NeurIPS 2008

250 papers

Robust Regression and Lasso

Huan Xu, Constantine Caramanis, Shie Mannor

Online Models for Content Optimization

Deepak Agarwal, Bee-chung Chen, Pradheep Elango, Nitin Motgi, Seung-taek Park

Stochastic Relational Models for Large-scale Dyadic Data using MCMC

Shenghuo Zhu, Kai Yu, Yihong Gong

Supervised Exponential Family Principal Component Analysis via Convex Optimization

Yuhong Guo

A Scalable Hierarchical Distributed Language Model

Andriy Mnih, Geoffrey E. Hinton

Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images

Vincent Q. Vu, Bin Yu, Thomas Naselaris, Kendrick Kay, Jack Gallant

Multi-stage Convex Relaxation for Learning with Sparse Regularization

Tong Zhang

Estimating the Location and Orientation of Complex, Correlated Neural Activity using MEG

Julia Owen, Hagai T. Attias, Kensuke Sekihara, Srikantan S. Nagarajan, David P. Wipf

Spectral Clustering with Perturbed Data

Ling Huang, Donghui Yan, Nina Taft, Michael I. Jordan

Probabilistic detection of short events, with application to critical care monitoring

Norm Aleks, Stuart Russell, Michael G. Madden, Diane Morabito, Kristan Staudenmayer

Estimating vector fields using sparse basis field expansions

Stefan Haufe, Vadim V. Nikulin, Andreas Ziehe, Klaus-Robert Müller, Guido Nolte

Efficient Inference in Phylogenetic InDel Trees

Alexandre Bouchard-côté, Dan Klein, Michael I. Jordan

Multi-task Gaussian Process Learning of Robot Inverse Dynamics

Christopher Williams, Stefan Klanke, Sethu Vijayakumar, Kian M. Chai

A ``Shape Aware'' Model for semi-supervised Learning of Objects and its Context

Abhinav Gupta, Jianbo Shi, Larry S. Davis

Sparse Convolved Gaussian Processes for Multi-output Regression

Mauricio Alvarez, Neil D. Lawrence

Inferring rankings under constrained sensing

Srikanth Jagabathula, Devavrat Shah

Differentiable Sparse Coding

J. A. Bagnell, David M. Bradley

Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection

Takafumi Kanamori, Shohei Hido, Masashi Sugiyama

Breaking Audio CAPTCHAs

Jennifer Tam, Jiri Simsa, Sean Hyde, Luis V. Ahn

Regularized Learning with Networks of Features

Ted Sandler, John Blitzer, Partha P. Talukdar, Lyle H. Ungar

Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning

Ali Rahimi, Benjamin Recht

Domain Adaptation with Multiple Sources

Yishay Mansour, Mehryar Mohri, Afshin Rostamizadeh

Supervised Bipartite Graph Inference

Yoshihiro Yamanishi

Counting Solution Clusters in Graph Coloring Problems Using Belief Propagation

Lukas Kroc, Ashish Sabharwal, Bart Selman

Evaluating probabilities under high-dimensional latent variable models

Iain Murray, Ruslan Salakhutdinov

Semi-supervised Learning with Weakly-Related Unlabeled Data : Towards Better Text Categorization

Liu Yang, Rong Jin, Rahul Sukthankar

MDPs with Non-Deterministic Policies

Mahdi M. Fard, Joelle Pineau

Influence of graph construction on graph-based clustering measures

Markus Maier, Ulrike V. Luxburg, Matthias Hein

Performance analysis for L\_2 kernel classification

Jooseuk Kim, Clayton Scott

Biasing Approximate Dynamic Programming with a Lower Discount Factor

Marek Petrik, Bruno Scherrer

Kernel Change-point Analysis

Zaïd Harchaoui, Eric Moulines, Francis R. Bach

An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis

Gabriele Schweikert, Gunnar Rätsch, Christian K. Widmer, Bernhard Schölkopf

Hebbian Learning of Bayes Optimal Decisions

Bernhard Nessler, Michael Pfeiffer, Wolfgang Maass

Modeling human function learning with Gaussian processes

Thomas L. Griffiths, Chris Lucas, Joseph Williams, Michael L. Kalish

Unifying the Sensory and Motor Components of Sensorimotor Adaptation

Adrian Haith, Carl P. Jackson, R. C. Miall, Sethu Vijayakumar

Unlabeled data: Now it helps, now it doesn't

Aarti Singh, Robert Nowak, Xiaojin Zhu

On the Efficient Minimization of Classification Calibrated Surrogates

Richard Nock, Frank Nielsen

Linear Classification and Selective Sampling Under Low Noise Conditions

Giovanni Cavallanti, Nicolò Cesa-bianchi, Claudio Gentile

Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes

Ben Calderhead, Mark Girolami, Neil D. Lawrence

Cascaded Classification Models: Combining Models for Holistic Scene Understanding

Geremy Heitz, Stephen Gould, Ashutosh Saxena, Daphne Koller

Asynchronous Distributed Learning of Topic Models

Padhraic Smyth, Max Welling, Arthur U. Asuncion

Structured ranking learning using cumulative distribution networks

Jim C. Huang, Brendan J. Frey

From Online to Batch Learning with Cutoff-Averaging

Ofer Dekel

Analyzing human feature learning as nonparametric Bayesian inference

Thomas L. Griffiths, Joseph L. Austerweil

Designing neurophysiology experiments to optimally constrain receptive field models along parametric submanifolds

Jeremy Lewi, Robert Butera, David M. Schneider, Sarah Woolley, Liam Paninski

Grouping Contours Via a Related Image

Praveen Srinivasan, Liming Wang, Jianbo Shi

Local Gaussian Process Regression for Real Time Online Model Learning

Duy Nguyen-tuong, Jan R. Peters, Matthias Seeger

Learning Bounded Treewidth Bayesian Networks

Gal Elidan, Stephen Gould

Translated Learning: Transfer Learning across Different Feature Spaces

Wenyuan Dai, Yuqiang Chen, Gui-rong Xue, Qiang Yang, Yong Yu

Near-minimax recursive density estimation on the binary hypercube

Maxim Raginsky, Svetlana Lazebnik, Rebecca Willett, Jorge Silva