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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
Alekh Agarwal, Daniel Hsu, Satyen Kale, John Langford, Lihong Li, and Robert E. Schapire
Publication details
Date: 1 June 2014
Type: Inproceeding
Publication details
Date: 1 June 2014
Type: Inproceeding
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
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 correct...
Publication details
Date: 1 June 2014
Type: Inproceeding
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. We...
Publication details
Date: 1 June 2014
Type: Inproceeding
Publisher: ACM conference on Economics and Computation
Soroosh Mariooryad, Anitha Kannan, Dilek Hakkani-Tur, and Elizabeth Shriberg
Recent studies have shown the importance of using online videos along with textual material in educational instruction, especially for better content retention and improved concept understanding. A key question is how to select videos to maximize student engagement, particularly when there are multiple possible videos on the same topic. While there are many aspects that drive student engagement, in this paper we focus on presenter speaking styles in the video. We use crowd-sourcing to explore speaking...
Publication details
Date: 1 May 2014
Type: Article
Publisher: International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Varun Tulsian, Aditya Kanade, Rahul Kumar, Akash Lal, and Aditya V. Nori
With the growing complexity of modern day software, software model checking has become a critical technology for ensuring correctness of software. As is true with any promising technology, there are a number of tools for software model checking. However, their respective performance trade-offs are difficult to characterize accurately -- making it difficult for practitioners to select a suitable tool for the task at hand. This paper proposes a technique called MUX that addresses the problem of selecting the...
Publication details
Date: 1 May 2014
Type: Inproceeding
Publisher: ACM
Andrew D. Gordon, Thomas A. Henzinger, Aditya V. Nori, and Sriram K. Rajamani
Probabilistic programs are usual functional or imperative programs with two added constructs: (1) the ability to draw values at random from distributions, and (2) the ability to condition values of variables in a program via observations. Models from diverse application areas such as computer vision, coding theory, cryptographic protocols, biology and reliability analysis can be written as probabilistic programs. Probabilistic inference is the problem of computing an explicit representation of the...
Publication details
Date: 1 May 2014
Type: Inproceeding
Publisher: IEEE
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 multi-stage scheme to progressively reduce the variance...
Publication details
Date: 18 March 2014
Type: Technical report
Number: MSR-TR-2014-38
Rakesh Agrawal, Behzad Golshan, and Evimaria Terzi
Given a class of large number of students, each exhibiting a different ability level, how can we form teams of students so that the expected performance of team members improves due to team participation? We take a computational perspective and formally define two versions of such team-formation problem: the MaxTeam and the MaxPartition problems. The first asks for the identification of a single team of students that improves the performance of most of the participating team members. The second asks for a...
Publication details
Date: 1 March 2014
Type: Inproceeding
Publisher: ACM
James Bornholt, Todd Mytkowicz, and Kathryn S. McKinley
Emerging applications increasingly use estimates such as sensor data (GPS), probabilistic models, machine learning, big data, and human data. Unfortunately, representing this uncertain data with discrete types (floats, integers, and booleans) encourages developers to pretend it is not probabilistic, which causes three types of uncertainty bugs. (1) Using estimates as facts ignores random error in estimates. (2) Computation compounds that error. (3) Boolean questions on probabilistic data induce false...
Publication details
Date: 1 March 2014
Type: Inproceeding
Publisher: Architectural Support for Programming Languages and Operating Systems (ASPLOS)
Yang Song, Weiwei Cui, Shixia Liu, and Kuansan Wang
We present a system to analyze user interests by analyzing their online behaviors from large-scale usage logs. We surmise that user interests can be characterized by a large collection of features we call the behavioral genes that can be deduced from both their explicit and implicit online behaviors. It is the goal of this research to sequence the entire behavioral genome for online population, namely, to identify the pertinent behavioral genes and uncover their relationships in explaining and predicting...
Publication details
Date: 1 March 2014
Type: Inproceeding
Publisher: ACM
Yang Song, Hongning Wang, and Xiaodong He
RankNet is one of the widely adopted ranking models for web search tasks. However, adapting a generic RankNet for personalized search is little studied. In this paper, we first trained a variety of RankNets with different number of hidden layers and network structures on a per-user basis, and observed that a deep neural network with five hidden layers gives the best performance. To further improve the performance of adaptation, we propose a set of novel methods categorized into two groups. In the first...
Publication details
Date: 1 February 2014
Type: Inproceeding
Publisher: ACM
Matthew J. Smith, Paul I. Palmer, Drew W. Purves, Mark C. Vanderwel, Vassily Lyutsarev, Ben Calderhead, Lucas N. Joppa, Christopher M. Bishop, and Stephen Emmott
New details about natural and anthropogenic processes are continually added to models of the Earth System, anticipating that the increased realism will increase the accuracy of their predictions. However perspectives differ about whether this approach will improve the value of the information the models provide to decision makers, scientists and societies. The present bias towards increasing realism leads to a range of updated projections, but at the expense of uncertainty quantification and model...
Publication details
Date: 1 February 2014
Type: Article
Publisher: American Meteorological Society
Publication details
Date: 1 February 2014
Type: Article
Lu Wang, Larry Heck, and Dilek Hakkani-Tur
Training statistical dialog models in spoken dialog systems (SDS) requires large amounts of annotated data. The lack of scalable methods for data mining and annotation poses a significant hurdle for state-of-the-art SDS. This paper presents an approach that directly leverage billions of web search and browse sessions to overcome this hurdle. The key insight is that task completion through web search and browse sessions is (a) predictable and (b) generalizes to spoken dialog task completion. The new...
Publication details
Date: 1 January 2014
Type: Inproceeding
Publisher: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
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 consumer satisfaction. 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 develop the method through minimax conditional entropy subject to...
Publication details
Date: 1 January 2014
Type: Inproceeding
Seyed Omid Sadjadi and Larry Heck
Co-channel speech, which occurs in monaural audio recordings of two or more overlapping talkers, poses a great challenge for automatic speech applications. Automatic speech recognition (ASR) performance, in particular, has been shown to degrade significantly in the presence of a competing talker. In this paper, assuming a known target talker scenario, we present two different masking strategies based on speaker verification to alleviate the impact of the competing talker (a.k.a. masker) interference on ASR...
Publication details
Date: 1 January 2014
Type: Inproceeding
Publisher: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Publication details
Date: 1 December 2013
Type: Proceedings
Publisher: IEEE Workshop on Spoken Language Technology
Kaisheng Yao, Baolin Peng, Geoffrey Zweig, Dong Yu, Xiaolong Li, and Feng Gao
Publication details
Date: 1 December 2013
Type: Inproceeding
Rakesh Agrawal
This paper summarizes the results of our recent investigations into how information propagates, how people assimilate information, and how people form relationships to gain information in Internet-centric social settings. It includes key ideas related to the role of the nature of information items in information diffusion as well as the notion of receptivity on part of the receiver and how it affects information assimilation and opinion formation. It describes a system that incorporates availability,...
Publication details
Date: 1 November 2013
Type: Technical report
Publisher: Microsoft Technical Report
Number: MSR-TR-2013-115
Allison Chaney, Mike Gartrell, Jake Hofman, John Guiver, Noam Koenigstein, Pushmeet Kohli, and Ulrich Paquet
We present a large-scale study of television viewing habits, focusing on how individuals adapt their preferences when consuming content in group settings. While there has been a great deal of recent work on modeling individual preferences, there has been considerably less work studying the behavior and preferences of groups, due mostly to the difficulty of data collection in these settings. In contrast to past work that has relied either on small-scale surveys or prototypes, we explore more than 4 million...
Publication details
Date: 1 November 2013
Type: Technical report
Publisher: Microsoft Technical Report
Number: MSR-TR-2013-114
S. M. Ali Eslami, Nicolas Heess, Christopher K. I. Williams, and John Winn
A good model of object shape is essential in applications such as segmentation, detection, inpainting and graphics. For example, when performing segmentation, local constraints on the shapes can help where object boundaries are noisy or unclear, and global constraints can resolve ambiguities where background clutter looks similar to parts of the objects. In general, the stronger the model of shape, the more performance is improved. In this paper, we use a type of deep Boltzmann machine (Salakhutdinov and...
Publication details
Date: 1 November 2013
Type: Article
Publisher: Springer
Michael Gamon, Tae Yano, Xinying Song, Johnson Apacible, and Patrick Pantel
We propose a system that determines the salience of entities within web documents. Many recent advances in commercial search engines leverage the identification of entities in web pages. However, for many pages, only a small subset of entities are central to the document, which can lead to degraded relevance for entity triggered experiences. We address this problem by devising a system that scores each entity on a web page according to its centrality to the page content. We propose salience classification...
Publication details
Date: 1 November 2013
Type: Inproceeding
Publisher: ACM International Conference on Information and Knowledge Management (CIKM)
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