NIPS 2015 Papers
Explore and analyze this year's NIPS papers
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Papers
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idId | titleTitle | eventtypeEventType | pdfnamePdfName | abstractAbstract | papertextPaperText |
|---|---|---|---|---|---|
| 5677 | Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing | Poster | 5677-double-or-nothing-multiplicative-incentive-mechanisms-for-crowdsourcing.pdf | Crowdsourcing has gained immense popularity in machine learning applications for obtaining large amounts of labeled data. Crowdsourcing is cheap and fast, but suffers from the problem of low-quality data. To address this fundamental challenge in crowdsourcing, we propose a simple payment mechanism t… | Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing Nihar B. Shah University of California, Berkeley nihar@eecs.berkeley.edu Dengyong Zhou Microsoft Research dengyong.zhou@microsoft.com Abstract Crowdsourcing has gained immense popularity in machine learning applications for o… |
| 5941 | Learning with Symmetric Label Noise: The Importance of Being Unhinged | Spotlight | 5941-learning-with-symmetric-label-noise-the-importance-of-being-unhinged.pdf | Convex potential minimisation is the de facto approach to binary classification. However, Long and Servedio [2008] proved that under symmetric label noise (SLN), minimisation of any convex potential over a linear function class can result in classification performance equivalent to random guessing. … | Learning with Symmetric Label Noise: The Importance of Being Unhinged Brendan van Rooyen∗,† ∗ Aditya Krishna Menon†,∗ The Australian National University † Robert C. Williamson∗,† National ICT Australia { brendan.vanrooyen, aditya.menon, bob.williamson }@nicta.com.au Abstract Convex potential… |
| 6019 | Algorithmic Stability and Uniform Generalization | Poster | 6019-algorithmic-stability-and-uniform-generalization.pdf | One of the central questions in statistical learning theory is to determine the conditions under which agents can learn from experience. This includes the necessary and sufficient conditions for generalization from a given finite training set to new observations. In this paper, we prove that algorit… | Algorithmic Stability and Uniform Generalization Ibrahim Alabdulmohsin King Abdullah University of Science and Technology Thuwal 23955, Saudi Arabia ibrahim.alabdulmohsin@kaust.edu.sa Abstract One of the central questions in statistical learning theory is to determine the conditions under which ag… |
| 6035 | Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models | Poster | 6035-adaptive-low-complexity-sequential-inference-for-dirichlet-process-mixture-models.pdf | We develop a sequential low-complexity inference procedure for Dirichlet process mixtures of Gaussians for online clustering and parameter estimation when the number of clusters are unknown a-priori. We present an easily computable, closed form parametric expression for the conditional likelihood, i… | Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models Theodoros Tsiligkaridis, Keith W. Forsythe Massachusetts Institute of Technology, Lincoln Laboratory Lexington, MA 02421 USA ttsili@ll.mit.edu, forsythe@ll.mit.edu Abstract We develop a sequential low-complexity infer… |
| 5978 | Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling | Poster | 5978-covariance-controlled-adaptive-langevin-thermostat-for-large-scale-bayesian-sampling.pdf | Monte Carlo sampling for Bayesian posterior inference is a common approach used in machine learning. The Markov Chain Monte Carlo procedures that are used are often discrete-time analogues of associated stochastic differential equations (SDEs). These SDEs are guaranteed to leave invariant the requir… | Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling Xiaocheng Shang∗ University of Edinburgh x.shang@ed.ac.uk Zhanxing Zhu∗ University of Edinburgh zhanxing.zhu@ed.ac.uk Benedict Leimkuhler University of Edinburgh b.leimkuhler@ed.ac.uk Amos J. Storkey University … |
| 5714 | Robust Portfolio Optimization | Poster | 5714-robust-portfolio-optimization.pdf | We propose a robust portfolio optimization approach based on quantile statistics. The proposed method is robust to extreme events in asset returns, and accommodates large portfolios under limited historical data. Specifically, we show that the risk of the estimated portfolio converges to the oracle … | Robust Portfolio Optimization Fang Han Department of Biostatistics Johns Hopkins University Baltimore, MD 21205 fhan@jhu.edu Huitong Qiu Department of Biostatistics Johns Hopkins University Baltimore, MD 21205 hqiu7@jhu.edu Han Liu Department of Operations Research and Financial Engineering Prince… |
| 5937 | Logarithmic Time Online Multiclass prediction | Spotlight | 5937-logarithmic-time-online-multiclass-prediction.pdf | We study the problem of multiclass classification with an extremely large number of classes (k), with the goal of obtaining train and test time complexity logarithmic in the number of classes. We develop top-down tree construction approaches for constructing logarithmic depth trees. On the theoretic… | Logarithmic Time Online Multiclass prediction Anna Choromanska Courant Institute of Mathematical Sciences New York, NY, USA achoroma@cims.nyu.edu John Langford Microsoft Research New York, NY, USA jcl@microsoft.com Abstract We study the problem of multiclass classification with an extremely large … |
| 5802 | Planar Ultrametrics for Image Segmentation | Poster | 5802-planar-ultrametrics-for-image-segmentation.pdf | We study the problem of hierarchical clustering on planar graphs. We formulate this in terms of finding the closest ultrametric to a specified set of distances and solve it using an LP relaxation that leverages minimum cost perfect matching as a subroutine to efficiently explore the space of planar … | Planar Ultrametrics for Image Segmentation Charless C. Fowlkes Department of Computer Science University of California Irvine fowlkes@ics.uci.edu Julian Yarkony Experian Data Lab San Diego, CA 92130 julian.yarkony@experian.com Abstract We study the problem of hierarchical clustering on planar gra… |
| 5776 | Expressing an Image Stream with a Sequence of Natural Sentences | Poster | 5776-expressing-an-image-stream-with-a-sequence-of-natural-sentences.pdf | We propose an approach for generating a sequence of natural sentences for an image stream. Since general users usually take a series of pictures on their special moments, much online visual information exists in the form of image streams, for which it would better take into consideration of the whol… | Expressing an Image Stream with a Sequence of Natural Sentences Cesc Chunseong Park Gunhee Kim Seoul National University, Seoul, Korea {park.chunseong,gunhee}@snu.ac.kr https://github.com/cesc-park/CRCN Abstract We propose an approach for retrieving a sequence of natural sentences for an image stre… |
| 5814 | Parallel Correlation Clustering on Big Graphs | Poster | 5814-parallel-correlation-clustering-on-big-graphs.pdf | Given a similarity graph between items, correlation clustering (CC) groups similar items together and dissimilar ones apart. One of the most popular CC algorithms is KwikCluster: an algorithm that serially clusters neighborhoods of vertices, and obtains a 3-approximation ratio. Unfortunately, in pr… | Parallel Correlation Clustering on Big Graphs Xinghao Pan↵,✏ , Dimitris Papailiopoulos↵,✏ , Samet Oymak↵,✏ , Benjamin Recht↵,✏, , Kannan Ramchandran✏ , and Michael I. Jordan↵,✏, ↵ AMPLab, ✏ EECS at UC Berkeley, Statistics at UC Berkeley Abstract Given a similarity graph between items, correlation c… |
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