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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
Publication details
Date: 1 December 2015
Type: Article
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
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 and language models learn 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 a set of over 400,000 image...

Publication details
Date: 1 June 2015
Type: Article
Publisher: IEEE – Institute of Electrical and Electronics Engineers
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)
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
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
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
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
刘树杰, 董力, 张家俊, 韦福如, 李沐, and 周明
Publication details
Date: 1 April 2015
Type: Article
Toby Sharp, Cem Keskin, Duncan Robertson, Jonathan Taylor, Jamie Shotton, David Kim, Christoph Rhemann, Ido Leichter, Alon Vinnikov, Yichen Wei, Daniel Freedman, Pushmeet Kohli, Eyal Krupka, Andrew Fitzgibbon, and Shahram Izadi

VIDEO: https://www.youtube.com/watch?v=A-xXrMpOHyc

Paper and Abstract coming soon.

Publication details
Date: 1 April 2015
Type: Inproceeding
Publisher: CHI
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
Grégoire Mesnil, Yann Dauphin, Kaisheng Yao, Yoshua Bengio, Li Deng, Dilek Hakkani-Tur, Xiaodong He, Larry Heck, Gokhan Tur, Dong Yu, and Geoffrey Zweig

Semantic slot filling is one of the most challenging problems in spoken language understanding (SLU). In this paper, we propose to use recurrent neural networks (RNNs) for this task, and present several novel architectures designed to efficiently model past and future temporal dependencies. Specifically, we implemented and compared several important RNN architectures, including Elman, Jordan, and hybrid variants. To facilitate reproducibility, we implemented these networks with the publicly available...

Publication details
Date: 1 March 2015
Type: Article
Publisher: IEEE – Institute of Electrical and Electronics Engineers
Elad Yom-Tov, Ingemar Johansson Cox, and Vasileios Lampos

Surveys show that around 70% of US Internet users consult the Internet when they require medical information. People seek this information using both traditional search engines and via social media. The information created using the search process offers an unprecedented opportunity for applications to monitor and improve the quality of life of people with a variety of medical conditions. In recent years, research in this area has addressed public-health questions such as the effect of media on...

Publication details
Date: 2 February 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Dan Alistarh, Jennifer Iglesias, and Milan Vojnovic

In many applications, the structure of data can be represented by a hyper-graph, where the data items are vertices, and the associations among items are represented by hyper-edges. Equivalently, we are given as input a bipartite graph with two kinds of vertices: items, and associations (which we refer to as topics). We consider the problem of partitioning the set of items into a given number of partitions, such that the maximum number of topics covered by a partition is minimized.

This is a...

Publication details
Date: 1 February 2015
Type: Technical report
Publisher: Microsoft Research
Number: MSR-TR-2015-15
Lihong Li

A tutorial given at WSDM'15, the Eighth ACM International Conference on Web Search and Data Mining, Shanghai, China, February 6, 2015

Publication details
Date: 1 February 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Asli Celikyilmaz and Dilek Hakkani-Tur

While ensemble models have proven useful for sequence learning tasks there is relatively fewer work that provide insights into what makes them powerful. In this paper, we investigate the empirical behavior of the ensemble approaches on sequence modeling, specifically for the semantic tagging task. We explore this by comparing the performance of commonly used and easy to implement ensemble methods such as majority voting, linear combination and stacking to a learning based and rather complex ensemble...

Publication details
Date: 1 February 2015
Type: Proceedings
Publisher: IEEE – Institute of Electrical and Electronics Engineers
Hamid Palangi, Li Deng, Yelong Shen, Jianfeng Gao, Xiaodong He, Jianshu Chen, Xinying Song, and Rabab Ward

This paper develops a model that addresses sentence embedding using recurrent neural networks (RNN) with Long Short Term Memory (LSTM) cells. The proposed LSTM-RNN model sequentially takes each word in a sentence, extracts its information, and embeds it into a semantic vector. Due to its ability to capture long term memory, the LSTM-RNN accumulates increasingly richer information as it goes through the sentence, and when it reaches the last word, the hidden layer of the network provides a semantic...

Publication details
Date: 1 February 2015
Type: Article
Publisher: arXiv
Publication details
Date: 1 February 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Asli Celikyilmaz, Dilek Hakkani-Tur, Panupong Pasupat, and Ruhi Sarikaya

Unsupervised word embeddings provide rich linguistic and conceptual information about words. However, they may provide weak information about domain specific semantic relations for certain tasks such as semantic parsing of natural language queries, where such information about words can be valuable. To encode the prior knowledge about the semantic word relations, we present new method as follows: we extend the neural network based lexical word embedding objective function (Mikolov et al. 2013) by...

Publication details
Date: 19 January 2015
Type: Inproceeding
Publisher: AAAI - Association for the Advancement of Artificial Intelligence
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
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
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