NIPS pre-prints

A google search reveals the following preprints associated with NIPS 2005:

• Lafferty, Blei, Correlated Topic Models
  
• Lafferty, Wasserman, Rodeo - Sparse Nonparametric Regression in High Dimensions
  
• Tong Zhang and Rie K. Ando. Analysis of Spectral Kernel Design based Semi-supervised Learning.
  
• Philipp Häfliger, et al, AER Building Blocks for Multi-Layer Multi-Chip Neuromorphic Vision Systems
• Paninski, L. - Inferring prior probabilities from Bayes-optimal behavior
  
• Ahrens, M., Huys, Q. & Paninski, L.. Large-scale biophysical parameter estimation in single neurons via constrained linear regression
  
• Jack M. Wang, David J. Fleet, Aaron Hertzmann. Gaussian Process Dynamical Models.
  J.-P. Vert, R. Thurman and W. S. Noble, "Kernels for gene regulatory regions",
  
• Firas Hamze and Nando de Freitas. Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs
  
• Jason E. Holt, On the Job Training
  
• Marco Cuturi, Kenji Fukumizu - Multiresolution Kernels
  
• Fan Li, Yiming Yang, Eric P. Xing. From lasso regression to feature vector machine.
  
• Jian Zhang, Zoubin Ghahramani and Yiming Yang. Learning Multiple Related Tasks using Latent Independent Component Analysis.
  
• James Diebel, An Application of Markov Random Fields to Range Sensing
  
• A Computational Model of Eye Movements during Object Class Detection, Wei Zhang, Dimitris Samaras, Hyejin Yang, Greg Zelinsky
  
• Cue Integration in Figure/Ground Labeling Xiaofeng Ren, Charless Fowlkes
  
• Nelson, JD; Cottrell, GW; Filimon, F; Sejnowski, T (2005, Dec). Optimal experimental design models of naive human information acquisition. NIPS 2005
  
• W. Zhang, D. Samaras, H. Yang and G. Zelinsky. A Computational Model of Eye Movements during Object Class Detection
  
• Peter Gehler and max Welling Products of "Edge-Perts"
  
• Cesa-Bianchi, Improved risk tail bounds for on-line algorithms
  
• Semi-supervised Learning with Penalized Probabilistic Clustering. NIPS 2005 Zhengdong Lu, Todd Leen
  
• LeCun, et al - Off-Road Obstacle Avoidance through End-to-End Learning
  
• Sparse Covariance Selection via Robust Maximum Likelihood Estimation Authors: Onureena Banerjee, Alexandre d'Aspremont, Laurent El Ghaoui
  
• Glenn Fung, Romer Rosales, Balaji Krishnapuram - Learning Rankings via Convex Hull Separations
  
• Maximum Margin Semi-Supervised Learning for Structured Variables - Y. Altun, D. McAllester, M. Belkin
  
• A Domain Decomposition Method for Fast Manifold Learning. Zhenyue Zhang and Hongyuan Zha
  
• C. Scott and R. Nowak, ``Learning Minimum Volume Sets,"
  
• huys qjm / zemel r / natarajan r / dayan pm - fast population coding
  
• Two view learning: SVM-2K, Theory and Practice" by Jason D. R. Farquhar, David R. Hardoon, Hongying Meng, John Shawe-Taylor and Sandor Szedmak
  
• N. Loeff and A.Sorokin and H. Arora and D.A. Forsyth, ``Efficient Unsupervised Learning for Localization and Detection in Object Categories'', NIPS 2005
  
• T. Roos, , P. Grünwald, P. Myllymäki and H.Tirri. Generalization to Unseen Case
  
• Mooy, J., & Kappen, H.J. Validity estimates for loopy belief propagation on binary real-world networks
  
• Jorge G. Silva, Jorge S. Marques, João M. Lemos, Selecting Landmarkpoints for Sparse manifold Learning
  
• Fleuret, F. and Blanchard, G. - Pattern Recognition from One Example by Chopping
  
• A Probabilistic Approach for Optimizing Spectral Clustering - R. Jin ,C.Ding and F.Kang
  
• Keiji Miura, Masato Okada, Shun-ichi Amari - Unbiased Estimator of Shape Parameter for Spiking Irregularities under Changing Environments
  
• Blatt D. and Hero A. O., "From weighted classification to policy search"
  
• A Connectionist Model for Constructive Modal Reasoning. A.d'Avila Garcez. Luis C. Lamb and Dov Gabbay
  
• J. Ting, A. D'Souza, K. Yamamoto, T. Yoshioka, D. Hoffman, S. Kakei, L. Sergio, J. Kalaska, M. Kawato, P. Strick and S. Schaal. Predicting EMG Data from M1 Neurons with Variational Bayesian Least Squares.
  
• G. L. Li, T.-Y. Leong, Learning Causal Bayesian Network with Constraints from Domain Knowledge
  
• G. L. Li, T.-Y. Leong, Learning Bayesian Networks with Variable Grouping Methods.
  
• L. Itti and P. Baldi. "Bayesian Surprise Attracts Human Attention"
  
• Q-Clustering M. Narasimhan, N. Jojic and J. Bilmes
  
• Nando de Freitas, Yang Wang, Maryam Mahdaviani, Dustin Lang. Fast Krylov Methods for N-Body Learning.
  
• Consistency of One-Class SVM and Related Algorithms, R. Vert and J.-P. Vert
  
• A. J. Bell and L. C. Parra, "Maximising sensitivity in a spiking network,"
  
• R. S. Zemel, Q. J. M. Huys, R. Natarajan, and P. Dayan, "Probabilistic computation
  
• in spiking populations," in Advances in NIPS, 2005
  
• C. Yang, R. Duraiswami and L. Davis: "Efficient Kernel Machines Using the Improved Fast Gauss Transform", NIPS 2005
  
• F. Orabona. Object-based Model of the Visual Attention for Imitation
  
• Jieping Ye, Ravi Janardan, Qi Li. Two-Dimensional Linear Discriminant Analysis
  
• L. Song, E. Gordon, and E. Gysels, "Phase Synchrony Rates for the Recognition of Motor Imageries in BCIs"
  
• Kakade, S., M. Seeger and D. Foster: Worst-Case Bounds for Gaussian Process Models.
  
• Shen, Y., A. Ng and M. Seeger: Fast Gaussian Process Regression using KD-Trees.
  
• Kevin J. Lang - Fixing two weakness of the Spectral Method
  
• Kari Torkkola and Eugene Tuv. Ecumenical kernels from random forests
  
• C. Molter and U. Salihoglu and H. Bersini, Storing information through complex dynamics in Recurrent Neural Networks
  
• J. Ting, A. D'Souza, K. Yamamoto, T. Yoshioka, D. Hoffman, S. Kakei, L. Sergio, J. Kalaska, M. Kawato, P. Strick and S. Schaal. Predicting EMG Data from M1 Neurons with Variational Bayesian Least Squares
  
• Z. Nenadic, D.S. Rizzuto, R.A. Andersen, and J.W. Burdick, "Discriminat basedfeature selection with information theoretic objective
  
• Mooy, J., & Kappen, H.J. (2004). Validity estimates for loopy belief propagation on binary real-world networks
  
• Lackey, J. and Colagrosso, M (2005). A Kernel Method for Polychotomous Classification
  
• J. A. Palmer, K. Kreutz-Delgado, D. P. Wipf, and B. D. Rao, Variational EM Algorithms for Non-Gaussian Latent Variable Models
  
• Singh, S., Barto, A., and Chentanez, N. (2005). Intrinsically motivated reinforcement learning
  
• Yun-Gang Zhang, Changshui Zhang, Separation of Music Signals by Harmonic Structure Modeling.
  
• Rob Powers, Yoav Shoham, New Criteria and a New Algorithm for Learning in Multi-Agent Systems
  
• Wood, F., Roth, S., and Black, M. J., "Modeling neural population spiking activity with Gibbs distributions,
  
• Hinton, G. E. and Nair, V. Inferring motor programs from images of handwritten digits.
  
• Onureena Banerjee, Alexandre d'Aspremont, Laurent El Ghaoui - Sparse Covariance Selection via Robust Maximum Likelihood Estimation.
  
• "Measuring Shared Information and Coordinated Activity in Neuronal Networks." K. Klinkner, C. Shalizi, and M. Camperi, NIPS, 2005.
  
• Generalisation Error Bounds for Classifiers Trained with Interdependent Data Usunier N., Amini M.-R., Gallinari P
  
• Brafman, R. I. and Shani, G. "Resolving perceptual asliasing with noisy sensors"
  
• Griffiths, T.L., and Ghahramani, Z. (to appear) Infinite Latent Feature Models and the Indian Buffet Process
  
• Zhang, J., Ghahramani, Z. and Yang, Y. (to appear) Learning Multiple Related Tasks using Latent Independent Component Analysis.
  
• Murray, I., MacKay, D.J.C., Ghahramani, Z. and Skilling, J. (to appear) Nested Sampling for Potts Models.
  
• Snelson, E. and Ghahramani, Z. (to appear) Sparse Parametric Gaussian Processes
  
• Ghahramani, Z. and Heller, K.A. (to appear) Bayesian Sets
  
• On the Convergence of Eigenspaces in Kernel Principal Component Analysis: G. Blanchard and L. Zwald
  
• Poupart, P. and Boutilier, C. VDCBPI: an Approximate Scalable Algorithm for Large Scale POMDPs
  
• T. Murayama and P. Davis, Rate distortion codes for sensor networks: A system-level analysis,
  
• Damian Eads, Karen Glocer, Simon Perkins, and James Theiler (2005). Grammar-guided feature extraction for time series classification.
  
• Doi, E., Balcan, D. C., & Lewicki, M. S. A theoretical analysis of robust coding over noisy overcomplete channels
  
• Nadler, Boaz; Lafon, Stephane; Coifman, Ronald R.; Kevrekidis, Ioannis G. - Diffusion Maps, Spectral Clustering and Eigenfunctions of Fokker-Planck operators
  
• "Abstractions and Hierarchies for Learning and Planning" Lihong Li
  
• "Walk-sum interpretation and analysis of Gaussian belief propagation ," Jason K. Johnson, Dmitry M. Malioutov, and Alan S. Willsky
  
• Baker C., Tenenbaum J., & Saxe R. - Bayesian models of perceiving.intentional action
  
• Tensor Subspace Analysis - Xiaofei He, Deng Cai, and Partha Niyogi
  
• Laplacian Score for Feature Selection - Xiaofei He, Deng Cai, and Partha Niyogi.
  
• Shunji Satoh ``Long-Range Horizontal Connections in V1 Serve to Multi-Scale Image Reconstruction
  
• "Faster Rates in Regression via Active Learning" Rebecca Willett with R. Castro and R. Nowak,
  
• Hyperparameter and Kernel Learning for Graph Based Semi-Supervised Classification - Ashish Kapoor, Yuan (Alan) Qi, Hyungil Ahn and Rosalind W. Picard
  
• Maxim Raginsky, Svetlana Lazebnik - Estimation of intrinsic dimensionality using high-rate vector quantization
  
• Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer - The Forgetron: A Kernel-Based Perceptron on a Fixed Budget
  
• Patrick Flaherty, Michael Jordan, Adam Arkin - Robust Design of Biological Experiments
  
• Yael Niv, Nathaniel Daw, Peter Dayan - How Fast to Work: Response Vigor, Motivation and Tonic Dopamine
  
• Learning from Data of Variable Quality - Koby Crammer, Michael Kearns, and Jennifer Wortman
  
• R. Kondor and T. Jebara - Gaussian and Wishart Hyperkernels
  
• L. Liao, D. Fox, and H. Kautz. - Location-Based Activity Recognition Reinforcement Learning of Local Shape in the Game of Atari-Go David Silver, Richard Sutton, Martin Müller, Markus Enzenberger