---
title: "NeurIPS 2009 — all accepted papers — the most cited first"
description: "All 57 papers of NeurIPS 2009, the most cited first; those with our summary link to it. Among them: Advances in Neural Information…"
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---
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# NeurIPS 2009: all accepted papers

57 papers · the most cited first · [← NeurIPS 2009](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/2009/)

1. **[Advances in Neural Information Processing Systems 21 ↗](https://www.research.ed.ac.uk/portal/en/publications/a-rational-model-of-preference-learning-and-choice-prediction-by-children(0237533d-3df7-4056-9369-b02c1963fef9)/export.html)**Thomas L. Griffiths, Christopher G. Lucas, Joseph Jay Williams et al.*1,853 citations*
2. **[Unsupervised feature learning for audio classification using convolutional deep belief networks ↗](http://ai.stanford.edu/~hllee//nips09-AudioConvolutionalDBN.pdf)**Honglak Lee, Peter T. Pham, Yan Largman et al.*918 citations*
3. **[Replicated Softmax: an Undirected Topic Model ↗](http://books.nips.cc/papers/files/nips22/NIPS2009_0817.pdf)**Geoffrey E. Hinton, Ruslan Salakhutdinov*415 citations*
4. **[Multi-Label Prediction via Compressed Sensing ↗](http://ece.duke.edu/~lcarin/MultLabelCS.pdf)**John C. Langford, Tong Zhang, Daniel Hsu et al.*258 citations*
5. **[Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models ↗](https://www.cs.nyu.edu/~mohri/pub/maxent.pdf)**Ryan McDonald, Mehryar Mohri, Nathan Silberman et al.*224 citations*
6. **[Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation ↗](http://castlelab.princeton.edu/ORF569papers/Maei%20-%20convergent%20temporal-difference%20learning%20with%20arbitrary%20smooth%20function%20approximation.pdf)**Shalabh Bhatnagar, Doina Precup, David Silver et al.*155 citations*
7. **[Slow Learners are Fast ↗](http://alex.smola.org/papers/2009/LanSmoZin09.pdf)**Martin Zinkevich, John C. Langford, Alex J. Smola*147 citations*
8. **[Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions ↗](http://eceweb.ucsd.edu/~gert/papers/nips_mmd.pdf)**Kenji Fukumizu, Arthur Gretton, Gert R. G. Lanckriet et al.*144 citations*
9. **[Grouped Orthogonal Matching Pursuit for Variable Selection and Prediction ↗](http://books.nips.cc/papers/files/nips22/NIPS2009_0868.pdf)**Grzegorz Świrszcz, Naoki Abe, Aurelie C. Lozano*97 citations*
10. **[Efficient Learning using Forward-Backward Splitting ↗](http://books.nips.cc/papers/files/nips22/NIPS2009_0248.pdf)**Yoram Singer, John C. Duchi*87 citations*
11. **[Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data ↗](https://papers.nips.cc/paper/3651-learning-brain-connectivity-of-alzheimers-disease-from-neuroimaging-data.pdf)**Shuai Huang, Jing Li, Liang Sun et al.*77 citations*
12. **[Exploring Functional Connectivities of the Human Brain using Multivariate Information Analysis ↗](http://books.nips.cc/papers/files/nips22/NIPS2009_1140.pdf)**Barry Chai, Dirk B. Walther, Diane M. Beck et al.*66 citations*
13. **[Noisy Generalized Binary Search ↗](https://papers.nips.cc/paper/3721-noisy-generalized-binary-search.pdf)**Robert D. Nowak*62 citations*
14. **[Slow, Decorrelated Features for Pretraining Complex Cell-like Networks ↗](http://books.nips.cc/papers/files/nips22/NIPS2009_0933.pdf)**Yoshua Bengio, James Bergstra*61 citations*
15. **[Statistical Analysis of Semi-Supervised Learning: The Limit of Infinite Unlabelled Data ↗](https://proceedings.neurips.cc/paper/2009/file/68ce199ec2c5517597ce0a4d89620f55-Paper.pdf)**Boaz Nadler, Nathan Srebro, Xueyuan Zhou*52 citations*
16. **[On the Algorithmics and Applications of a Mixed-norm based Kernel Learning Formulation ↗](http://books.nips.cc/papers/files/nips22/NIPS2009_0603.pdf)**Saketha Nath Jagarlapudi, G. Dinesh, Raman Sankaran et al.*50 citations*
17. **[Orthogonal Matching Pursuit From Noisy Random Measurements: A New Analysis ↗](https://papers.nips.cc/paper/3784-orthogonal-matching-pursuit-from-noisy-random-measurements-a-new-analysis.pdf)**Sundeep Rangan, Alyson K. Fletcher*47 citations*
18. **[Matrix Completion from Power-Law Distributed Samples ↗](http://books.nips.cc/papers/files/nips22/NIPS2009_0864.pdf)**Raghu Meka, Prateek Jain, Inderjit S. Dhillon*44 citations*
19. **[Nonlinear directed acyclic structure learning with weakly additive noise models ↗](http://is.tuebingen.mpg.de/fileadmin/user_upload/files/publications/NIPS2009-Tillman_[0].pdf)**Arthur Gretton, Peter Spirtes, Robert E. Tillman*44 citations*
20. **[Sequential effects reflect parallel learning of multiple environmental regularities ↗](http://matt.colorado.edu/papers/wilder-jones-mozer_nips09.pdf)**Matthew Wilder, Matt Jones, Michael C. Mozer*43 citations*
21. **[Asymptotic Analysis of MAP Estimation via the Replica Method and Compressed Sensing ↗](https://papers.nips.cc/paper/3635-asymptotic-analysis-of-map-estimation-via-the-replica-method-and-compressed-sensing.pdf)**Sundeep Rangan, Vivek K Goyal, Alyson K. Fletcher*37 citations*
22. **[Boosting with Spatial Regularization ↗](http://books.nips.cc/papers/files/nips22/NIPS2009_0149.pdf)**Yongxin Xi, Uri Hasson, Peter J. Ramadge et al.*30 citations*
23. **[No evidence for active sparsification in the visual cortex ↗](https://papers.nips.cc/paper/3774-no-evidence-for-active-sparsification-in-the-visual-cortex.pdf)**Pietro Berkes, Benjamen White, József Fiser*28 citations*
24. **[Generalization Errors and Learning Curves for Regression with Multi-task Gaussian Processes ↗](https://papers.nips.cc/paper/3786-generalization-errors-and-learning-curves-for-regression-with-multi-task-gaussian-processes.pdf)**Kian Ming A. Chai*27 citations*
25. **[Hierarchical Mixture of Classification Experts Uncovers Interactions between Brain Regions ↗](https://cs.stanford.edu/groups/vision/pdf/YaoWaltherBeckFei-Fei_NIPS2009.pdf)**Bangpeng Yao, Dirk B. Walther, Diane M. Beck et al.*27 citations*
26. **[Estimating image bases for visual image reconstruction from human brain activity ↗](http://books.nips.cc/papers/files/nips22/NIPS2009_0804.pdf)**Yusuke Fujiwara, Yoichi Miyawaki, Yukiyasu Kamitani*26 citations*
27. **[Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models ↗](https://papers.nips.cc/paper/3740-time-rescaling-methods-for-the-estimation-and-assessment-of-non-poisson-neural-encoding-models.pdf)**Jonathan W. Pillow*25 citations*
28. **[Hierarchical Modeling of Local Image Features through L_p-Nested Symmetric Distributions ↗](https://papers.nips.cc/paper/3642-hierarchical-modeling-of-local-image-features-through-l_p-nested-symmetric-distributions.pdf)**Matthias Bethge, Eero P. Simoncelli, Fabian H. Sinz*23 citations*
29. **[Hierarchical Learning of Dimensional Biases in Human Categorization ↗](https://papers.nips.cc/paper/3849-hierarchical-learning-of-dimensional-biases-in-human-categorization.pdf)**Adam N. Sanborn, Nick Chater, Katherine Heller*18 citations*
30. **[Learning transport operators for image manifolds ↗](http://redwood.berkeley.edu/bruno/papers/manifold-transport.pdf)**Benjamin J. Culpepper, Bruno A. Olshausen*18 citations*
31. **[Multi-Step Dyna Planning for Policy Evaluation and Control ↗](https://papers.nips.cc/paper/3670-multi-step-dyna-planning-for-policy-evaluation-and-control.pdf)**Hengshuai Yao, Shalabh Bhatnagar, Dongcui Diao et al.*16 citations*
32. **[Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness ↗](http://books.nips.cc/papers/files/nips22/NIPS2009_1088.pdf)**Garvesh Raskutti, Bin Yu, Martin J. Wainwright*14 citations*
33. **[Speaker Comparison with Inner Product Discriminant Functions ↗](https://papers.nips.cc/paper/3802-speaker-comparison-with-inner-product-discriminant-functions.pdf)**Zahi N. Karam, Douglas E. Sturim, William M. Campbell*13 citations*
34. **[A Generalized Natural Actor-Critic Algorithm ↗](https://papers.nips.cc/paper/2009/file/acf4b89d3d503d8252c9c4ba75ddbf6d-Paper.pdf)**Tetsuro Morimura, Eiji Uchibe, Junichiro Yoshimoto et al.*12 citations*
35. **[Audio Scene Understanding using Topic Models ↗](http://ict.usc.edu/pubs/Audio%20Scene%20Understanding%20using%20Topic%20Models.pdf)**Samuel Kim, Shiva Sundaram, Panayiotis Georgiou et al.*12 citations*
36. **[Augmenting Feature-driven fMRI Analyses: Semi-supervised learning and resting state activity ↗](https://fias.uni-frankfurt.de/~shelton/papers/wiml2010.pdf)**Andreas Bartels, Matthew B. Blaschko, Jacquelyn A. Shelton*12 citations*
37. **[Evaluating multi-class learning strategies in a generative hierarchical framework for object detection ↗](https://papers.nips.cc/paper/3798-evaluating-multi-class-learning-strategies-in-a-generative-hierarchical-framework-for-object-detection.pdf)**Sanja Fidler, Marko Boben, Aleš Leonardis*12 citations*
38. **[Particle-based Variational Inference for Continuous Systems ↗](http://sli.ics.uci.edu/pmwiki/uploads/Pubs/nips09.pdf)**Andrew J. Frank, Padhraic Smyth, Alexander Ihler*10 citations*
39. **[Beyond Convexity: Online Submodular Minimization ↗](https://papers.nips.cc/paper/2009/file/f770b62bc8f42a0b66751fe636fc6eb0-Paper.pdf)**Elad Hazan, Satyen Kale*9 citations*
40. **[Nonparametric Greedy Algorithms for the Sparse Learning Problem ↗](https://papers.nips.cc/paper/3889-nonparametric-greedy-algorithms-for-the-sparse-learning-problem.pdf)**Han Liu, Xi Chen*8 citations*
41. **[Randomized Pruning: Efficiently Calculating Expectations in Large Dynamic Programs ↗](http://petrovi.de/data/nips09.pdf)**Alexandre Bouchard‐Côté, Slav Petrov, Dan Klein*6 citations*
42. **[An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism ↗](http://books.nips.cc/papers/files/nips22/NIPS2009_1100.pdf)**Douglas Eck, Yoshua Bengio, Aaron C. Courville*5 citations*
43. **[Time series analysis with the causality workbench ↗](http://proceedings.mlr.press/v12/guyon11/guyon11.pdf)**Isabelle Guyon, Alexander Statnikov, Constantin Aliferis*4 citations*
44. **[Breaking Boundaries Between Induction Time and Diagnosis Time Active Information Acquisition ↗](https://papers.nips.cc/paper/2009/file/fc221309746013ac554571fbd180e1c8-Paper.pdf)**Ashish Kapoor, Eric Horvitz*3 citations*
45. **[Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference ↗](http://books.nips.cc/papers/files/nips22/NIPS2009_0910.pdf)**Khashayar Rohanimanesh, Sameer Kumar Singh, Andrew McCallum et al.*3 citations*
46. **[Approximating MAP by Compensating for Structural Relaxations ↗](https://papers.nips.cc/paper/2009/file/cfbce4c1d7c425baf21d6b6f2babe6be-Paper.pdf)**Arthur Choi, Adnan Y. Darwiche*1 citation*
47. **[Demonstration, Tactile Correction and Multiple Training Data Sources for Robot Motion Control ↗](http://lasa.epfl.ch/publications/uploadedFiles/09nips-workshop.pdf)**Brenna Argall, Eric L. Sauser, Aude Billard*1 citation*
48. **[Individuation, Identification and Object Discovery ↗](https://papers.nips.cc/paper/2009/file/38af86134b65d0f10fe33d30dd76442e-Paper.pdf)**Charles Kemp, Alan Jern, Fei Xu*1 citation*
49. **[Modeling the spacing effect in sequential category learning ↗](http://books.nips.cc/papers/files/nips22/NIPS2009_1010.pdf)**Hongjing Lu, Matthew Weiden, Alan Yuille*1 citation*
50. **[Variable selection and grouping with multiple graph priors ↗](https://lirias.kuleuven.be/handle/123456789/251772)**Marco Signoretto, Anneleen Daemen, Carlo Savorgnan et al.*1 citation*
51. **[Hierarchical Fisher Kernel for Longitudinal Data ↗](https://www.microsoft.com/en-us/research/wp-content/uploads/2009/01/NIPS08.pdf)**Zhengdong Lu, Todd K. Leen, Jeffrey Alan Kaye
52. **[Local Rules for Global MAP: When Do They Work? (Technical Report) ↗](https://research.microsoft.com/en-us/um/people/pkohli/papers/jks_nips09_tr.pdf)**Kyomin Jung, Pushmeet Kohli, Devavrat Shah
53. **[Machine Learning for Brain-Computer Interfaces ↗](https://pure.mpg.de/pubman/item/item_1788976_3/component/file_3031583/Hill-NIPS2009-MLBCI_6220%5B0%5D.pdf)**N. Jeremy Hill
54. **[Proceedings of the 12th International Conference on Neural Information Processing Systems (NIPS)Mini-Symposium on Causality in Time Series ↗](http://dl.acm.org/citation.cfm?id=2969932)**Florin Popescu, Isabelle Guyon
55. **[STDP performs stochastic EM to reveal the hidden causes of spatiotemporal spiking patterns ↗](https://graz.pure.elsevier.com/en/publications/stdp-performs-stochastic-em-to-reveal-the-hidden-causes-of-spatio)**Bernhard Nessler, Michael Pfeiffer, Wolfgang Maass
56. **[Transfer Learning Methods and Applications in Computational Biology ↗](https://pure.mpg.de/pubman/faces/ViewItemOverviewPage.jsp?itemId=item_3031576)**Gunnar Rätsch
57. **[Who Talks to Whom: Modeling Latent Structures in Dialogue Documents ↗](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/07/nips_workshop_2009_ldsm.pdf)**Bailu Ding, Jiang-Ming Yang, Chong Wang et al.
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