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ORCIDNIPS 2008: Vancouver, British Columbia, Canada
- Daphne Koller, Dale Schuurmans, Yoshua Bengio, Léon Bottou:
Advances in Neural Information Processing Systems 21, Proceedings of the Twenty-Second Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 8-11, 2008. Curran Associates, Inc. 2009 - Daniel E. Acuna, 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 M. 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 R. 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 L. Austerweil, Thomas L. Griffiths:
Analyzing human feature learning as nonparametric Bayesian inference. 97-104 - Francis R. 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 D. Wood, Jonathan W. 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 M. 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 A. 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 Schmidt, Nando de Freitas:
An interior-point stochastic approximation method and an L1-regularized delta rule. 233-240 - Rui M. Castro, Charles Kalish, Robert D. 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 D. 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 Jay 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 R. 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 R. 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 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 W. 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 R. 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 J. 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 F. 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 N. 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 W. 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, Alexander J. Smola:
Kernelized Sorting. 1289-1296 - Gerald T. 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 G. 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 Ravikumar, Garvesh Raskutti, Martin J. Wainwright, Bin Yu:
Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of l1-regularized MLE. 1329-1336 - Pradeep 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 Legenstein:
On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing. 1425-1432 - Gabriele Beate 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 A. Sontag, Amir Globerson, Tommi S. 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 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 W. 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, Alexander 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 A. 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
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