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Li Deng and John C. Platt

Deep learning systems have dramatically improved the accuracy of speech recognition, and various deep architectures and learning methods have been developed with distinct strengths and weaknesses in recent years. How can ensemble learning be applied to these varying deep learning systems to achieve greater recognition accuracy is the focus of this paper. We develop and report linear and log-linear stacking methods for ensemble learning with applications specifically to speechclass posterior...

Publication details
Date: 1 September 2014
Type: Inproceeding
Publisher: Proc. Interspeech
Yanjie Fu, Hui Xiong, Yong Ge, Zijun Yao, Yu Zheng, and Zhi-Hua Zhou

It is traditionally a challenge for home buyers to understand, compare and contrast the investment values of real estates. While a number of estate appraisal methods have been developed to value real property, the performances of these methods have been limited by the traditional data sources for estate appraisal. However, with the development of new ways of collecting estate-related mobile data, there is a potential to leverage geographic dependencies of estates for enhancing estate appraisal. Indeed,...

Publication details
Date: 1 August 2014
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Yang Song, Xiaolin Shi, Ryen White, and Ahmed Hassan
Publication details
Date: 1 July 2014
Type: Inproceeding
Publisher: ACM
Aditya V. Nori, Chung-Kil Hur, Sriram K. Rajamani, and Selva Samuel

We present a new Markov Chain Monte Carlo (MCMC) sampling algorithm for probabilistic programs. Our approach and tool, called R2, has the unique feature of employing program analysis in order to improve the efficiency of MCMC sampling. Given an input program P, R2 propagates observations in P backwards to obtain a semantically equivalent program P0 in which every probabilistic assignment is immediately followed by an observe statement. Inference is performed by a suitably modified version of the...

Publication details
Date: 1 July 2014
Type: Inproceeding
Publisher: AAAI
Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen

We deal with embedding a large scale knowledge graph composed of entities and relations into a continuous vector space. TransE is a promising method proposed recently, which is very efficient while achieving state-of-the-art predictive performance. We discuss some mapping properties of relations which should be considered in embedding, such as reflexive, one-to-many, many-to-one, and many-to-many. We note that TransE does not do well in dealing with these properties. Some complex models are capable of...

Publication details
Date: 1 July 2014
Type: Inproceeding
Publisher: AAAI - Association for the Advancement of Artificial Intelligence
David Heckerman, Christopher Meek, and Thomas S. Richardson

We compare alternative definitions of undirected graphical models for discrete, finite variables. Lauritzen (1996) provides several definitions of such models and describes their relationships. He shows that the definitions agree only when joint distributions represented by the models are limited to strictly positive distributions. Heckerman et al. (2000), in their paper on dependency networks, describe another definition of undirected graphical models for strictly positive distributions. They show that...

Publication details
Date: 1 July 2014
Type: Article
Publisher: Kybernetika
Tianbing Xu, Jianfeng Gao, Lin Xiao, and Amelia C. Regan

We propose a voted dual averaging method for online classification problems with explicit regularization. This method employs the update rule of the regularized dual averaging (RDA) method proposed by Xiao, but only on the subsequence of training examples where a classification error is made. We derive a bound on the number of mistakes made by this method on the training set, as well as its generalization error rate. We also introduce the concept of relative strength of regularization, and show how it...

Publication details
Date: 1 July 2014
Type: Proceedings
Publisher: AAAI
Qihang Lin, Zhaosong Lu, and Lin Xiao

We consider the problem of minimizing the sum of two convex functions: one is smooth and given by a gradient oracle, and the other is separable over blocks of coordinates and has a simple known structure over each block. We develop an accelerated randomized proximal coordinate gradient (APCG) method for minimizing such convex composite functions. For strongly convex functions, our method achieves faster linear convergence rates than existing randomized proximal coordinate gradient methods. Without...

Publication details
Date: 1 July 2014
Type: Technical report
Publisher: Microsoft Research
Number: MSR-TR-2014-94
Hamid Palangi, Li Deng, and Rabab K Ward

Deep Stacking Networks (DSNs) are constructed by stacking shallow feed-forward neural networks on top of each other using concatenated features derived from the lower modules of the DSN and the raw input data. DSNs do not have recurrent connections, making them less effective to model and classify input data with temporal dependencies. In this paper, we embed recurrent connections into the DSN, giving rise to Recurrent Deep Stacking Networks (R-DSNs). Each module of the R-DSN consists of a special form...

Publication details
Date: 1 July 2014
Type: Inproceeding
Publisher: IEEE Conference ChinaSIP
Long Tran-Thanh, Lampros Stavrogiannis, Victor Naroditskiy, Valentin Robu, Nicholas R Jennings, and Peter Key

We study the problem of an advertising agent who needs to intelligently distribute her budget across a sequence of online keyword bidding auctions. We assume the closing price of each auction is governed by the same unknown distribution, and study the problem of making provably optimal bidding decisions. Learning the distribution is done under censored observations, i.e. the closing price of an auction is revealed only if the bid we place is above it. We consider three algorithms, namely...

Publication details
Date: 1 July 2014
Type: Inproceeding
Publisher: AUAI
Hongning Wang, Yang Song, Ming-Wei Chang, Xiaodong He, Ahmed Hassan, and Ryen White
Publication details
Date: 1 July 2014
Type: Proceedings
Publisher: ACM
Tomáš Kocák, Michal Valko, Rémi Munos, and Shipra Agrawal
Publication details
Date: 1 July 2014
Type: Article
Publisher: AAAI - Association for the Advancement of Artificial Intelligence
Maxime Oquab, Leon Bottou, Ivan Laptev, and Josef Sivic

Convolutional neural networks (CNN) have recently shown outstanding image classification performance in the largescale visual recognition challenge (ILSVRC2012). The success of CNNs is attributed to their ability to learn rich midlevel image representations as opposed to hand-designed low-level features used in other image classification methods. Learning CNNs, however, amounts to estimating millions of parameters and requires a very large number of annotated image samples. This property currently...

Publication details
Date: 24 June 2014
Type: Proceedings
Publisher: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)
Minjie Wang, Hucheng Zhou, Minyi Guo, and Zheng Zhang

This paper addresses the problem of model synchronization in data-parallelism of deep-learning systems. In such systems, workers on different machines continuously update their local copies of the model, and the updates need to be merged so that the copies are roughly consistent to each other. In modern implementations using GPUs, workers generate very high updates, posing significant scalability challenges.

We model this as a distributed state anti-entropy problem, and propose a fully...

Publication details
Date: 24 June 2014
Type: Inproceeding
Publisher: APSys 2014
Alekh Agarwal, Daniel Hsu, Satyen Kale, John Langford, Lihong Li, and Robert E. Schapire
Publication details
Date: 1 June 2014
Type: Inproceeding
Qihang Lin and Lin Xiao

We consider optimization problems with an objective function that is the sum of two convex terms: one is smooth and given by a black-box oracle, and the other is general but with a simple, known structure. We first present an accelerated proximal gradient (APG) method for problems where the smooth part of the objective function is also strongly convex. This method incorporates an efficient line-search procedure, and achieves the optimal iteration complexity for such composite optimization problems. In...

Publication details
Date: 1 June 2014
Type: Inproceeding
Dengyong Zhou, Qiang Liu, John C. Platt, and Christopher Meek

We propose a method to aggregate noisy ordinal labels collected from a crowd of workers or annotators. Eliciting ordinal labels is important in tasks such as judging web search quality and rating products. Our method is motivated by the observation that workers usually have difficulty distinguishing between two adjacent ordinal classes whereas distinguishing between two classes which are far away from each other is much easier. We formulate our method as minimax conditional entropy subject to...

Publication details
Date: 1 June 2014
Type: Inproceeding
Chung-Kil Hur, Aditya V. Nori, Sriram K. Rajamani, and Selva Samuel

Probabilistic programs use familiar notation of programming languages to specify probabilistic models. Suppose we are interested in estimating the distribution or expectation of a return expression r of a probabilistic program P. We are interested in slicing the probabilistic program P and obtain a simpler program SLI(P) which retains only those parts of P that are relevant to estimating r, and elides those parts P that are not relevant to estimating r. We desire that the SLI transformation be both...

Publication details
Date: 1 June 2014
Type: Inproceeding
Publisher: ACM
Jianfeng Gao, Xiaodong He, Wen-tau Yih, and Li Deng

This paper tackles the sparsity problem in estimating phrase translation probabilities by learning continuous phrase representations, whose distributed nature enables the sharing of related phrases in their repre-sentations. A pair of source and target phrases are projected into continuous-valued vector representations in a low-dimensional latent space, where their translation score is computed by the distance between the pair in this new space. The projection is performed by a neural network whose...

Publication details
Date: 1 June 2014
Type: Inproceeding
Publisher: Association for Computational Linguistics
Sebastian Nowozin

A probabilistic model allows us to reason about the world and make statistically optimal decisions using Bayesian decision theory. However, in practice the intractability of the decision problem forces us to adopt simplistic loss functions such as the 0/1 loss or Hamming loss and as result we make poor decisions through MAP estimates or through low-order marginal statistics. In this work we investigate optimal decision making for more realistic loss functions. Specifically we consider the popular...

Publication details
Date: 1 June 2014
Type: Inproceeding
Publisher: IEEE Computer Society
Yoram Bachrach, Sofia Ceppi, Ian A. Kash, Peter Key, and David Kurokawa

We examine trade-offs among stakeholders in ad auctions. Our metrics are the revenue for the utility of the auctioneer, the number of clicks for the utility of the users and the welfare for the utility of the advertisers. We show how to optimize linear combinations of the stakeholder utilities, showing that these can be tackled through a GSP auction with a per-click reserve price. We then examine constrained optimization of stakeholder utilities.

We use simulations and analysis of real-world...

Publication details
Date: 1 June 2014
Type: Proceedings
Publisher: ACM
Shipra Agrawal and Nikhil R. Devanur

In this paper, we consider a very general model for exploration-exploitation tradeoff which allows arbitrary concave rewards and convex constraints on the decisions across time, in addition to the customary limitation on the time horizon. This model subsumes the classic multi-armed bandit (MAB) model, and the Bandits with Knapsacks (BwK) model of Badanidiyuru et al.[2013]. We also consider an extension of this model to allow linear contexts, similar to the linear contextual extension of the MAB model....

Publication details
Date: 1 June 2014
Type: Inproceeding
Publisher: ACM conference on Economics and Computation
Alejandro Alanis, Trang Thai, Gerald Dejean, Ran Gilad-Bachrach, and Dimitrios Lymberopoulos

Human interaction with devices is constrained to the surface of these devices through widely used touch sensors. In this work, we enable touchless interfaces that allow humans to interact with devices from a distance. Our approach is based on the design of a two-dimensional array of RF sensors specifically designed to detect the proximity of human body. Each sensor in the array acts as a near-field RF proximity sensor. When parts of the human body come to close proximity to the sensor, they slightly...

Publication details
Date: 1 June 2014
Type: Technical report
Publisher: Microsoft Research Technical Report
Number: MSR-TR-2014-81
Publication details
Date: 1 June 2014
Type: Inproceeding
Publisher: Journal of Machine Learning Research
Wen-tau Yih, Xiaodong He, and Christopher Meek

We develop a semantic parsing framework based on semantic similarity for open domain question answering (QA). We focus on single-relation questions and decompose each question into an entity mention and a relation pattern. Using convolutional neural network models, we measure the similarity of entity mentions with entities in the knowledge base (KB) and the similarity of relation patterns and relations in the KB. We score relational triples in the KB using these measures and select the top scoring...

Publication details
Date: 1 June 2014
Type: Inproceeding
Publisher: Association for Computational Linguistics
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