAAI - Association for the Advancement of Artificial Intelligence
Nihar B. Shah and Dengyong Zhou

Human computation or crowdsourcing involves joint inference of the ground-truth-answers and the worker abilities by optimizing an objective function, for instance, by maximizing the data likelihood based on an assumed underlying model. A variety of methods have been proposed in the literature to address this inference problem. As far as we know, none of the objective functions in existing methods is convex. In machine learning and applied statistics, a convex function such as the objective function of...

Publication details
Date: 1 January 2015
Type: Inproceeding
Publisher: AAAI - Association for the Advancement of Artificial Intelligence
Shipra Agrawal and Nikhil R. Devanur

We introduce the online stochastic Convex Programming (CP) problem, a very general version of stochastic online problems which allows arbitrary concave objectives and convex feasibility constraints. Many well-studied problems like online stochastic packing and covering, online stochastic matching with concave returns, etc. form a special case of online stochastic CP. We present fast algorithms for these problems, which achieve near-optimal regret guarantees for both the i.i.d. and the...

Publication details
Date: 1 January 2015
Type: Inproceeding
Publisher: SIAM – Society for Industrial and Applied Mathematics
Xian-Sheng Hua and Jin Li

With the advances in distributed computation, machine learning and deep neural networks, we enter into an era that it is possible to build a real world image recognition system. There are three essential components to build a real-world image recognition system: 1) creating representative features, 2) de-signing powerful learning approaches, and 3) identifying massive training data. While extensive researches have been done on the first two aspects, much less attention has been paid on the third. In...

Publication details
Date: 1 January 2015
Type: Inproceeding
Publisher: AAAI - Association for the Advancement of Artificial Intelligence
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng

In this paper we present a unified framework for modeling multi-relational representations, scoring, and learning, and conduct an empirical study of several recent multi-relational embedding models under the framework. We investigate the different choices of relation operators based on linear and bilinear transformations, and also the effects of entity representations by incorporating unsupervised vectors pre-trained on extra textual resources. Our results show several interesting findings, enabling the...

Publication details
Date: 12 December 2014
Type: Inproceeding
Lin Xiao and Tong Zhang

We consider the problem of minimizing the sum of two convex functions: one is the average of a large number of smooth component functions, and the other is a general convex function that admits a simple proximal mapping. We assume the whole objective function is strongly convex. Such problems often arise in machine learning, known as regularized empirical risk minimization. We propose and analyze a new proximal stochastic gradient method, which uses a multistage scheme to progressively reduce the...

Publication details
Date: 1 December 2014
Type: Article
Publisher: SIAM – Society for Industrial and Applied Mathematics
Number: 4
Oleksandra Hararuk, Matthew J. Smith, and Yiqi Luo

Long-term carbon (C) cycle feedbacks to climate depend on the future dynamics of soil organic carbon (SOC). Current models show low predictive accuracy at simulating contemporary SOC pools, which can be improved through parameter estimation. However major uncertainty remains in global soil responses to climate change, particularly uncertainty in how the activity of soil microbial communities will respond. To date, the role of microbes in SOC dynamics has been implicitly described by decay rate constants...

Publication details
Date: 1 December 2014
Type: Article
Publisher: Wiley
Xiaohu Liu and Ruhi Sarikaya

Spoken language understanding (SLU) systems use various features to detect the domain, intent and semantic slots of a query. In addition to n-grams, features generated from entity dictionaries are often used in model training. Clean or properly weighted dictionaries are critical to improve model’s coverage and accuracy for unseen entities during test time. However, clean dictionaries are hard to obtain for some applications since they are automatically generated and can potentially contain millions of...

Publication details
Date: 1 December 2014
Type: Proceedings
Publisher: IEEE – Institute of Electrical and Electronics Engineers
Prateek Jain, Ambuj Tewari, and Purushottam Kar

The use of M-estimators in generalized linear regression models in high dimensional settings requires risk minimization with hard L0 constraints. Of the known methods, the class of projected gradient descent (also known as iterative hard thresholding (IHT)) methods is known to offer the fastest and most scalable solutions. However, the current state-of-the-art is only able to analyze these methods in very restrictive settings which do not hold in high dimensional statistical models. In this...

Publication details
Date: 1 December 2014
Type: Inproceeding
Publisher: Neural Information Processing Systems
Publication details
Date: 1 December 2014
Type: Inproceeding
Publisher: IEEE – Institute of Electrical and Electronics Engineers
Purushottam Kar, Harikrishna Narasimhan, and Prateek Jain

Modern applications in sensitive domains such as biometrics and medicine frequently require the use of non-decomposable loss functions such as precision@k, F-measure etc. Compared to point loss functions such as hinge-loss, these offer much more fine grained control over prediction, but at the same time present novel challenges in terms of algorithm design and analysis. In this work we initiate a study of online learning techniques for such non-decomposable loss functions with an aim to enable...

Publication details
Date: 1 December 2014
Type: Inproceeding
Publisher: Neural Information Processing Systems
Qi Li, Gokhan Tur, Dilek Hakkani-Tur, Xiang Li, Tim Paek, Asela Gunawardana, and Chris Quirk

Traditional spoken dialog systems are usually based on centralized architecture, in which the number of domains is predefined, and the provider is fixed for a given domain and intent. The spoken language understanding (SLU) component is responsible for detecting domain and intents, and filling domain-specific slots. It is expensive and time-consuming for this architecture to add new and/or competing domains, intents, or providers. The rapid growth of service providers in mobile computing market calls...

Publication details
Date: 1 December 2014
Type: Inproceeding
Publisher: IEEE – Institute of Electrical and Electronics Engineers
Kenton O'Hara, Gerardo Gonzalez, Abigail Sellen, Graeme Penney, Varnavas, Helena Mentis, Antonio Criminisi, Robert Corish, Mark Rouncefield, Neville Dastur, and Tom Carrell
Publication details
Date: 1 December 2014
Type: Article
James D. McCaffrey

The Python language is well-suited for creating neural network systems in hybrid-technology environments.

Publication details
Date: 15 November 2014
Type: Article
Michael J. Paul, Ryen W. White, and Eric Horvitz

We seek to understand the evolving needs of people who are faced with a life-changing medical diagnosis based on analyses of queries extracted from an anonymized search query log. Focusing on breast cancer, we manually tag a set of Web searchers as showing disruptive shifts in focus of attention and long-term patterns of search behavior consistent with the diagnosis and treatment of breast cancer. We build and apply probabilistic classifiers to detect these searchers from multiple sessions and to detect...

Publication details
Date: 15 November 2014
Type: Technical report
Publisher: Microsoft Research
Number: MSR-TR-2014-144
Katja Hofmann, Bhaskar Mitra, Filip Radlinski, and Milad Shokouhi

Query Auto Completion (QAC) suggests possible queries to web search users from the moment they start entering a query. This popular feature of web search engines is thought to reduce physical and cognitive effort when formulating a query.

Perhaps surprisingly, despite QAC being widely used, users’ interactions with it are poorly understood. This paper begins to address this gap. We present the results of an in-depth user study of user interactions with QAC in web search. While study participants...

Publication details
Date: 1 November 2014
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Publication details
Date: 1 November 2014
Type: Inproceeding
Publisher: CIKM
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