Paper Group NANR 224
Split LBI: An Iterative Regularization Path with Structural Sparsity. Report of NEWS 2016 Machine Transliteration Shared Task. A Bayesian method for reducing bias in neural representational similarity analysis. Temporal Modelling of Geospatial Words in Twitter. Summarizing Multi-Party Argumentative Conversations in Reader Comment on News. Recognizi …
Split LBI: An Iterative Regularization Path with Structural Sparsity
| Title | Split LBI: An Iterative Regularization Path with Structural Sparsity |
| Authors | Chendi Huang, Xinwei Sun, Jiechao Xiong, Yuan Yao |
| Abstract | An iterative regularization path with structural sparsity is proposed in this paper based on variable splitting and the Linearized Bregman Iteration, hence called \emph{Split LBI}. Despite its simplicity, Split LBI outperforms the popular generalized Lasso in both theory and experiments. A theory of path consistency is presented that equipped with a proper early stopping, Split LBI may achieve model selection consistency under a family of Irrepresentable Conditions which can be weaker than the necessary and sufficient condition for generalized Lasso. Furthermore, some $\ell_2$ error bounds are also given at the minimax optimal rates. The utility and benefit of the algorithm are illustrated by applications on both traditional image denoising and a novel example on partial order ranking. |
| Tasks | Denoising, Image Denoising, Model Selection |
| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6288-split-lbi-an-iterative-regularization-path-with-structural-sparsity |
| http://papers.nips.cc/paper/6288-split-lbi-an-iterative-regularization-path-with-structural-sparsity.pdf | |
| PWC | https://paperswithcode.com/paper/split-lbi-an-iterative-regularization-path |
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Report of NEWS 2016 Machine Transliteration Shared Task
| Title | Report of NEWS 2016 Machine Transliteration Shared Task |
| Authors | Xiangyu Duan, Rafael Banchs, Min Zhang, Haizhou Li, A. Kumaran |
| Abstract | |
| Tasks | Information Retrieval, Machine Translation, Transliteration |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-2709/ |
| https://www.aclweb.org/anthology/W16-2709 | |
| PWC | https://paperswithcode.com/paper/report-of-news-2016-machine-transliteration |
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A Bayesian method for reducing bias in neural representational similarity analysis
| Title | A Bayesian method for reducing bias in neural representational similarity analysis |
| Authors | Ming Bo Cai, Nicolas W. Schuck, Jonathan W. Pillow, Yael Niv |
| Abstract | In neuroscience, the similarity matrix of neural activity patterns in response to different sensory stimuli or under different cognitive states reflects the structure of neural representational space. Existing methods derive point estimations of neural activity patterns from noisy neural imaging data, and the similarity is calculated from these point estimations. We show that this approach translates structured noise from estimated patterns into spurious bias structure in the resulting similarity matrix, which is especially severe when signal-to-noise ratio is low and experimental conditions cannot be fully randomized in a cognitive task. We propose an alternative Bayesian framework for computing representational similarity in which we treat the covariance structure of neural activity patterns as a hyper-parameter in a generative model of the neural data, and directly estimate this covariance structure from imaging data while marginalizing over the unknown activity patterns. Converting the estimated covariance structure into a correlation matrix offers a much less biased estimate of neural representational similarity. Our method can also simultaneously estimate a signal-to-noise map that informs where the learned representational structure is supported more strongly, and the learned covariance matrix can be used as a structured prior to constrain Bayesian estimation of neural activity patterns. Our code is freely available in Brain Imaging Analysis Kit (Brainiak) (https://github.com/IntelPNI/brainiak), a python toolkit for brain imaging analysis. |
| Tasks | |
| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6131-a-bayesian-method-for-reducing-bias-in-neural-representational-similarity-analysis |
| http://papers.nips.cc/paper/6131-a-bayesian-method-for-reducing-bias-in-neural-representational-similarity-analysis.pdf | |
| PWC | https://paperswithcode.com/paper/a-bayesian-method-for-reducing-bias-in-neural |
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Temporal Modelling of Geospatial Words in Twitter
| Title | Temporal Modelling of Geospatial Words in Twitter |
| Authors | Bo Han, Antonio Jimeno Yepes, Andrew MacKinlay, Lianhua Chi |
| Abstract | |
| Tasks | Language Modelling |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/U16-1015/ |
| https://www.aclweb.org/anthology/U16-1015 | |
| PWC | https://paperswithcode.com/paper/temporal-modelling-of-geospatial-words-in |
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Summarizing Multi-Party Argumentative Conversations in Reader Comment on News
| Title | Summarizing Multi-Party Argumentative Conversations in Reader Comment on News |
| Authors | Emma Barker, Robert Gaizauskas |
| Abstract | |
| Tasks | Argument Mining |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-2802/ |
| https://www.aclweb.org/anthology/W16-2802 | |
| PWC | https://paperswithcode.com/paper/summarizing-multi-party-argumentative |
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Recognizing the Absence of Opposing Arguments in Persuasive Essays
| Title | Recognizing the Absence of Opposing Arguments in Persuasive Essays |
| Authors | Christian Stab, Iryna Gurevych |
| Abstract | |
| Tasks | Argument Mining, Document Classification |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-2813/ |
| https://www.aclweb.org/anthology/W16-2813 | |
| PWC | https://paperswithcode.com/paper/recognizing-the-absence-of-opposing-arguments |
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Error Analysis of Generalized Nyström Kernel Regression
| Title | Error Analysis of Generalized Nyström Kernel Regression |
| Authors | Hong Chen, Haifeng Xia, Heng Huang, Weidong Cai |
| Abstract | Nystr"{o}m method has been used successfully to improve the computational efficiency of kernel ridge regression (KRR). Recently, theoretical analysis of Nystr"{o}m KRR, including generalization bound and convergence rate, has been established based on reproducing kernel Hilbert space (RKHS) associated with the symmetric positive semi-definite kernel. However, in real world applications, RKHS is not always optimal and kernel function is not necessary to be symmetric or positive semi-definite. In this paper, we consider the generalized Nystr"{o}m kernel regression (GNKR) with $\ell_2$ coefficient regularization, where the kernel just requires the continuity and boundedness. Error analysis is provided to characterize its generalization performance and the column norm sampling is introduced to construct the refined hypothesis space. In particular, the fast learning rate with polynomial decay is reached for the GNKR. Experimental analysis demonstrates the satisfactory performance of GNKR with the column norm sampling. |
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| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6602-error-analysis-of-generalized-nystrom-kernel-regression |
| http://papers.nips.cc/paper/6602-error-analysis-of-generalized-nystrom-kernel-regression.pdf | |
| PWC | https://paperswithcode.com/paper/error-analysis-of-generalized-nystrom-kernel |
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Learning to Recognize Ancillary Information for Automatic Paraphrase Identification
| Title | Learning to Recognize Ancillary Information for Automatic Paraphrase Identification |
| Authors | Simone Filice, Aless Moschitti, ro |
| Abstract | |
| Tasks | Paraphrase Identification |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-1129/ |
| https://www.aclweb.org/anthology/N16-1129 | |
| PWC | https://paperswithcode.com/paper/learning-to-recognize-ancillary-information |
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FBK HLT-MT at SemEval-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word Embeddings
| Title | FBK HLT-MT at SemEval-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word Embeddings |
| Authors | Duygu Ataman, Jos{'e} G. C. de Souza, Marco Turchi, Matteo Negri |
| Abstract | |
| Tasks | Cross-Lingual Semantic Textual Similarity, Machine Translation, Natural Language Inference, Paraphrase Identification, Semantic Similarity, Semantic Textual Similarity, Sentiment Analysis, Word Embeddings, Word Sense Disambiguation |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1086/ |
| https://www.aclweb.org/anthology/S16-1086 | |
| PWC | https://paperswithcode.com/paper/fbk-hlt-mt-at-semeval-2016-task-1-cross |
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Maximization of Approximately Submodular Functions
| Title | Maximization of Approximately Submodular Functions |
| Authors | Thibaut Horel, Yaron Singer |
| Abstract | We study the problem of maximizing a function that is approximately submodular under a cardinality constraint. Approximate submodularity implicitly appears in a wide range of applications as in many cases errors in evaluation of a submodular function break submodularity. Say that $F$ is $\eps$-approximately submodular if there exists a submodular function $f$ such that $(1-\eps)f(S) \leq F(S)\leq (1+\eps)f(S)$ for all subsets $S$. We are interested in characterizing the query-complexity of maximizing $F$ subject to a cardinality constraint $k$ as a function of the error level $\eps > 0$. We provide both lower and upper bounds: for $\eps > n^{-1/2}$ we show an exponential query-complexity lower bound. In contrast, when $\eps < {1}/{k}$ or under a stronger bounded curvature assumption, we give constant approximation algorithms. |
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| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6236-maximization-of-approximately-submodular-functions |
| http://papers.nips.cc/paper/6236-maximization-of-approximately-submodular-functions.pdf | |
| PWC | https://paperswithcode.com/paper/maximization-of-approximately-submodular |
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Automatic Prediction of Linguistic Decline in Writings of Subjects with Degenerative Dementia
| Title | Automatic Prediction of Linguistic Decline in Writings of Subjects with Degenerative Dementia |
| Authors | Davy Weissenbacher, Travis A. Johnson, Laura Wojtulewicz, Amylou Dueck, Dona Locke, Richard Caselli, Graciela Gonzalez |
| Abstract | |
| Tasks | |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-1143/ |
| https://www.aclweb.org/anthology/N16-1143 | |
| PWC | https://paperswithcode.com/paper/automatic-prediction-of-linguistic-decline-in |
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Scaled Least Squares Estimator for GLMs in Large-Scale Problems
| Title | Scaled Least Squares Estimator for GLMs in Large-Scale Problems |
| Authors | Murat A. Erdogdu, Lee H. Dicker, Mohsen Bayati |
| Abstract | We study the problem of efficiently estimating the coefficients of generalized linear models (GLMs) in the large-scale setting where the number of observations $n$ is much larger than the number of predictors $p$, i.e. $n\gg p \gg 1$. We show that in GLMs with random (not necessarily Gaussian) design, the GLM coefficients are approximately proportional to the corresponding ordinary least squares (OLS) coefficients. Using this relation, we design an algorithm that achieves the same accuracy as the maximum likelihood estimator (MLE) through iterations that attain up to a cubic convergence rate, and that are cheaper than any batch optimization algorithm by at least a factor of $\mathcal{O}(p)$. We provide theoretical guarantees for our algorithm, and analyze the convergence behavior in terms of data dimensions. % Finally, we demonstrate the performance of our algorithm through extensive numerical studies on large-scale real and synthetic datasets, and show that it achieves the highest performance compared to several other widely used optimization algorithms. |
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| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6522-scaled-least-squares-estimator-for-glms-in-large-scale-problems |
| http://papers.nips.cc/paper/6522-scaled-least-squares-estimator-for-glms-in-large-scale-problems.pdf | |
| PWC | https://paperswithcode.com/paper/scaled-least-squares-estimator-for-glms-in |
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CNN- and LSTM-based Claim Classification in Online User Comments
| Title | CNN- and LSTM-based Claim Classification in Online User Comments |
| Authors | Chinnappa Guggilla, Tristan Miller, Iryna Gurevych |
| Abstract | When processing arguments in online user interactive discourse, it is often necessary to determine their bases of support. In this paper, we describe a supervised approach, based on deep neural networks, for classifying the claims made in online arguments. We conduct experiments using convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) on two claim data sets compiled from online user comments. Using different types of distributional word embeddings, but without incorporating any rich, expensive set of features, we achieve a significant improvement over the state of the art for one data set (which categorizes arguments as factual vs. emotional), and performance comparable to the state of the art on the other data set (which categorizes propositions according to their verifiability). Our approach has the advantages of using a generalized, simple, and effective methodology that works for claim categorization on different data sets and tasks. |
| Tasks | Sarcasm Detection, Word Embeddings |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1258/ |
| https://www.aclweb.org/anthology/C16-1258 | |
| PWC | https://paperswithcode.com/paper/cnn-and-lstm-based-claim-classification-in |
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Privacy Odometers and Filters: Pay-as-you-Go Composition
| Title | Privacy Odometers and Filters: Pay-as-you-Go Composition |
| Authors | Ryan M. Rogers, Aaron Roth, Jonathan Ullman, Salil Vadhan |
| Abstract | In this paper we initiate the study of adaptive composition in differential privacy when the length of the composition, and the privacy parameters themselves can be chosen adaptively, as a function of the outcome of previously run analyses. This case is much more delicate than the setting covered by existing composition theorems, in which the algorithms themselves can be chosen adaptively, but the privacy parameters must be fixed up front. Indeed, it isn’t even clear how to define differential privacy in the adaptive parameter setting. We proceed by defining two objects which cover the two main use cases of composition theorems. A privacy filter is a stopping time rule that allows an analyst to halt a computation before his pre-specified privacy budget is exceeded. A privacy odometer allows the analyst to track realized privacy loss as he goes, without needing to pre-specify a privacy budget. We show that unlike the case in which privacy parameters are fixed, in the adaptive parameter setting, these two use cases are distinct. We show that there exist privacy filters with bounds comparable (up to constants) with existing privacy composition theorems. We also give a privacy odometer that nearly matches non-adaptive private composition theorems, but is sometimes worse by a small asymptotic factor. Moreover, we show that this is inherent, and that any valid privacy odometer in the adaptive parameter setting must lose this factor, which shows a formal separation between the filter and odometer use-cases. |
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| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6170-privacy-odometers-and-filters-pay-as-you-go-composition |
| http://papers.nips.cc/paper/6170-privacy-odometers-and-filters-pay-as-you-go-composition.pdf | |
| PWC | https://paperswithcode.com/paper/privacy-odometers-and-filters-pay-as-you-go |
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RankDCG: Rank-Ordering Evaluation Measure
| Title | RankDCG: Rank-Ordering Evaluation Measure |
| Authors | Denys Katerenchuk, Andrew Rosenberg |
| Abstract | Ranking is used for a wide array of problems, most notably information retrieval (search). Kendall{'}s Ï„, Average Precision, and nDCG are a few popular approaches to the evaluation of ranking. When dealing with problems such as user ranking or recommendation systems, all these measures suffer from various problems, including the inability to deal with elements of the same rank, inconsistent and ambiguous lower bound scores, and an inappropriate cost function. We propose a new measure, a modification of the popular nDCG algorithm, named rankDCG, that addresses these problems. We provide a number of criteria for any effective ranking algorithm and show that only rankDCG satisfies them all. Results are presented on constructed and real data sets. We release a publicly available rankDCG evaluation package. |
| Tasks | Information Retrieval, Recommendation Systems |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1583/ |
| https://www.aclweb.org/anthology/L16-1583 | |
| PWC | https://paperswithcode.com/paper/rankdcg-rank-ordering-evaluation-measure |
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