NIPS 2008: Vancouver, British Columbia, Canada

• Daniel Acuña, Paul R. Schrater:
  Structure Learning in Human Sequential Decision-Making. 1-8
  
• Ryan Prescott Adams, Iain Murray, David J. C. MacKay:
  The Gaussian Process Density Sampler. 9-16
  
• Deepak Agarwal, Bee-Chung Chen, Pradheep Elango, Nitin Motgi, Seung-Taek Park, Raghu Ramakrishnan, Scott Roy, Joe Zachariah:
  Online Models for Content Optimization. 17-24
  
• Nir Ailon:
  Reconciling Real Scores with Binary Comparisons: A New Logistic Based Model for Ranking. 25-32
  
• Edoardo Airoldi, David M. Blei, Stephen E. Fienberg, Eric P. Xing:
  Mixed Membership Stochastic Blockmodels. 33-40
  
• Ijaz Akhter, Yaser Sheikh, Sohaib Khan, Takeo Kanade:
  Nonrigid Structure from Motion in Trajectory Space. 41-48
  
• Norm Aleks, Stuart Russell, Michael G. Madden, Diane Morabito, Kristan Staudenmayer, Mitchell J. Cohen, Geoffrey T. Manley:
  Probabilistic detection of short events, with application to critical care monitoring. 49-56
  
• Mauricio A. Álvarez, Neil D. Lawrence:
  Sparse Convolved Gaussian Processes for Multi-output Regression. 57-64
  
• Massih-Reza Amini, François Laviolette, Nicolas Usunier:
  A Transductive Bound for the Voted Classifier with an Application to Semi-supervised Learning. 65-72
  
• Cédric Archambeau, Francis Bach:
  Sparse probabilistic projections. 73-80
  
• Arthur U. Asuncion, Padhraic Smyth, Max Welling:
  Asynchronous Distributed Learning of Topic Models. 81-88
  
• Peter Auer, Thomas Jaksch, Ronald Ortner:
  Near-optimal Regret Bounds for Reinforcement Learning. 89-96
  
• Joseph Austerweil, Thomas L. Griffiths:
  Analyzing human feature learning as nonparametric Bayesian inference. 97-104
  
• Francis Bach:
  Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning. 105-112
  
• J. Andrew Bagnell, David M. Bradley:
  Differentiable Sparse Coding. 113-120
  
• Shai Ben-David, Margareta Ackerman:
  Measures of Clustering Quality: A Working Set of Axioms for Clustering. 121-128
  
• Pietro Berkes, Frank Wood, Jonathan Pillow:
  Characterizing neural dependencies with copula models. 129-136
  
• Patrice Bertail, Stéphan Clémençon, Nicolas Vayatis:
  On Bootstrapping the ROC Curve. 137-144
  
• Steffen Bickel, Christoph Sawade, Tobias Scheffer:
  Transfer Learning by Distribution Matching for Targeted Advertising. 145-152
  
• Matthew B. Blaschko, Arthur Gretton:
  Learning Taxonomies by Dependence Maximization. 153-160
  
• Phil Blunsom, Trevor Cohn, Miles Osborne:
  Bayesian Synchronous Grammar Induction. 161-168
  
• Matthew Botvinick, James An:
  Goal-directed decision making in prefrontal cortex: a computational framework. 169-176
  
• Alexandre Bouchard-Côté, Michael I. Jordan, Dan Klein:
  Efficient Inference in Phylogenetic InDel Trees. 177-184
  
• Jordan L. Boyd-Graber, David M. Blei:
  Syntactic Topic Models. 185-192
  
• Alexander Braunstein, Zhi Wei, Shane T. Jensen, Jon D. McAuliffe:
  A spatially varying two-sample recombinant coalescent, with applications to HIV escape response. 193-200
  
• Sébastien Bubeck, Rémi Munos, Gilles Stoltz, Csaba Szepesvári:
  Online Optimization in X-Armed Bandits. 201-208
  
• Charles F. Cadieu, Bruno A. Olshausen:
  Learning Transformational Invariants from Natural Movies. 209-216
  
• Ben Calderhead, Mark Girolami, Neil D. Lawrence:
  Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes. 217-224
  
• Guangzhi Cao, Charles A. Bouman:
  Covariance Estimation for High Dimensional Data Vectors Using the Sparse Matrix Transform. 225-232
  
• Peter Carbonetto, Mark W. Schmidt, Nando de Freitas:
  An interior-point stochastic approximation method and an L1-regularized delta rule. 233-240
  
• Rui M. Castro, Charles Kalish, Robert Nowak, Ruichen Qian, Timothy T. Rogers, Xiaojin Zhu:
  Human Active Learning. 241-248
  
• Giovanni Cavallanti, Nicolò Cesa-Bianchi, Claudio Gentile:
  Linear Classification and Selective Sampling Under Low Noise Conditions. 249-256
  
• Volkan Cevher, Marco F. Duarte, Chinmay Hegde, Richard G. Baraniuk:
  Sparse Signal Recovery Using Markov Random Fields. 257-264
  
• Kian Ming Adam Chai, Christopher K. I. Williams, Stefan Klanke, Sethu Vijayakumar:
  Multi-task Gaussian Process Learning of Robot Inverse Dynamics. 265-272
  
• Deepayan Chakrabarti, Ravi Kumar, Filip Radlinski, Eli Upfal:
  Mortal Multi-Armed Bandits. 273-280
  
• Olivier Chapelle, Chuong B. Do, Quoc V. Le, Alexander J. Smola, Choon Hui Teo:
  Tighter Bounds for Structured Estimation. 281-288
  
• Kamalika Chaudhuri, Claire Monteleoni:
  Privacy-preserving logistic regression. 289-296
  
• Silvia Chiappa, Jens Kober, Jan Peters:
  Using Bayesian Dynamical Systems for Motion Template Libraries. 297-304
  
• Stéphan Clémençon, Nicolas Vayatis:
  Empirical performance maximization for linear rank statistics. 305-312
  
• Stéphan Clémençon, Nicolas Vayatis:
  Overlaying classifiers: a practical approach for optimal ranking. 313-320
  
• Shay B. Cohen, Kevin Gimpel, Noah A. Smith:
  Logistic Normal Priors for Unsupervised Probabilistic Grammar Induction. 321-328
  
• Kevyn Collins-Thompson:
  Estimating Robust Query Models with Convex Optimization. 329-336
  
• Pierre-Arnaud Coquelin, Romain Deguest, Rémi Munos:
  Particle Filter-based Policy Gradient in POMDPs. 337-344
  
• Koby Crammer, Mark Dredze, Fernando Pereira:
  Exact Convex Confidence-Weighted Learning. 345-352
  
• Wenyuan Dai, Yuqiang Chen, Gui-Rong Xue, Qiang Yang, Yong Yu:
  Translated Learning: Transfer Learning across Different Feature Spaces. 353-360
  
• Sanmay Das, Malik Magdon-Ismail:
  Adapting to a Market Shock: Optimal Sequential Market-Making. 361-368
  
• Peter Dayan:
  Load and Attentional Bayes. 369-376
  
• Ofer Dekel:
  From Online to Batch Learning with Cutoff-Averaging. 377-384
  
• Dotan Di Castro, Dmitry Volkinshtein, Ron Meir:
  Temporal Difference Based Actor Critic Learning - Convergence and Neural Implementation. 385-392
  
• Doug Downey, Oren Etzioni:
  Look Ma, No Hands: Analyzing the Monotonic Feature Abstraction for Text Classification. 393-400
  
• Miroslav Dudík, Steven J. Phillips:
  Generative and Discriminative Learning with Unknown Labeling Bias. 401-408
  
• Laurent El Ghaoui, Assane Gueye:
  A Convex Upper Bound on the Log-Partition Function for Binary Distributions. 409-416
  
• Gal Elidan, Stephen Gould:
  Learning Bounded Treewidth Bayesian Networks. 417-424
  
• Dominik Endres, Peter Földiák:
  Interpreting the neural code with Formal Concept Analysis. 425-432
  
• Lev Faivishevsky, Jacob Goldberger:
  ICA based on a Smooth Estimation of the Differential Entropy. 433-440
  
• Amir Massoud Farahmand, Mohammad Ghavamzadeh, Csaba Szepesvári, Shie Mannor:
  Regularized Policy Iteration. 441-448
  
• Alyson K. Fletcher, Sundeep Rangan, Vivek K. Goyal:
  Resolution Limits of Sparse Coding in High Dimensions. 449-456
  
• Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky:
  Nonparametric Bayesian Learning of Switching Linear Dynamical Systems. 457-464
  
• Paolo Frasconi, Andrea Passerini:
  Predicting the Geometry of Metal Binding Sites from Protein Sequence. 465-472
  
• Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf:
  Characteristic Kernels on Groups and Semigroups. 473-480
  
• C. C. Alan Fung, K. Y. Michael Wong, Si Wu:
  Tracking Changing Stimuli in Continuous Attractor Neural Networks. 481-488
  
• Pierre Garrigues, Laurent El Ghaoui:
  An Homotopy Algorithm for the Lasso with Online Observations. 489-496
  
• Jan Gasthaus, Frank Wood, Dilan Görür, Yee Whye Teh:
  Dependent Dirichlet Process Spike Sorting. 497-504
  
• Sharad Goel, John Langford, Alexander L. Strehl:
  Predictive Indexing for Fast Search. 505-512
  
• Vicenç Gómez, Andreas Kaltenbrunner, Vicente López, Hilbert J. Kappen:
  Self-organization using synaptic plasticity. 513-520
  
• Dilan Görür, Yee Whye Teh:
  An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering. 521-528
  
• Hans Peter Graf, Srihari Cadambi, Igor Durdanovic, Venkata Jakkula, Murugan Sankaradass, Eric Cosatto, Srimat T. Chakradhar:
  A Massively Parallel Digital Learning Processor. 529-536
  
• Yves Grandvalet, Alain Rakotomamonjy, Joseph Keshet, Stéphane Canu:
  Support Vector Machines with a Reject Option. 537-544
  
• Alex Graves, Jürgen Schmidhuber:
  Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks. 545-552
  
• Thomas L. Griffiths, Christopher G. Lucas, Joseph Williams, Michael L. Kalish:
  Modeling human function learning with Gaussian processes. 553-560
  
• Moritz Grosse-Wentrup:
  Understanding Brain Connectivity Patterns during Motor Imagery for Brain-Computer Interfacing. 561-568
  
• Yuhong Guo:
  Supervised Exponential Family Principal Component Analysis via Convex Optimization. 569-576
  
• Abhinav Gupta, Jianbo Shi, Larry S. Davis:
  A "Shape Aware" Model for semi-supervised Learning of Objects and its Context. 577-584
  
• Ralf M. Haefner, Bruce G. Cumming:
  An improved estimator of Variance Explained in the presence of noise. 585-592
  
• Adrian Haith, Carl P. T. Jackson, R. Chris Miall, Sethu Vijayakumar:
  Unifying the Sensory and Motor Components of Sensorimotor Adaptation. 593-600
  
• Jihun Ham, Daniel D. Lee:
  Extended Grassmann Kernels for Subspace-Based Learning. 601-608
  
• Zaïd Harchaoui, Francis Bach, Eric Moulines:
  Kernel Change-point Analysis. 609-616
  
• Stefan Haufe, Vadim V. Nikulin, Andreas Ziehe, Klaus-Robert Müller, Guido Nolte:
  Estimating vector fields using sparse basis field expansions. 617-624
  
• Xuming He, Richard S. Zemel:
  Learning Hybrid Models for Image Annotation with Partially Labeled Data. 625-632
  
• Geremy Heitz, Gal Elidan, Benjamin Packer, Daphne Koller:
  Shape-Based Object Localization for Descriptive Classification. 633-640
  
• Geremy Heitz, Stephen Gould, Ashutosh Saxena, Daphne Koller:
  Cascaded Classification Models: Combining Models for Holistic Scene Understanding. 641-648
  
• Mark Herbster, Guy Lever, Massimiliano Pontil:
  Online Prediction on Large Diameter Graphs. 649-656
  
• Mark Herbster, Massimiliano Pontil, Sergio Rojas Galeano:
  Fast Prediction on a Tree. 657-664
  
• N. Jeremy Hill, Jason Farquhar, Suzanna Martens, Felix Bießmann, Bernhard Schölkopf:
  Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance. 665-672
  
• Michael P. Holmes, Alexander G. Gray, Charles Lee Isbell Jr.:
  QUIC-SVD: Fast SVD Using Cosine Trees. 673-680
  
• Xiaodi Hou, Liqing Zhang:
  Dynamic visual attention: searching for coding length increments. 681-688
  
• Patrik O. Hoyer, Dominik Janzing, Joris M. Mooij, Jonas Peters, Bernhard Schölkopf:
  Nonlinear causal discovery with additive noise models. 689-696
  
• Jim C. Huang, Brendan J. Frey:
  Structured ranking learning using cumulative distribution networks. 697-704
  
• Ling Huang, Donghui Yan, Michael I. Jordan, Nina Taft:
  Spectral Clustering with Perturbed Data. 705-712
  
• Juan Huo, Zhijun Yang, Alan F. Murray:
  Bio-inspired Real Time Sensory Map Realignment in a Robotic Barn Owl. 713-720
  
• Zakria Hussain, John Shawe-Taylor:
  Theory of matching pursuit. 721-728
  
• Quentin J. M. Huys, Joshua T. Vogelstein, Peter Dayan:
  Psychiatry: Insights into depression through normative decision-making models. 729-736
  
• Michael Isard, John MacCormick, Kannan Achan:
  Continuously-adaptive discretization for message-passing algorithms. 737-744
  
• Laurent Jacob, Francis Bach, Jean-Philippe Vert:
  Clustered Multi-Task Learning: A Convex Formulation. 745-752
  
• Srikanth Jagabathula, Devavrat Shah:
  Inferring rankings under constrained sensing. 753-760
  
• Prateek Jain, Brian Kulis, Inderjit S. Dhillon, Kristen Grauman:
  Online Metric Learning and Fast Similarity Search. 761-768
  
• Viren Jain, H. Sebastian Seung:
  Natural Image Denoising with Convolutional Networks. 769-776
  
• Shuiwang Ji, Liang Sun, Rong Jin, Jieping Ye:
  Multi-label Multiple Kernel Learning. 777-784
  
• Matt Jones, Michael C. Mozer, Sachiko Kinoshita:
  Optimal Response Initiation: Why Recent Experience Matters. 785-792
  
• Sham M. Kakade, Karthik Sridharan, Ambuj Tewari:
  On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization. 793-800
  
• Sham M. Kakade, Ambuj Tewari:
  On the Generalization Ability of Online Strongly Convex Programming Algorithms. 801-808
  
• Takafumi Kanamori, Shohei Hido, Masashi Sugiyama:
  Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection. 809-816
  
• Kentaro Katahira, Jun Nishikawa, Kazuo Okanoya, Masato Okada:
  Extracting State Transition Dynamics from Multiple Spike Trains with Correlated Poisson HMM. 817-824
  
• Charles Kemp, Fei Xu:
  An ideal observer model of infant object perception. 825-832
  
• JooSeuk Kim, Clayton D. Scott:
  Performance analysis for L_2 kernel classification. 833-840
  
• Tae-Kyun Kim, Roberto Cipolla:
  MCBoost: Multiple Classifier Boosting for Perceptual Co-clustering of Images and Visual Features. 841-856
  
• Jens Kober, Jan Peters:
  Policy Search for Motor Primitives in Robotics. 849-856
  
• Christoph Kolodziejski, Bernd Porr, Minija Tamosiunaite, Florentin Wörgötter:
  On the asymptotic equivalence between differential Hebbian and temporal difference learning using a local third factor. 857-864
  
• Nikos Komodakis, Nikos Paragios, Georgios Tziritas:
  Clustering via LP-based Stabilities. 865-872
  
• Lukas Kroc, Ashish Sabharwal, Bart Selman:
  Counting Solution Clusters in Graph Coloring Problems Using Belief Propagation. 873-880
  
• Pavel P. Kuksa, Pai-Hsi Huang, Vladimir Pavlovic:
  Scalable Algorithms for String Kernels with Inexact Matching. 881-888
  
• M. Pawan Kumar, Philip H. S. Torr:
  Improved Moves for Truncated Convex Models. 889-896
  
• Simon Lacoste-Julien, Fei Sha, Michael I. Jordan:
  DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification. 897-904
  
• John Langford, Lihong Li, Tong Zhang:
  Sparse Online Learning via Truncated Gradient. 905-912
  
• Longin Jan Latecki, ChengEn Lu, Marc Sobel, Xiang Bai:
  Multiscale Random Fields with Application to Contour Grouping. 913-920
  
• Jonathan Le Roux, Alain de Cheveigné, Lucas C. Parra:
  Adaptive Template Matching with Shift-Invariant Semi-NMF. 921-928
  
• Dongryeol Lee, Alexander G. Gray:
  Fast High-dimensional Kernel Summations Using the Monte Carlo Multipole Method. 929-936
  
• Roger P. Levy, Florencia Reali, Thomas L. Griffiths:
  Modeling the effects of memory on human online sentence processing with particle filters. 937-944
  
• Jeremy Lewi, Robert J. Butera, David M. Schneider, Sarah M. N. Woolley, Liam Paninski:
  Designing neurophysiology experiments to optimally constrain receptive field models along parametric submanifolds. 945-952
  
• Ping Li, Kenneth Ward Church, Trevor Hastie:
  One sketch for all: Theory and Application of Conditional Random Sampling. 953-960
  
• Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh:
  Dimensionality Reduction for Data in Multiple Feature Representations. 961-968
  
• Han Liu, John D. Lafferty, Larry A. Wasserman:
  Nonparametric regression and classification with joint sparsity constraints. 969-976
  
• Philip M. Long, Rocco A. Servedio:
  Adaptive Martingale Boosting. 977-984
  
• Christopher G. Lucas, Thomas L. Griffiths, Fei Xu, Christine Fawcett:
  A rational model of preference learning and choice prediction by children. 985-992
  
• Elliot A. Ludvig, Richard S. Sutton, Eric Verbeek, E. James Kehoe:
  A computational model of hippocampal function in trace conditioning. 993-1000
  
• Gediminas Luksys, Carmen Sandi, Wulfram Gerstner:
  Stress, noradrenaline, and realistic prediction of mouse behaviour using reinforcement learning. 1001-1008
  
• Siwei Lyu, Eero P. Simoncelli:
  Reducing statistical dependencies in natural signals using radial Gaussianization. 1009-1016
  
• Lester Mackey:
  Deflation Methods for Sparse PCA. 1017-1024
  
• Markus Maier, Ulrike von Luxburg, Matthias Hein:
  Influence of graph construction on graph-based clustering measures. 1025-1032
  
• Julien Mairal, Francis Bach, Jean Ponce, Guillermo Sapiro, Andrew Zisserman:
  Supervised Dictionary Learning. 1033-1040
  
• Yishay Mansour, Mehryar Mohri, Afshin Rostamizadeh:
  Domain Adaptation with Multiple Sources. 1041-1048
  
• Hamed Masnadi-Shirazi, Nuno Vasconcelos:
  On the Design of Loss Functions for Classification: theory, robustness to outliers, and SavageBoost. 1049-1056
  
• Julian John McAuley, Tibério S. Caetano, Alexander J. Smola:
  Robust Near-Isometric Matching via Structured Learning of Graphical Models. 1057-1064
  
• Mahdi Milani Fard, Joelle Pineau:
  MDPs with Non-Deterministic Policies. 1065-1072
  
• Tom Minka, John M. Winn:
  Gates. 1073-1080
  
• Andriy Mnih, Geoffrey E. Hinton:
  A Scalable Hierarchical Distributed Language Model. 1081-1088
  
• Shakir Mohamed, Katherine A. Heller, Zoubin Ghahramani:
  Bayesian Exponential Family PCA. 1089-1096
  
• Mehryar Mohri, Afshin Rostamizadeh:
  Rademacher Complexity Bounds for Non-I.I.D. Processes. 1097-1104
  
• Joris M. Mooij, Hilbert J. Kappen:
  Bounds on marginal probability distributions. 1105-1112
  
• Vlad I. Morariu, Balaji Vasan Srinivasan, Vikas C. Raykar, Ramani Duraiswami, Larry S. Davis:
  Automatic online tuning for fast Gaussian summation. 1113-1120
  
• Mehmet K. Muezzinoglu, Alexander Vergara, Ramón Huerta, Thomas Nowotny, Nikolai Rulkov, Henry D. I. Abarbanel, Allen I. Selverston, Mikhail I. Rabinovich:
  Artificial Olfactory Brain for Mixture Identification. 1121-1128
  
• Indraneel Mukherjee, David M. Blei:
  Relative Performance Guarantees for Approximate Inference in Latent Dirichlet Allocation. 1129-1136
  
• Iain Murray, Ruslan Salakhutdinov:
  Evaluating probabilities under high-dimensional latent variable models. 1137-1144
  
• Vinod Nair, Geoffrey E. Hinton:
  Implicit Mixtures of Restricted Boltzmann Machines. 1145-1152
  
• Rama Natarajan, Iain Murray, Ladan Shams, Richard S. Zemel:
  Characterizing response behavior in multisensory perception with conflicting cues. 1153-1160
  
• Sahand Negahban, Martin J. Wainwright:
  Phase transitions for high-dimensional joint support recovery. 1161-1168
  
• Bernhard Nessler, Michael Pfeiffer, Wolfgang Maass:
  Hebbian Learning of Bayes Optimal Decisions. 1169-1176
  
• Gerhard Neumann, Jan Peters:
  Fitted Q-iteration by Advantage Weighted Regression. 1177-1184
  
• Minh Hoai Nguyen, Fernando De la Torre:
  Robust Kernel Principal Component Analysis. 1185-1192
  
• Duy Nguyen-Tuong, Matthias Seeger, Jan Peters:
  Local Gaussian Process Regression for Real Time Online Model Learning. 1193-1200
  
• Richard Nock, Frank Nielsen:
  On the Efficient Minimization of Classification Calibrated Surrogates. 1201-1208
  
• Ali Nouri, Michael L. Littman:
  Multi-resolution Exploration in Continuous Spaces. 1209-1216
  
• Guillaume Obozinski, Martin J. Wainwright, Michael I. Jordan:
  High-dimensional support union recovery in multivariate regression. 1217-1224
  
• Masafumi Oizumi, Toshiyuki Ishii, Kazuya Ishibashi, Toshihiko Hosoya, Masato Okada:
  A general framework for investigating how far the decoding process in the brain can be simplified. 1225-1232
  
• Arno Onken, Steffen Grünewälder, Matthias H. J. Munk, Klaus Obermayer:
  Modeling Short-term Noise Dependence of Spike Counts in Macaque Prefrontal Cortex. 1233-1240
  
• Manfred Opper, Ulrich Paquet, Ole Winther:
  Improving on Expectation Propagation. 1241-1248
  
• Jean-Philippe Pellet, André Elisseeff:
  Finding Latent Causes in Causal Networks: an Efficient Approach Based on Markov Blankets. 1249-1256
  
• Fernando Pérez-Cruz:
  Estimation of Information Theoretic Measures for Continuous Random Variables. 1257-1264
  
• Marek Petrik, Bruno Scherrer:
  Biasing Approximate Dynamic Programming with a Lower Discount Factor. 1265-1272
  
• Adam Ponzi, Jeff Wickens:
  Cell Assemblies in Large Sparse Inhibitory Networks of Biologically Realistic Spiking Neurons. 1273-1280
  
• Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang, Hang Li:
  Global Ranking Using Continuous Conditional Random Fields. 1281-1288
  
• Novi Quadrianto, Le Song, Alex J. Smola:
  Kernelized Sorting. 1289-1296
  
• Gerald Quon, Yee Whye Teh, Esther T. Chan, Timothy R. Hughes, Michael Brudno, Quaid Morris:
  A mixture model for the evolution of gene expression in non-homogeneous datasets. 1297-1304
  
• Maxim Raginsky, Svetlana Lazebnik, Rebecca Willett, Jorge Silva:
  Near-minimax recursive density estimation on the binary hypercube. 1305-1312
  
• Ali Rahimi, Benjamin Recht:
  Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning. 1313-1320
  
• Piyush Rai, Hal Daumé III:
  The Infinite Hierarchical Factor Regression Model. 1321-1328
  
• Pradeep D. Ravikumar, Garvesh Raskutti, Martin J. Wainwright, Bin Yu:
  Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of l₁-regularized MLE. 1329-1336
  
• Pradeep D. Ravikumar, Vincent Q. Vu, Bin Yu, Thomas Naselaris, Kendrick N. Kay, Jack L. Gallant:
  Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images. 1337-1344
  
• Debajyoti Ray, Brooks King-Casas, P. Read Montague, Peter Dayan:
  Bayesian Model of Behaviour in Economic Games. 1345-1352
  
• Jeremy Reynolds, Michael C. Mozer:
  Temporal Dynamics of Cognitive Control. 1353-1360
  
• John W. Roberts, Russ Tedrake:
  Signal-to-Noise Ratio Analysis of Policy Gradient Algorithms. 1361-1368
  
• Joshua W. Robinson, Alexander J. Hartemink:
  Non-stationary dynamic Bayesian networks. 1369-1376
  
• Daniel M. Roy, Yee Whye Teh:
  The Mondrian Process. 1377-1384
  
• Paul Ruvolo, Ian R. Fasel, Javier R. Movellan:
  Optimization on a Budget: A Reinforcement Learning Approach. 1385-1392
  
• Kate Saenko, Trevor Darrell:
  Unsupervised Learning of Visual Sense Models for Polysemous Words. 1393-1400
  
• Ted Sandler, John Blitzer, Partha Pratim Talukdar, Lyle H. Ungar:
  Regularized Learning with Networks of Features. 1401-1408
  
• Tanya Schmah, Geoffrey E. Hinton, Richard S. Zemel, Steven L. Small, Stephen C. Strother:
  Generative versus discriminative training of RBMs for classification of fMRI images. 1409-1416
  
• Nicol N. Schraudolph, Dmitry Kamenetsky:
  Efficient Exact Inference in Planar Ising Models. 1417-1424
  
• Benjamin Schrauwen, Lars Buesing, Robert A. Legenstein:
  On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing. 1425-1432
  
• Gabriele Schweikert, Christian Widmer, Bernhard Schölkopf, Gunnar Rätsch:
  An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis. 1433-1440
  
• Matthias W. Seeger, Hannes Nickisch, Rolf Pohmann, Bernhard Schölkopf:
  Bayesian Experimental Design of Magnetic Resonance Imaging Sequences. 1441-1448
  
• Mohak Shah:
  Risk Bounds for Randomized Sample Compressed Classifiers. 1449-1456
  
• Shai Shalev-Shwartz, Sham M. Kakade:
  Mind the Duality Gap: Logarithmic regret algorithms for online optimization. 1457-1464
  
• Ohad Shamir, Naftali Tishby:
  On the Reliability of Clustering Stability in the Large Sample Regime. 1465-1472
  
• Chunhua Shen, Alan Welsh, Lei Wang:
  PSDBoost: Matrix-Generation Linear Programming for Positive Semidefinite Matrices Learning. 1473-1480
  
• Pannagadatta K. Shivaswamy, Tony Jebara:
  Relative Margin Machines. 1481-1488
  
• Lavi Shpigelman, Hagai Lalazar, Eilon Vaadia:
  Kernel-ARMA for Hand Tracking and Brain-Machine interfacing During 3D Motor Control. 1489-1496
  
• Özgür Simsek, Andrew G. Barto:
  Skill Characterization Based on Betweenness. 1497-1504
  
• Vikas Sindhwani, Jianying Hu, Aleksandra Mojsilovic:
  Regularized Co-Clustering with Dual Supervision. 1505-1512
  
• Aarti Singh, Robert D. Nowak, Xiaojin Zhu:
  Unlabeled data: Now it helps, now it doesn't. 1513-1520
  
• Fabian H. Sinz, Matthias Bethge:
  The Conjoint Effect of Divisive Normalization and Orientation Selectivity on Redundancy Reduction. 1521-1528
  
• Andrew Smith, Xiaoming Huo, Hongyuan Zha:
  Convergence and Rate of Convergence of a Manifold-Based Dimension Reduction Algorithm. 1529-1536
  
• David Sontag, Amir Globerson, Tommi Jaakkola:
  Clusters and Coarse Partitions in LP Relaxations. 1537-1544
  
• Karthik Sridharan, Shai Shalev-Shwartz, Nathan Srebro:
  Fast Rates for Regularized Objectives. 1545-1552
  
• Praveen Srinivasan, Liming Wang, Jianbo Shi:
  Grouping Contours Via a Related Image. 1553-1560
  
• Florian Steinke, Matthias Hein:
  Non-parametric Regression Between Manifolds. 1561-1568
  
• Ingo Steinwart, Andreas Christmann:
  Sparsity of SVMs that use the epsilon-insensitive loss. 1569-1576
  
• Matthew J. Streeter, Daniel Golovin:
  An Online Algorithm for Maximizing Submodular Functions. 1577-1584
  
• Erik B. Sudderth, Michael I. Jordan:
  Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes. 1585-1592
  
• Ilya Sutskever, Geoffrey E. Hinton:
  Using matrices to model symbolic relationship. 1593-1600
  
• Ilya Sutskever, Geoffrey E. Hinton, Graham W. Taylor:
  The Recurrent Temporal Restricted Boltzmann Machine. 1601-1608
  
• Richard S. Sutton, Csaba Szepesvári, Hamid Reza Maei:
  A Convergent O(n) Temporal-difference Algorithm for Off-policy Learning with Linear Function Approximation. 1609-1616
  
• Erik Talvitie, Satinder Singh:
  Simple Local Models for Complex Dynamical Systems. 1617-1624
  
• Jennifer Tam, Jirí Simsa, Sean Hyde, Luis von Ahn:
  Breaking Audio CAPTCHAs. 1625-1632
  
• Yik-Cheung Tam, Tanja Schultz:
  Correlated Bigram LSA for Unsupervised Language Model Adaptation. 1633-1640
  
• Michael Tangermann, Matthias Krauledat, Konrad Grzeska, Max Sagebaum, Benjamin Blankertz, Carmen Vidaurre, Klaus-Robert Müller:
  Playing Pinball with non-invasive BCI. 1641-1648
  
• Jonathan Taylor, Doina Precup, Prakash Panangaden:
  Bounding Performance Loss in Approximate MDP Homomorphisms. 1649-1656
  
• Tran The Truyen, Dinh Q. Phung, Hung Hai Bui, Svetha Venkatesh:
  Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data. 1657-1664
  
• Robert E. Tillman, David Danks, Clark Glymour:
  Integrating Locally Learned Causal Structures with Overlapping Variables. 1665-1672
  
• Jo-Anne Ting, Mrinal Kalakrishnan, Sethu Vijayakumar, Stefan Schaal:
  Bayesian Kernel Shaping for Learning Control. 1673-1680
  
• Michalis K. Titsias, Neil D. Lawrence, Magnus Rattray:
  Efficient Sampling for Gaussian Process Inference using Control Variables. 1681-1688
  
• Michael T. Todd, Yael Niv, Jonathan D. Cohen:
  Learning to Use Working Memory in Partially Observable Environments through Dopaminergic Reinforcement. 1689-1696
  
• Jurgen Van Gael, Yee Whye Teh, Zoubin Ghahramani:
  The Infinite Factorial Hidden Markov Model. 1697-1704
  
• Sudheendra Vijayanarasimhan, Kristen Grauman:
  Multi-Level Active Prediction of Useful Image Annotations for Recognition. 1705-1712
  
• Christian Walder, Bernhard Schölkopf:
  Diffeomorphic Dimensionality Reduction. 1713-1720
  
• Yang Wang, Greg Mori:
  Learning a discriminative hidden part model for human action recognition. 1721-1728
  
• Yizao Wang, Jean-Yves Audibert, Rémi Munos:
  Algorithms for Infinitely Many-Armed Bandits. 1729-1736
  
• Kilian Q. Weinberger, Olivier Chapelle:
  Large Margin Taxonomy Embedding for Document Categorization. 1737-1744
  
• Daphna Weinshall, Hynek Hermansky, Alon Zweig, Jie Luo, Holly Brügge Jimison, Frank Ohl, Misha Pavel:
  Beyond Novelty Detection: Incongruent Events, when General and Specific Classifiers Disagree. 1745-1752
  
• Yair Weiss, Antonio Torralba, Robert Fergus:
  Spectral Hashing. 1753-1760
  
• Ydo Wexler, Christopher Meek:
  MAS: a multiplicative approximation scheme for probabilistic inference. 1761-1768
  
• Klaus Wimmer, Marcel Stimberg, Robert Martin, Lars Schwabe, Jorge Mariño, James Schummers, David C. Lyon, Mriganka Sur, Klaus Obermayer:
  Dependence of Orientation Tuning on Recurrent Excitation and Inhibition in a Network Model of V1. 1769-1776
  
• David P. Wipf, Julia P. Owen, Hagai Attias, Kensuke Sekihara, Srikantan S. Nagarajan:
  Estimating the Location and Orientation of Complex, Correlated Neural Activity using MEG. 1777-1784
  
• Qiang Wu, Sayan Mukherjee, Feng Liang:
  Localized Sliced Inverse Regression. 1785-1792
  
• Shuang Wu, Hongjing Lu, Alan L. Yuille:
  Model selection and velocity estimation using novel priors for motion patterns. 1793-1800
  
• Huan Xu, Constantine Caramanis, Shie Mannor:
  Robust Regression and Lasso. 1801-1808
  
• Jing Xu, Thomas L. Griffiths:
  How memory biases affect information transmission: A rational analysis of serial reproduction. 1809-1816
  
• Peng Xu, Timothy K. Horiuchi, Pamela Abshire:
  Short-Term Depression in VLSI Stochastic Synapse. 1817-1824
  
• Zenglin Xu, Rong Jin, Irwin King, Michael R. Lyu:
  An Extended Level Method for Efficient Multiple Kernel Learning. 1825-1832
  
• Benjamin Yackley, Eduardo Corona, Terran Lane:
  Bayesian Network Score Approximation using a Metagraph Kernel. 1833-1840
  
• Yoshihiro Yamanishi:
  Supervised Bipartite Graph Inference. 1841-1848
  
• Haixuan Yang, Irwin King, Michael R. Lyu:
  Learning with Consistency between Inductive Functions and Kernels. 1849-1856
  
• Liu Yang, Rong Jin, Rahul Sukthankar:
  Semi-supervised Learning with Weakly-Related Unlabeled Data: Towards Better Text Categorization. 1857-1864
  
• Zhi Yang, Qi Zhao, Wentai Liu:
  Spike Feature Extraction Using Informative Samples. 1865-1872
  
• Angela J. Yu, Jonathan D. Cohen:
  Sequential effects: Superstition or rational behavior? 1873-1880
  
• Byron M. Yu, John P. Cunningham, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani:
  Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. 1881-1888
  
• Kai Yu, Wei Xu, Yihong Gong:
  Deep Learning with Kernel Regularization for Visual Recognition. 1889-1896
  
• Chao Yuan, Claus Neubauer:
  Variational Mixture of Gaussian Process Experts. 1897-1904
  
• Luke S. Zettlemoyer, Brian Milch, Leslie Pack Kaelbling:
  Multi-Agent Filtering with Infinitely Nested Beliefs. 1905-1912
  
• Liang Zhang, Deepak Agarwal:
  Fast Computation of Posterior Mode in Multi-Level Hierarchical Models. 1913-1920
  
• Tong Zhang:
  Adaptive Forward-Backward Greedy Algorithm for Sparse Learning with Linear Models. 1921-1928
  
• Tong Zhang:
  Multi-stage Convex Relaxation for Learning with Sparse Regularization. 1929-1936
  
• Xinhua Zhang, Le Song, Arthur Gretton, Alex J. Smola:
  Kernel Measures of Independence for non-iid Data. 1937-1944
  
• Yi Zhang, Jeff G. Schneider, Artur Dubrawski:
  Learning the Semantic Correlation: An Alternative Way to Gain from Unlabeled Text. 1945-1952
  
• Deli Zhao, Xiaoou Tang:
  Cyclizing Clusters via Zeta Function of a Graph. 1953-1960
  
• Zhengdong Lu, Todd K. Leen, Jeffrey Kaye:
  Hierarchical Fisher Kernels for Longitudinal Data. 1961-1968
  
• Zhihua Zhang, Michael I. Jordan, Dit-Yan Yeung:
  Posterior Consistency of the Silverman g-prior in Bayesian Model Choice. 1969-1976
  
• Jun Zhu, Eric P. Xing, Bo Zhang:
  Partially Observed Maximum Entropy Discrimination Markov Networks. 1977-1984
  
• Leo Zhu, Yuanhao Chen, Yuan Lin, Chenxi Lin, Alan L. Yuille:
  Recursive Segmentation and Recognition Templates for 2D Parsing. 1985-1992
  
• Shenghuo Zhu, Kai Yu, Yihong Gong:
  Stochastic Relational Models for Large-scale Dyadic Data using MCMC. 1993-2000
  

Last update Thu May 24 04:38:51 2012 CET by the DBLP Team — Data released under the ODC-BY 1.0 license — See also our legal information page