NIPS 2006: Vancouver, British Columbia, Canada

• Pieter Abbeel, Adam Coates, Morgan Quigley, Andrew Y. Ng:
  An Application of Reinforcement Learning to Aerobatic Helicopter Flight. 1-8
  
• Amiran Ambroladze, Emilio Parrado-Hernández, John Shawe-Taylor:
  Tighter PAC-Bayes Bounds. 9-16
  
• Yonatan Amit, Shai Shalev-Shwartz, Yoram Singer:
  Online Classification for Complex Problems Using Simultaneous Projections. 17-24
  
• Rie Kubota Ando, Tong Zhang:
  Learning on Graph with Laplacian Regularization. 25-32
  
• Jerónimo Arenas-García, Kaare Brandt Petersen, Lars Kai Hansen:
  Sparse Kernel Orthonormalized PLS for feature extraction in large data sets. 33-40
  
• Andreas Argyriou, Theodoros Evgeniou, Massimiliano Pontil:
  Multi-Task Feature Learning. 41-48
  
• Peter Auer, Ronald Ortner:
  Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning. 49-56
  
• Shai Avidan, Moshe Butman:
  Efficient Methods for Privacy Preserving Face Detection. 57-64
  
• Francis R. Bach:
  Active learning for misspecified generalized linear models. 65-72
  
• Aharon Bar-Hillel, Daphna Weinshall:
  Subordinate class recognition using relational object models. 73-80
  
• David Barber, Silvia Chiappa:
  Unified Inference for Variational Bayesian Linear Gaussian State-Space Models. 81-88
  
• David Barber, Bertrand Mesot:
  A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical Systems. 89-96
  
• Peter L. Bartlett, Ambuj Tewari:
  Sample Complexity of Policy Search with Known Dynamics. 97-104
  
• Peter L. Bartlett, Mikhail Traskin:
  AdaBoost is Consistent. 105-112
  
• Chiara Bartolozzi, Giacomo Indiveri:
  A selective attention multi--chip system with dynamic synapses and spiking neurons. 113-120
  
• Alexis Battle, Gal Chechik, Daphne Koller:
  Temporal and Cross-Subject Probabilistic Models for fMRI Prediction Tasks. 121-128
  
• Mikhail Belkin, Partha Niyogi:
  Convergence of Laplacian Eigenmaps. 129-136
  
• Shai Ben-David, John Blitzer, Koby Crammer, Fernando Pereira:
  Analysis of Representations for Domain Adaptation. 137-144
  
• Eli Ben-Sasson, Adam Tauman Kalai, Ehud Kalai:
  An Approach to Bounded Rationality. 145-152
  
• Yoshua Bengio, Pascal Lamblin, Dan Popovici, Hugo Larochelle:
  Greedy Layer-Wise Training of Deep Networks. 153-160
  
• Steffen Bickel, Tobias Scheffer:
  Dirichlet-Enhanced Spam Filtering based on Biased Samples. 161-168
  
• Alessandro Bissacco, Ming-Hsuan Yang, Stefano Soatto:
  Detecting Humans via Their Pose. 169-176
  
• Oren Boiman, Michal Irani:
  Similarity by Composition. 177-184
  
• Mikio L. Braun, Joachim M. Buhmann, Klaus-Robert Müller:
  Denoising and Dimension Reduction in Feature Space. 185-192
  
• Christopher J. C. Burges, Robert Ragno, Quoc Viet Le:
  Learning to Rank with Nonsmooth Cost Functions. 193-200
  
• Peter Carbonetto, Nando de Freitas:
  Conditional mean field. 201-208
  
• Gavin C. Cawley, Nicola L. C. Talbot, Mark Girolami:
  Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation. 209-216
  
• Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keerthi:
  Branch and Bound for Semi-Supervised Support Vector Machines. 217-224
  
• Laurent Charlin, Pascal Poupart, Romy Shioda:
  Automated Hierarchy Discovery for Planning in Partially Observable Environments. 225-232
  
• Gal Chechik, Geremy Heitz, Gal Elidan, Pieter Abbeel, Daphne Koller:
  Max-margin classification of incomplete data. 233-240
  
• Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyvers:
  Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model. 241-248
  
• Li Cheng, S. V. N. Vishwanathan, Dale Schuurmans, Shaojun Wang, Terry Caelli:
  implicit Online Learning with Kernels. 249-256
  
• Elisabetta Chicca, Giacomo Indiveri, Rodney J. Douglas:
  Context dependent amplification of both rate and event-correlation in a VLSI network of spiking neurons. 257-264
  
• Hugh A. Chipman, Edward I. George, Robert E. McCulloch:
  Bayesian Ensemble Learning. 265-272
  
• Christian Walder, Bernhard Schölkopf, Olivier Chapelle:
  Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions. 273-280
  
• Cheng-Tao Chu, Sang Kyun Kim, Yi-An Lin, YuanYuan Yu, Gary R. Bradski, Andrew Y. Ng, Kunle Olukotun:
  Map-Reduce for Machine Learning on Multicore. 281-288
  
• Wei Chu, Vikas Sindhwani, Zoubin Ghahramani, S. Sathiya Keerthi:
  Relational Learning with Gaussian Processes. 289-296
  
• David Cohn, Deepak Verma, Karl Pfleger:
  Recursive Attribute Factoring. 297-304
  
• Corinna Cortes, Mehryar Mohri:
  On Transductive Regression. 305-312
  
• Timothée Cour, Praveen Srinivasan, Jianbo Shi:
  Balanced Graph Matching. 313-320
  
• Koby Crammer, Michael J. Kearns, Jennifer Wortman:
  Learning from Multiple Sources. 321-328
  
• Marco Cuturi, Kenji Fukumizu:
  Kernels on Structured Objects Through Nested Histograms. 329-336
  
• Jason V. Davis, Inderjit S. Dhillon:
  Differential Entropic Clustering of Multivariate Gaussians. 337-344
  
• Ofer Dekel, Yoram Singer:
  Support Vector Machines on a Budget. 345-352
  
• Eizaburo Doi, Michael S. Lewicki:
  A Theory of Retinal Population Coding. 353-360
  
• Piotr Dollár, Serge Belongie, Vincent Rabaud:
  Learning to Traverse Image Manifolds. 361-368
  
• John C. Duchi, Daniel Tarlow, Gal Elidan, Daphne Koller:
  Using Combinatorial Optimization within Max-Product Belief Propagation. 369-376
  
• Ran El-Yaniv, Mordechai Nisenson:
  Optimal Single-Class Classification Strategies. 377-384
  
• Eyal Even-Dar, Michael J. Kearns:
  A Small World Threshold for Economic Network Formation. 385-392
  
• Yu Feng, Greg Hamerly:
  PG-means: learning the number of clusters in data. 393-400
  
• Mário A. T. Figueiredo, Dong Seon Cheng, Vittorio Murino:
  Clustering Under Prior Knowledge with Application to Image Segmentation. 401-408
  
• Karim Filali, Jeff A. Bilmes:
  Multi-dynamic Bayesian Networks. 409-416
  
• Andrea Frome, Yoram Singer, Jitendra Malik:
  Image Retrieval and Classification Using Local Distance Functions. 417-424
  
• Glenn Fung, Murat Dundar, Balaji Krishnapuram, R. Bharat Rao:
  Multiple Instance Learning for Computer Aided Diagnosis. 425-432
  
• Stanislav Funiak, Carlos Guestrin, Mark A. Paskin, Rahul Sukthankar:
  Distributed Inference in Dynamical Systems. 433-440
  
• Alborz Geramifard, Michael H. Bowling, Martin Zinkevich, Richard S. Sutton:
  iLSTD: Eligibility Traces and Convergence Analysis. 441-448
  
• Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand:
  A PAC-Bayes Risk Bound for General Loss Functions. 449-456
  
• Mohammad Ghavamzadeh, Yaakov Engel:
  Bayesian Policy Gradient Algorithms. 457-464
  
• Mark Girolami, Mingjun Zhong:
  Data Integration for Classification Problems Employing Gaussian Process Priors. 465-472
  
• Amir Globerson, Tommi Jaakkola:
  Approximate inference using planar graph decomposition. 473-480
  
• Carla P. Gomes, Ashish Sabharwal, Bart Selman:
  Near-Uniform Sampling of Combinatorial Spaces Using XOR Constraints. 481-488
  
• Geoffrey J. Gordon:
  No-regret Algorithms for Online Convex Programs. 489-496
  
• Amit Gore, Shantanu Chakrabartty:
  Large Margin Multi-channel Analog-to-Digital Conversion with Applications to Neural Prosthesis. 497-504
  
• Kristen Grauman, Trevor Darrell:
  Approximate Correspondences in High Dimensions. 505-512
  
• Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola:
  A Kernel Method for the Two-Sample-Problem. 513-520
  
• David B. Grimes, Daniel R. Rashid, Rajesh P. N. Rao:
  Learning Nonparametric Models for Probabilistic Imitation. 521-528
  
• Samuel S. Gross, Olga Russakovsky, Chuong B. Do, Serafim Batzoglou:
  Training Conditional Random Fields for Maximum Labelwise Accuracy. 529-536
  
• Moritz Grosse-Wentrup, Klaus Gramann, Martin Buss:
  Adaptive Spatial Filters with predefined Region of Interest for EEG based Brain-Computer-Interfaces. 537-544
  
• Jonathan Harel, Christof Koch, Pietro Perona:
  Graph-Based Visual Saliency. 545-552
  
• Gloria Haro, Gregory Randall, Guillermo Sapiro:
  Stratification Learning: Detecting Mixed Density and Dimensionality in High Dimensional Point Clouds. 553-560
  
• Matthias Hein, Markus Maier:
  Manifold Denoising. 561-568
  
• Ralf Herbrich, Tom Minka, Thore Graepel:
  TrueSkill^TM: A Bayesian Skill Rating System. 569-576
  
• Mark Herbster, Massimiliano Pontil:
  Prediction on a Graph with a Perceptron. 577-584
  
• Alfred O. Hero III:
  Geometric entropy minimization (GEM) for anomaly detection and localization. 585-592
  
• John R. Hershey, Trausti T. Kristjansson, Steven J. Rennie, Peder A. Olsen:
  Single Channel Speech Separation Using Factorial Dynamics. 593-600
  
• Jiayuan Huang, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Bernhard Schölkopf:
  Correcting Sample Selection Bias by Unlabeled Data. 601-608
  
• Ke Huang, Selin Aviyente:
  Sparse Representation for Signal Classification. 609-616
  
• Ling Huang, XuanLong Nguyen, Minos N. Garofalakis, Michael I. Jordan, Anthony D. Joseph, Nina Taft:
  In-Network PCA and Anomaly Detection. 617-624
  
• Alexander T. Ihler, Padhraic Smyth:
  Learning Time-Intensity Profiles of Human Activity using Non-Parametric Bayesian Models. 625-632
  
• Robert Jenssen, Torbjørn Eltoft, Mark Girolami, Deniz Erdogmus:
  Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm. 633-640
  
• Mark Johnson, Thomas L. Griffiths, Sharon Goldwater:
  Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models. 641-648
  
• Amit Kagian, Gideon Dror, Tommer Leyvand, Daniel Cohen-Or, Eytan Ruppin:
  A Humanlike Predictor of Facial Attractiveness. 649-656
  
• Anitha Kannan, John M. Winn, Carsten Rother:
  Clustering appearance and shape by learning jigsaws. 657-664
  
• Yoshinobu Kawahara, Takehisa Yairi, Kazuo Machida:
  A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems. 665-672
  
• S. Sathiya Keerthi, Vikas Sindhwani, Olivier Chapelle:
  An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models. 673-680
  
• Charles Kemp, Patrick Shafto, Allison Berke, Joshua B. Tenenbaum:
  Combining causal and similarity-based reasoning. 681-688
  
• Wolf Kienzle, Felix A. Wichmann, Bernhard Schölkopf, Matthias O. Franz:
  A Nonparametric Approach to Bottom-Up Visual Saliency. 689-696
  
• Seyoung Kim, Padhraic Smyth:
  Hierarchical Dirichlet Processes with Random Effects. 697-704
  
• Joseph M. Kimmel, Richard M. Salter, Peter J. Thomas:
  An Information Theoretic Framework for Eukaryotic Gradient Sensing. 705-712
  
• Stefan Klampfl, Robert A. Legenstein, Wolfgang Maass:
  Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons. 713-720
  
• Ryota Kobayashi, Shigeru Shinomoto:
  Predicting spike times from subthreshold dynamics of a neuron. 721-728
  
• Risi Imre Kondor, Tony Jebara:
  Gaussian and Wishart Hyperkernels. 729-736
  
• Konrad P. Körding, Joshua B. Tenenbaum:
  Causal inference in sensorimotor integration. 737-744
  
• Konrad P. Körding, Joshua B. Tenenbaum, Reza Shadmehr:
  Multiple timescales and uncertainty in motor adaptation. 745-752
  
• Matthias Krauledat, Michael Schröder, Benjamin Blankertz, Klaus-Robert Müller:
  Reducing Calibration Time For Brain-Computer Interfaces: A Clustering Approach. 753-760
  
• Kenichi Kurihara, Max Welling, Nikos A. Vlassis:
  Accelerated Variational Dirichlet Process Mixtures. 761-768
  
• Alexandre Lacasse, François Laviolette, Mario Marchand, Pascal Germain, Nicolas Usunier:
  PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier. 769-776
  
• Julian Laub, Jakob Macke, Klaus-Robert Müller, Felix A. Wichmann:
  Inducing Metric Violations in Human Similarity Judgements. 777-784
  
• Neil D. Lawrence, Guido Sanguinetti, Magnus Rattray:
  Modelling transcriptional regulation using Gaussian Processes. 785-792
  
• Chi-Hoon Lee, Shaojun Wang, Feng Jiao, Dale Schuurmans, Russell Greiner:
  Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields. 793-800
  
• Honglak Lee, Alexis Battle, Rajat Raina, Andrew Y. Ng:
  Efficient sparse coding algorithms. 801-808
  
• Michael D. Lee, Ian G. Fuss, Daniel J. Navarro:
  A Bayesian Approach to Diffusion Models of Decision-Making and Response Time. 809-816
  
• Su-In Lee, Varun Ganapathi, Daphne Koller:
  Efficient Structure Learning of Markov Networks using $L_1$-Regularization. 817-824
  
• Steven Lemm, Christin Schäfer, Gabriel Curio:
  Aggregating Classification Accuracy across Time: Application to Single Trial EEG. 825-832
  
• Máté Lengyel, Peter Dayan:
  Uncertainty, phase and oscillatory hippocampal recall. 833-840
  
• Anat Levin:
  Blind Motion Deblurring Using Image Statistics. 841-848
  
• Roger Levy, T. Florian Jaeger:
  Speakers optimize information density through syntactic reduction. 849-856
  
• Jeremy Lewi, Robert J. Butera, Liam Paninski:
  Real-time adaptive information-theoretic optimization of neurophysiology experiments. 857-864
  
• Ling Li, Hsuan-Tien Lin:
  Ordinal Regression by Extended Binary Classification. 865-872
  
• Ping Li, Kenneth Ward Church, Trevor Hastie:
  Conditional Random Sampling: A Sketch-based Sampling Technique for Sparse Data. 873-880
  
• Wenye Li, Kin-Hong Lee, Kwong-Sak Leung:
  Generalized Regularized Least-Squares Learning with Predefined Features in a Hilbert Space. 881-888
  
• Yi Li, Philip M. Long:
  Learnability and the doubling dimension. 889-896
  
• Jussi T. Lindgren, Aapo Hyvärinen:
  Emergence of conjunctive visual features by quadratic independent component analysis. 897-904
  
• Jennifer Listgarten, Radford M. Neal, Sam T. Roweis, Rachel Puckrin, Sean Cutler:
  Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure. 905-912
  
• Ce Liu, William T. Freeman, Edward H. Adelson:
  Analysis of Contour Motions. 913-920
  
• Philip M. Long, Rocco A. Servedio:
  Attribute-efficient learning of decision lists and linear threshold functions under unconcentrated distributions. 921-928
  
• Le Lu, Gregory D. Hager:
  Dynamic Foreground/Background Extraction from Images and Videos using Random Patches. 929-936
  
• Gediminas Luksys, Jérémie Knüsel, Denis Sheynikhovich, Carmen Sandi, Wulfram Gerstner:
  Effects of Stress and Genotype on Meta-parameter Dynamics in Reinforcement Learning. 937-944
  
• Siwei Lyu, Eero P. Simoncelli:
  Statistical Modeling of Images with Fields of Gaussian Scale Mixtures. 945-952
  
• Michael I. Mandel, Daniel P. W. Ellis, Tony Jebara:
  An EM Algorithm for Localizing Multiple Sound Sources in Reverberant Environments. 953-960
  
• Yi Mao, Guy Lebanon:
  Isotonic Conditional Random Fields and Local Sentiment Flow. 961-968
  
• Graham McNeill, Sethu Vijayakumar:
  Part-based Probabilistic Point Matching using Equivalence Constraints. 969-976
  
• Edward Meeds, Zoubin Ghahramani, Radford M. Neal, Sam T. Roweis:
  Modeling Dyadic Data with Binary Latent Factors. 977-984
  
• Frank Moosmann, Bill Triggs, Frédéric Jurie:
  Fast Discriminative Visual Codebooks using Randomized Clustering Forests. 985-992
  
• Michael C. Mozer, Michael Jones, Michael Shettel:
  Context Effects in Category Learning: An Investigation of Four Probabilistic Models. 993-1000
  
• Chris Murray, Geoffrey J. Gordon:
  Multi-Robot Negotiation: Approximating the Set of Subgame Perfect Equilibria in General-Sum Stochastic Games. 1001-1008
  
• Andriy Myronenko, Xubo B. Song, Miguel Á. Carreira-Perpiñán:
  Non-rigid point set registration: Coherent Point Drift. 1009-1016
  
• Boaz Nadler, Meirav Galun:
  Fundamental Limitations of Spectral Clustering. 1017-1024
  
• Hariharan Narayanan, Mikhail Belkin, Partha Niyogi:
  On the Relation Between Low Density Separation, Spectral Clustering and Graph Cuts. 1025-1032
  
• Daniel J. Navarro, Thomas L. Griffiths:
  A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments. 1033-1040
  
• Danko Nikolic, Stefan Häusler, Wolf Singer, Wolfgang Maass:
  Temporal dynamics of information content carried by neurons in the primary visual cortex. 1041-1048
  
• Lars Omlor, Martin A. Giese:
  Blind source separation for over-determined delayed mixtures. 1049-1056
  
• Thomas Ott, Ruedi Stoop:
  The Neurodynamics of Belief Propagation on Binary Markov Random Fields. 1057-1064
  
• Sandeep Pandey, Christopher Olston:
  Handling Advertisements of Unknown Quality in Search Advertising. 1065-1072
  
• Sridevi Parise, Max Welling:
  Bayesian Model Scoring in Markov Random Fields. 1073-1080
  
• Luis Pérez-Breva, Luis E. Ortiz, Chen-Hsiang Yeang, Tommi Jaakkola:
  Game Theoretic Algorithms for Protein-DNA binding. 1081-1088
  
• Lyndsey C. Pickup, David P. Capel, Stephen J. Roberts, Andrew Zisserman:
  Bayesian Image Super-resolution, Continued. 1089-1096
  
• Yuan (Alan) Qi, Tommi Jaakkola:
  Parameter Expanded Variational Bayesian Methods. 1097-1104
  
• Michael Rabbat, Mário A. T. Figueiredo, Robert D. Nowak:
  Inferring Network Structure from Co-Occurrences. 1105-1112
  
• Ali Rahimi, Ben Recht:
  Unsupervised Regression with Applications to Nonlinear System Identification. 1113-1120
  
• Alexander Rakhlin, Andrea Caponnetto:
  Stability of $K$-Means Clustering. 1121-1128
  
• Deva Ramanan:
  Learning to parse images of articulated bodies. 1129-1136
  
• Marc'Aurelio Ranzato, Christopher S. Poultney, Sumit Chopra, Yann LeCun:
  Efficient Learning of Sparse Representations with an Energy-Based Model. 1137-1144
  
• Martin Raphan, Eero P. Simoncelli:
  Learning to be Bayesian without Supervision. 1145-1152
  
• Nathan D. Ratliff, David M. Bradley, J. Andrew Bagnell, Joel E. Chestnutt:
  Boosting Structured Prediction for Imitation Learning. 1153-1160
  
• Gunnar Rätsch, Sören Sonnenburg:
  Large Scale Hidden Semi-Markov SVMs. 1161-1168
  
• Silvia Richter, Douglas Aberdeen, Jin Yu:
  Natural Actor-Critic for Road Traffic Optimisation. 1169-1176
  
• Konrad Rieck, Pavel Laskov, Sören Sonnenburg:
  Computation of Similarity Measures for Sequential Data using Generalized Suffix Trees. 1177-1184
  
• Daniel M. Roy, Charles Kemp, Vikash K. Mansinghka, Joshua B. Tenenbaum:
  Learning annotated hierarchies from relational data. 1185-1192
  
• Benjamin I. P. Rubinstein, Peter L. Bartlett, J. Hyam Rubinstein:
  Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds. 1193-1200
  
• Jason M. Samonds, Brian Potetz, Tai Sing Lee:
  Neurophysiological Evidence of Cooperative Mechanisms for Stereo Computation. 1201-1208
  
• Ashutosh Saxena, Justin Driemeyer, Justin Kearns, Andrew Y. Ng:
  Robotic Grasping of Novel Objects. 1209-1216
  
• Paul R. Schrater, Rashmi Sundareswara:
  Theory and Dynamics of Perceptual Bistability. 1217-1224
  
• Nicol N. Schraudolph, Simon Günter, S. V. N. Vishwanathan:
  Fast Iterative Kernel PCA. 1225-1232
  
• Matthias Seeger:
  Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods. 1233-1240
  
• Yevgeny Seldin, Noam Slonim, Naftali Tishby:
  Information Bottleneck for Non Co-Occurrence Data. 1241-1248
  
• Fei Sha, Lawrence K. Saul:
  Large Margin Hidden Markov Models for Automatic Speech Recognition. 1249-1256
  
• Gregory Shakhnarovich, Sung-Phil Kim, Michael J. Black:
  Nonlinear physically-based models for decoding motor-cortical population activity. 1257-1264
  
• Shai Shalev-Shwartz, Yoram Singer:
  Convex Repeated Games and Fenchel Duality. 1265-1272
  
• Honghao Shan, Lingyun Zhang, Garrison W. Cottrell:
  Recursive ICA. 1273-1280
  
• Christian R. Shelton, Wesley Huie, Kin Fai Kan:
  Chained Boosting. 1281-1288
  
• Hideaki Shimazaki, Shigeru Shinomoto:
  A recipe for optimizing a time-histogram. 1289-1296
  
• Tobias Sing, Niko Beerenwinkel:
  Mutagenetic tree Fisher kernel improves prediction of HIV drug resistance from viral genotype. 1297-1304
  
• Kyung-Ah Sohn, Eric P. Xing:
  Hidden Markov Dirichlet Process: Modeling Genetic Recombination in Open Ancestral Space. 1305-1312
  
• Florian Steinke, Bernhard Schölkopf, Volker Blanz:
  Learning Dense 3D Correspondence. 1313-1320
  
• Ingo Steinwart, Don R. Hush, Clint Scovel:
  An Oracle Inequality for Clipped Regularized Risk Minimizers. 1321-1328
  
• Amos J. Storkey, Enrico Simonotto, Heather Whalley, Stephen Lawrie, Lawrence Murray, David McGonigle:
  Learning Structural Equation Models for fMRI. 1329-1336
  
• Amos J. Storkey, Masashi Sugiyama:
  Mixture Regression for Covariate Shift. 1337-1344
  
• Graham W. Taylor, Geoffrey E. Hinton, Sam T. Roweis:
  Modeling Human Motion Using Binary Latent Variables. 1345-1352
  
• Yee Whye Teh, David Newman, Max Welling:
  A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation. 1353-1360
  
• Fabian J. Theis:
  Towards a general independent subspace analysis. 1361-1368
  
• Emanuel Todorov:
  Linearly-solvable Markov decision problems. 1369-1376
  
• Ryota Tomioka, Kazuyuki Aihara, Klaus-Robert Müller:
  Logistic Regression for Single Trial EEG Classification. 1377-1384
  
• Lorenzo Torresani, Kuang-chih Lee:
  Large Margin Component Analysis. 1385-1392
  
• Lorenzo Torresani, Peggy Hackney, Christoph Bregler:
  Learning Motion Style Synthesis from Perceptual Observations. 1393-1400
  
• Ivor W. Tsang, James T. Kwok:
  Large-Scale Sparsified Manifold Regularization. 1401-1408
  
• Joseph P. Turian, Benjamin Wellington, I. Dan Melamed:
  Scalable Discriminative Learning for Natural Language Parsing and Translation. 1409-1416
  
• Hamed Valizadegan, Rong Jin:
  Generalized Maximum Margin Clustering and Unsupervised Kernel Learning. 1417-1424
  
• Andrea Vedaldi, Stefano Soatto:
  A Complexity-Distortion Approach to Joint Pattern Alignment. 1425-1432
  
• René Vidal:
  Online Clustering of Moving Hyperplanes. 1433-1440
  
• Jade P. Vinson, David DeCaprio, Matthew D. Pearson, Stacey Luoma, James E. Galagan:
  Comparative Gene Prediction using Conditional Random Fields. 1441-1448
  
• S. V. N. Vishwanathan, Karsten M. Borgwardt, Nicol N. Schraudolph:
  Fast Computation of Graph Kernels. 1449-1456
  
• Thomas Voegtlin:
  Temporal Coding using the Response Properties of Spiking Neurons. 1457-1464
  
• Martin J. Wainwright, Pradeep D. Ravikumar, John D. Lafferty:
  High-Dimensional Graphical Model Selection Using $\ell_1$-Regularized Logistic Regression. 1465-1472
  
• Yingxue Wang, Rodney J. Douglas, Shih-Chii Liu:
  Attentional Processing on a Spike-Based VLSI Neural Network. 1473-1480
  
• Manfred K. Warmuth, Dima Kuzmin:
  Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension. 1481-1488
  
• Kilian Q. Weinberger, Fei Sha, Qihui Zhu, Lawrence K. Saul:
  Graph Laplacian Regularization for Large-Scale Semidefinite Programming. 1489-1496
  
• Oliver Williams:
  A Switched Gaussian Process for Estimating Disparity and Segmentation in Binocular Stereo. 1497-1504
  
• David P. Wipf, Rey Ramírez, Jason A. Palmer, Scott Makeig, Bhaskar D. Rao:
  Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization. 1505-1512
  
• Frank Wood, Thomas L. Griffiths:
  Particle Filtering for Nonparametric Bayesian Matrix Factorization. 1513-1520
  
• Lin Wu, Pierre Baldi:
  A Scalable Machine Learning Approach to Go. 1521-1528
  
• Mingrui Wu, Bernhard Schölkopf:
  A Local Learning Approach for Clustering. 1529-1536
  
• Huan Xu, Shie Mannor:
  The Robustness-Performance Tradeoff in Markov Decision Processes. 1537-1544
  
• Angela J. Yu:
  Optimal Change-Detection and Spiking Neurons. 1545-1552
  
• Kai Yu, Wei Chu, Shipeng Yu, Volker Tresp, Zhao Xu:
  Stochastic Relational Models for Discriminative Link Prediction. 1553-1560
  
• Ron Zass, Amnon Shashua:
  Nonnegative Sparse PCA. 1561-1568
  
• Ron Zass, Amnon Shashua:
  Doubly Stochastic Normalization for Spectral Clustering. 1569-1576
  
• Kai Zhang, James T. Kwok:
  Simplifying Mixture Models through Function Approximation. 1577-1584
  
• Xinhua Zhang, Wee Sun Lee:
  Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms. 1585-1592
  
• Zhenyue Zhang, Jing Wang:
  MLLE: Modified Locally Linear Embedding Using Multiple Weights. 1593-1600
  
• Dengyong Zhou, Jiayuan Huang, Bernhard Schölkopf:
  Learning with Hypergraphs: Clustering, Classification, and Embedding. 1601-1608
  
• Zhi-Hua Zhou, Min-Ling Zhang:
  Multi-Instance Multi-Label Learning with Application to Scene Classification. 1609-1616
  
• Long Zhu, Yuanhao Chen, Alan L. Yuille:
  Unsupervised Learning of a Probabilistic Grammar for Object Detection and Parsing. 1617-1624
  
• Johanna M. Zumer, Hagai Thomas Attias, Kensuke Sekihara, Srikantan S. Nagarajan:
  A Probabilistic Algorithm Integrating Source Localization and Noise Suppression of MEG and EEG data. 1625-1632
  

Last update Wed Feb 15 04:19:23 2012 CET by the DBLP Team — Data released under the ODC-BY 1.0 license — See also our legal information page