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Anshumali Srivastava, Arnd Christian König, and Misha Bilenko

Obtaining frequency information of data streams, in limited space, is a well-recognized problem in literature. A number of recent practical applications (such as those in computational advertising) require temporally-aware solutions: obtaining historical count statistics for both time-points as well as time-ranges. In these scenarios, accuracy of estimates is typically more important for recent instances than for older ones; we call this desirable property as ``Time Adaptiveness". With this...

Publication details
Date: 26 June 2016
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Royi Ronen, Gal Lavee, and Elad Yom-Tov

Collaborative filtering (CF) recommendation systems are one of the most popular and successful methods for recommending products to people. CF systems work by finding similarities between different people according to their past purchases, and using these similarities to suggest possible items of interest. Here we investigate how CF systems can be enhanced using Internet browsing data and search engine query logs, both of which represent a rich profile of individuals’ interests. We introduce two...

Publication details
Date: 16 May 2016
Type: Inproceeding
Publisher: IEEE – Institute of Electrical and Electronics Engineers
A. Ghosh, V. Wottschel, E. Kaden, J. Zhang, H. Zhang, S. N. Sotiropoulos, D. Zikic, T. B. Dyrby, A. Criminisi, and D. C. Alexander
Publication details
Date: 1 May 2016
Type: Inproceeding
Publication details
Date: 1 May 2016
Type: Inproceeding
Qingwei LIN, Jian-Guang LOU, Hongyu ZHANG, and Dongmei ZHANG
Publication details
Date: 1 May 2016
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Eric Nalisnick, Bhaskar Mitra, Nick Craswell, and Rich Caruana

This paper investigates the popular neural word embedding method Word2vec as a source of evidence in document ranking. In contrast to NLP applications of word2vec, which tend to use only the input embeddings, we retain both the input and the output embeddings, allowing us to calculate a different word similarity that may be more suitable for document ranking. We map the query words into the input space and the document words into the output space, and compute a relevance score by aggregating the cosine...

Publication details
Date: 11 April 2016
Type: Inproceeding
Publisher: WWW – World Wide Web Consortium (W3C)
Publication details
Date: 1 April 2016
Type: Inproceeding
Publisher: WWW – World Wide Web Consortium (W3C)
M. D'Souza, J. Burggraaff, S. Steinheimer, P. Kontschieder, C. Morrison, J. Dorn, P. Tewarie, K. Miciunaite, A. Sellen, A. Criminisi, C. Kamm, F. Dahlke, B. Uitdehaag, and L. Kappos
Publication details
Date: 1 April 2016
Type: Inproceeding
Publisher: Microsoft Research
Christian Daniel, Jonathan Taylor, and Sebastian Nowozin

This paper investigates algorithms to automatically adapt the learning rate of neural networks (NNs). Starting with stochastic gradient descent, a large variety of learning methods has been proposed for the NN setting. However, these methods are usually sensitive to the initial learning rate which has to be chosen by the experimenter. We investigate several features and show how an adaptive controller can adjust the learning rate without prior knowledge of the learning problem at hand.

Publication details
Date: 12 February 2016
Type: Inproceeding
Publisher: AAAI - Association for the Advancement of Artificial Intelligence
Bokai Cao, Hucheng Zhou, Guoqiang Li, and Philip S. Yu

With rapidly growing amount of data available on the web, it becomes increasingly likely to obtain data from different perspectives for multi-view learning. Some successive examples of web applications include recommendation and target advertising. Specifically, to predict whether a user will click an ad in a query context, there are available features extracted from user profile, ad information and query description, and each of them can only capture part of the task signals from a particular...

Publication details
Date: 1 February 2016
Type: Inproceeding
Publisher: WSDM
Weinan Zhang, Ulrich Paquet, and Katja Hofmann

We address the problem of learning behaviour policies to optimise online metrics from heterogeneous usage data. While online metrics, e.g., click-through rate, can be optimised effectively using exploration data, such data is costly to collect in practice, as it temporarily degrades the user experience. Leveraging related data sources to improve online performance would be extremely valuable, but is not possible using current approaches.

We formulate this task as a policy transfer learning...

Publication details
Date: 1 February 2016
Type: Inproceeding
Publisher: AAAI - Association for the Advancement of Artificial Intelligence
Zhaohui Wu, Yang Song, and C. Lee Giles

Continuously discovering novel entities in news and Web data is important for Knowledge Base (KB) maintenance. One of the key challenges is to decide whether an entity mention refers to an in-KB or out-of-KB entity. We propose a principled approach that learns a novel entity classifier by modeling mention and entity representation into multiple feature spaces, including contextual, topical, lexical, neural embedding and query spaces. Different from most previous studies that address novel entity...

Publication details
Date: 1 February 2016
Type: Inproceeding
Publisher: AAAI - Association for the Advancement of Artificial Intelligence
Ravi Mangal, Xin Zhang, Aditya Kamath, Aditya V. Nori, and Mayur Naik

Many inference problems are naturally formulated using hard and soft constraints over relational domains: the desired solution must satisfy the hard constraints, while optimizing the objectives expressed by the soft constraints. Existing techniques for solving such constraints rely on efficiently grounding a sufficient subset of constraints that is tractable to solve. We present an eager-lazy grounding algorithm that eagerly exploits proofs and lazily refutes counterexamples. We show that our algorithm...

Publication details
Date: 1 February 2016
Type: Inproceeding
Publisher: AAAI - Association for the Advancement of Artificial Intelligence
Publication details
Date: 1 February 2016
Type: Article
Irit Hochberg, Guy Feraru, Mark Kozdoba, Shie Mannor, Moshe Tennenholtz, and Elad Yom-Tov

Despite the clear benefit of regular physical activity, most patients with diabetes type 2 are sedentary. We provided 27 sedentary diabetes type 2 patients with a smartphone-based pedometer and a personal plan for physical activity. Patients were sent SMS messages to encourage physical activity between once a day to once a week. Messages were personalized through a Reinforcement Learning algorithm which optimized messages to improve each participant's compliance with the activity regimen. The...

Publication details
Date: 28 January 2016
Type: Article
Je Hyeong Hong and Andrew Fitzgibbon

Matrix factorization (or low-rank matrix completion) with missing data is a key computation in many computer vision and machine learning tasks, and is also related to a broader class of nonlinear optimization problems such as bundle adjustment. The problem has received much attention recently, with renewed interest in variable-projection approaches, yielding dramatic improvements in reliability and speed. However, on a wide class of problems, no one approach dominates, and because the various...

Publication details
Date: 15 December 2015
Type: Inproceeding
Publisher: IEEE – Institute of Electrical and Electronics Engineers
Yun-Nung Chen, Dilek Hakkani-Tur, and Xiaodong He

The recent success of voice interaction with smart devices (humanmachine genre) and improvements in speech recognition for conversational speech show the possibility of conversation-related applications. This paper investigates the task of actionable item detection in meetings (human-human genre), where the intelligent assistant dynamically provides the participants access to information (e.g. scheduling a meeting, taking notes) without interrupting the meetings. A convolutional deep structured semantic...

Publication details
Date: 12 December 2015
Type: Inproceeding
Publisher: IEEE – Institute of Electrical and Electronics Engineers
William D. Lewis, Christian Federmann, and Ying Xin

Cross Entropy Difference (CED) has proven to be a very effective method for selecting domain-specific data from large corpora of out-of-domain or general domain content. It is used in a number of different scenarios, and is particularly popular in bake-off competitions in which participants have a limited set of resources to draw from, and need to sub-sample the data in such a way as to ensure better results on domain-specific test sets. The underlying algorithm is handy since one can provide a set of...

Publication details
Date: 4 December 2015
Type: Inproceeding
Jianshu Chen, Ji He, Yelong Shen, Lin Xiao, Xiaodong He, Jianfeng Gao, Xinying Song, and Li Deng

We develop a fully discriminative learning approach for supervised Latent Dirichlet Allocation (LDA) model, which maximizes the posterior probability of the prediction variable given the input document. Different from traditional variational learning or Gibbs sampling approaches, the proposed learning method applies (i) the mirror descent algorithm for exact maximum a posterior inference and (ii) back propagation with stochastic gradient descent for model parameter estimation, leading to scalable...

Publication details
Date: 1 December 2015
Type: Inproceeding
Diane Bouchacourt, Sebastian Nowozin, and M. Pawan Kumar

Recently several generalizations of the popular latent structural SVM framework have been proposed in the literature. Broadly speaking, the generalizations can be divided into two categories: (i) those that predict the output variables while either marginalizing the latent variables or estimating their most likely values; and (ii) those that predict the output variables by minimizing an entropy-based uncertainty measure over the latent space. In order to aid their application in computer vision, we...

Publication details
Date: 1 December 2015
Type: Inproceeding
Publisher: IEEE – Institute of Electrical and Electronics Engineers
Jianlong Fu, Yue Wu, Tao Mei, Jinqiao Wang, Hanqing Lu, and Yong Rui

The development of deep learning has empowered machines with comparable capability of recognizing limited image categories to human beings. However, most existing approaches heavily rely on human-curated training data, which hinders the scalability to large and unlabeled vocabularies in image tagging. In this paper, we propose a weakly-supervised deep learning model which can be trained from the readily available Web images to relax the dependence on human labors and scale up to arbitrary tags...

Publication details
Date: 1 December 2015
Type: Inproceeding
Publisher: IEEE International Conference on Computer Vision
Tzu-Kuo Huang, Alekh Agarwal, Daniel Hsu, John Langford, and Robert Schapire
Publication details
Date: 1 December 2015
Type: Inproceeding
Asli Celikyilmaz and Dilek Hakkani-Tur

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 or phrases can be valuable. To encode the prior knowledge about the semantic word relations, we extended the neural network based lexical word embedding objective function by incorporating the information about relationship...

Publication details
Date: 1 December 2015
Type: Inproceeding
Publisher: NIPS Workshop on Machine Learning for SLU & Interaction
Sauleh Eetemadi, William Lewis, Kristina Toutanova, and Hayder Radha

Statistical machine translation has seen significant improvements in quality over the past several years. The single biggest factor in this improvement has been the accumulation of ever larger stores of data. We now find ourselves, however, the victims of our own success, in that it has become increasingly difficult to train on such large sets of data, due to limitations in memory, processing power, and ultimately, speed (i.e. data-to-models takes an inordinate amount of time). Moreover, the training...

Publication details
Date: 1 December 2015
Type: Article
Publisher: Springer
Chung-Kil Hur, Aditya V. Nori, Sriram K. Rajamani, and Selva Samuel

We consider the problem of inferring the implicit distribution specified by a probabilistic program.
A popular inference technique for probabilistic programs called Markov Chain Monte Carlo or
MCMC sampling involves running the program repeatedly and generating sample values by
perturbing values produced in “previous runs”. This simulates a Markov chain whose stationary
distribution is the distribution specified by the probabilistic program.
However, it is non-trivial to...

Publication details
Date: 1 December 2015
Type: Inproceeding
Publisher: Leibniz International Proceedings in Informatics
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