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ORCIDNIPS 2006: Vancouver, British Columbia, Canada
- Bernhard Schölkopf, John C. Platt, Thomas Hofmann:
Advances in Neural Information Processing Systems 19, Proceedings of the Twentieth Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 4-7, 2006. MIT Press 2007, ISBN 0-262-19568-2 - 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 J. 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 A. 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 J. Belongie, Vincent C. 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 A. Girolami, Mingjun Zhong:
Data Integration for Classification Problems Employing Gaussian Process Priors. 465-472 - Amir Globerson, Tommi S. 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:
TrueSkillTM: 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 A. 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 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 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 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 H. 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 J. 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 L1-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 S. 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 S. Jaakkola:
Parameter Expanded Variational Bayesian Methods. 1097-1104 - Michael G. 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 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 W. 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 M. Lawrie, Lawrence Murray, David J. 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 Ravikumar, John D. Lafferty:
High-Dimensional Graphical Model Selection Using ℓ1-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 D. 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
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