Advances in Neural Information Processing Systems 21 (NIPS 2008)
The papers below appear in Advances in Neural Information Processing Systems 21 edited by D. Koller and D. Schuurmans and Y. Bengio and L. Bottou.They are proceedings from the conference, "Neural Information Processing Systems 2008."
- Structure Learning in Human Sequential Decision-Making Daniel Acuna, Paul R. Schrater
- The Gaussian Process Density Sampler Iain Murray, David MacKay, Ryan P. Adams
- Online Models for Content Optimization Deepak Agarwal, Bee-chung Chen, Pradheep Elango, Nitin Motgi, Seung-taek Park, Raghu Ramakrishnan, Scott Roy, Joe Zachariah
- Reconciling Real Scores with Binary Comparisons: A New Logistic Based Model for Ranking Nir Ailon
- Mixed Membership Stochastic Blockmodels Edo M. Airoldi, David M. Blei, Stephen E. Fienberg, Eric P. Xing
- Nonrigid Structure from Motion in Trajectory Space Ijaz Akhter, Yaser Sheikh, Sohaib Khan, Takeo Kanade
- Probabilistic detection of short events, with application to critical care monitoring Norm Aleks, Stuart J. Russell, Michael G. Madden, Diane Morabito, Kristan Staudenmayer, Mitchell Cohen, Geoffrey T. Manley
- Sparse Convolved Gaussian Processes for Multi-output Regression Mauricio Alvarez, Neil D. Lawrence
- A Transductive Bound for the Voted Classifier with an Application to Semi-supervised Learning Massih Amini, Nicolas Usunier, François Laviolette
- Sparse probabilistic projections Cédric Archambeau, Francis R. Bach
- Asynchronous Distributed Learning of Topic Models Padhraic Smyth, Max Welling, Arthur U. Asuncion
- Near-optimal Regret Bounds for Reinforcement Learning Peter Auer, Thomas Jaksch, Ronald Ortner
- Analyzing human feature learning as nonparametric Bayesian inference Thomas L. Griffiths, Joseph L. Austerweil
- Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning Francis R. Bach
- Differentiable Sparse Coding J. A. Bagnell, David M. Bradley
- Measures of Clustering Quality: A Working Set of Axioms for Clustering Shai Ben-David, Margareta Ackerman
- Characterizing neural dependencies with copula models Pietro Berkes, Frank Wood, Jonathan W. Pillow
- On Bootstrapping the ROC Curve Patrice Bertail, Stéphan J. Clémençcon, Nicolas Vayatis
- Transfer Learning by Distribution Matching for Targeted Advertising Steffen Bickel, Christoph Sawade, Tobias Scheffer
- Learning Taxonomies by Dependence Maximization Matthew Blaschko, Arthur Gretton
- Bayesian Synchronous Grammar Induction Phil Blunsom, Trevor Cohn, Miles Osborne
- Goal-directed decision making in prefrontal cortex: a computational framework Matthew Botvinick, James An
- Efficient Inference in Phylogenetic InDel Trees Alexandre Bouchard-côté, Dan Klein, Michael I. Jordan
- Syntactic Topic Models Jordan L. Boyd-graber, David M. Blei
- A spatially varying two-sample recombinant coalescent, with applications to HIV escape response Alexander Braunstein, Zhi Wei, Shane T. Jensen, Jon D. Mcauliffe
- Online Optimization in X-Armed Bandits Sébastien Bubeck, Gilles Stoltz, Csaba Szepesvári, Rémi Munos
- Learning Transformational Invariants from Natural Movies Charles Cadieu, Bruno A. Olshausen
- Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes Ben Calderhead, Mark Girolami, Neil D. Lawrence
- Covariance Estimation for High Dimensional Data Vectors Using the Sparse Matrix Transform Guangzhi Cao, Charles Bouman
- An interior-point stochastic approximation method and an L1-regularized delta rule Peter Carbonetto, Mark Schmidt, Nando D. Freitas
- Human Active Learning Rui M. Castro, Charles Kalish, Robert Nowak, Ruichen Qian, Tim Rogers, Xiaojin Zhu
- Linear Classification and Selective Sampling Under Low Noise Conditions Giovanni Cavallanti, Nicolò Cesa-bianchi, Claudio Gentile
- Sparse Signal Recovery Using Markov Random Fields Volkan Cevher, Marco F. Duarte, Chinmay Hegde, Richard Baraniuk
- Multi-task Gaussian Process Learning of Robot Inverse Dynamics Christopher Williams, Stefan Klanke, Sethu Vijayakumar, Kian M. Chai
- Mortal Multi-Armed Bandits Deepayan Chakrabarti, Ravi Kumar, Filip Radlinski, Eli Upfal
- Tighter Bounds for Structured Estimation Olivier Chapelle, Chuong B. Do, Choon H. Teo, Quoc V. Le, Alex J. Smola
- Privacy-preserving logistic regression Kamalika Chaudhuri, Claire Monteleoni
- Using Bayesian Dynamical Systems for Motion Template Libraries Silvia Chiappa, Jens Kober, Jan R. Peters
- Empirical performance maximization for linear rank statistics Stéphan J. Clémençcon, Nicolas Vayatis
- Overlaying classifiers: a practical approach for optimal ranking Stéphan J. Clémençcon, Nicolas Vayatis
- Logistic Normal Priors for Unsupervised Probabilistic Grammar Induction Shay B. Cohen, Kevin Gimpel, Noah A. Smith
- Estimating Robust Query Models with Convex Optimization Kevyn Collins-thompson
- Particle Filter-based Policy Gradient in POMDPs Pierre-arnaud Coquelin, Romain Deguest, Rémi Munos
- Exact Convex Confidence-Weighted Learning Koby Crammer, Mark Dredze, Fernando Pereira
- Translated Learning: Transfer Learning across Different Feature Spaces Wenyuan Dai, Yuqiang Chen, Gui-rong Xue, Qiang Yang, Yong Yu
- Adapting to a Market Shock: Optimal Sequential Market-Making Sanmay Das, Malik Magdon-Ismail
- Load and Attentional Bayes Peter Dayan
- From Online to Batch Learning with Cutoff-Averaging Ofer Dekel
- Temporal Difference Based Actor Critic Learning - Convergence and Neural Implementation Dotan D. Castro, Dmitry Volkinshtein, Ron Meir
- Look Ma, No Hands: Analyzing the Monotonic Feature Abstraction for Text Classification Doug Downey, Oren Etzioni
- Generative and Discriminative Learning with Unknown Labeling Bias Steven J. Phillips, Miroslav Dudík
- A Convex Upper Bound on the Log-Partition Function for Binary Distributions Laurent E. Ghaoui, Assane Gueye
- Learning Bounded Treewidth Bayesian Networks Gal Elidan, Stephen Gould
- Interpreting the neural code with Formal Concept Analysis Dominik Endres, Peter Foldiak
- ICA based on a Smooth Estimation of the Differential Entropy Lev Faivishevsky, Jacob Goldberger
- Regularized Policy Iteration Amir M. Farahmand, Mohammad Ghavamzadeh, Shie Mannor, Csaba Szepesvári
- Resolution Limits of Sparse Coding in High Dimensions Sundeep Rangan, Vivek Goyal, Alyson K. Fletcher
- Nonparametric Bayesian Learning of Switching Linear Dynamical Systems Emily Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky
- Predicting the Geometry of Metal Binding Sites from Protein Sequence Paolo Frasconi, Andrea Passerini
- Characteristic Kernels on Groups and Semigroups Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf, Bharath K. Sriperumbudur
- Tracking Changing Stimuli in Continuous Attractor Neural Networks K. Wong, Si Wu, Chi Fung
- An Homotopy Algorithm for the Lasso with Online Observations Pierre Garrigues, Laurent E. Ghaoui
- Dependent Dirichlet Process Spike Sorting Jan Gasthaus, Frank Wood, Dilan Gorur, Yee W. Teh
- Predictive Indexing for Fast Search Sharad Goel, John Langford, Alexander L. Strehl
- Self-organization using synaptic plasticity Vicençc Gómez, Andreas Kaltenbrunner, Vicente López, Hilbert J. Kappen
- An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering Dilan Gorur, Yee W. Teh
- A Massively Parallel Digital Learning Processor Hans P. Graf, Srihari Cadambi, Venkata Jakkula, Murugan Sankaradass, Eric Cosatto, Srimat Chakradhar, Igor Dourdanovic
- Support Vector Machines with a Reject Option Yves Grandvalet, Alain Rakotomamonjy, Joseph Keshet, Stéphane Canu
- Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks Alex Graves, Jürgen Schmidhuber
- Modeling human function learning with Gaussian processes Thomas L. Griffiths, Chris Lucas, Joseph Williams, Michael L. Kalish
- Understanding Brain Connectivity Patterns during Motor Imagery for Brain-Computer Interfacing Moritz Grosse-wentrup
- Supervised Exponential Family Principal Component Analysis via Convex Optimization Yuhong Guo
- A ``Shape Aware'' Model for semi-supervised Learning of Objects and its Context Abhinav Gupta, Jianbo Shi, Larry S. Davis
- An improved estimator of Variance Explained in the presence of noise Ralf M. Haefner, Bruce G. Cumming
- Unifying the Sensory and Motor Components of Sensorimotor Adaptation Adrian Haith, Carl P. Jackson, R. C. Miall, Sethu Vijayakumar
- Extended Grassmann Kernels for Subspace-Based Learning Jihun Hamm, Daniel D. Lee
- Kernel Change-point Analysis Zaïd Harchaoui, Eric Moulines, Francis R. Bach
- Estimating vector fields using sparse basis field expansions Stefan Haufe, Vadim V. Nikulin, Andreas Ziehe, Klaus-Robert Müller, Guido Nolte
- Learning Hybrid Models for Image Annotation with Partially Labeled Data Xuming He, Richard S. Zemel
- Shape-Based Object Localization for Descriptive Classification Geremy Heitz, Gal Elidan, Benjamin Packer, Daphne Koller
- Cascaded Classification Models: Combining Models for Holistic Scene Understanding Geremy Heitz, Stephen Gould, Ashutosh Saxena, Daphne Koller
- Online Prediction on Large Diameter Graphs Mark Herbster, Guy Lever, Massimiliano Pontil
- Fast Prediction on a Tree Mark Herbster, Massimiliano Pontil, Sergio R. Galeano
- Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance Jeremy Hill, Jason Farquhar, Suzanna Martens, Felix Biessmann, Bernhard Schölkopf
- QUIC-SVD: Fast SVD Using Cosine Trees Michael P. Holmes, Jr. Isbell, Charles Lee, Alexander G. Gray
- Dynamic visual attention: searching for coding length increments Xiaodi Hou, Liqing Zhang
- Nonlinear causal discovery with additive noise models Patrik O. Hoyer, Dominik Janzing, Joris M. Mooij, Jonas Peters, Bernhard Schölkopf
- Structured ranking learning using cumulative distribution networks Jim C. Huang, Brendan J. Frey
- Spectral Clustering with Perturbed Data Ling Huang, Donghui Yan, Nina Taft, Michael I. Jordan
- Bio-inspired Real Time Sensory Map Realignment in a Robotic Barn Owl Juan Huo, Zhijun Yang, Alan F. Murray
- Theory of matching pursuit Zakria Hussain, John S. Shawe-taylor
- Psychiatry: Insights into depression through normative decision-making models Quentin J. Huys, Joshua Vogelstein, Peter Dayan
- Continuously-adaptive discretization for message-passing algorithms Michael Isard, John MacCormick, Kannan Achan
- Clustered Multi-Task Learning: A Convex Formulation Laurent Jacob, Jean-philippe Vert, Francis R. Bach
- Inferring rankings under constrained sensing Srikanth Jagabathula, Devavrat Shah
- Online Metric Learning and Fast Similarity Search Prateek Jain, Brian Kulis, Inderjit S. Dhillon, Kristen Grauman
- Natural Image Denoising with Convolutional Networks Viren Jain, Sebastian Seung
- Multi-label Multiple Kernel Learning Shuiwang Ji, Liang Sun, Rong Jin, Jieping Ye
- Optimal Response Initiation: Why Recent Experience Matters Matt Jones, Sachiko Kinoshita, Michael C. Mozer
- On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization Sham M. Kakade, Karthik Sridharan, Ambuj Tewari
- On the Generalization Ability of Online Strongly Convex Programming Algorithms Sham M. Kakade, Ambuj Tewari
- Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection Takafumi Kanamori, Shohei Hido, Masashi Sugiyama
- Extracting State Transition Dynamics from Multiple Spike Trains with Correlated Poisson HMM Kentaro Katahira, Jun Nishikawa, Kazuo Okanoya, Masato Okada
- An ideal observer model of infant object perception Charles Kemp, Fei Xu
- Performance analysis for L\_2 kernel classification Jooseuk Kim, Clayton Scott
- MCBoost: Multiple Classifier Boosting for Perceptual Co-clustering of Images and Visual Features Tae-kyun Kim, Roberto Cipolla
- Policy Search for Motor Primitives in Robotics Jens Kober, Jan R. Peters
- On the asymptotic equivalence between differential Hebbian and temporal difference learning using a local third factor Christoph Kolodziejski, Bernd Porr, Minija Tamosiunaite, Florentin Wörgötter
- Clustering via LP-based Stabilities Nikos Komodakis, Nikos Paragios, Georgios Tziritas
- Counting Solution Clusters in Graph Coloring Problems Using Belief Propagation Lukas Kroc, Ashish Sabharwal, Bart Selman
- Scalable Algorithms for String Kernels with Inexact Matching Pavel P. Kuksa, Pai-hsi Huang, Vladimir Pavlovic
- Improved Moves for Truncated Convex Models Philip Torr, M. P. Kumar
- DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification Simon Lacoste-Julien, Fei Sha, Michael I. Jordan
- Sparse Online Learning via Truncated Gradient John Langford, Lihong Li, Tong Zhang
- Multiscale Random Fields with Application to Contour Grouping Longin J. Latecki, Chengen Lu, Marc Sobel, Xiang Bai
- Adaptive Template Matching with Shift-Invariant Semi-NMF Jonathan L. Roux, Alain D. Cheveigné, Lucas C. Parra
- Fast High-dimensional Kernel Summations Using the Monte Carlo Multipole Method Dongryeol Lee, Alexander G. Gray
- Modeling the effects of memory on human online sentence processing with particle filters Roger P. Levy, Florencia Reali, Thomas L. Griffiths
- Designing neurophysiology experiments to optimally constrain receptive field models along parametric submanifolds Jeremy Lewi, Robert Butera, David M. Schneider, Sarah Woolley, Liam Paninski
- One sketch for all: Theory and Application of Conditional Random Sampling Ping Li, Kenneth W. Church, Trevor J. Hastie
- Dimensionality Reduction for Data in Multiple Feature Representations Yen-yu Lin, Tyng-luh Liu, Chiou-shann Fuh
- Nonparametric regression and classification with joint sparsity constraints Han Liu, Larry Wasserman, John D. Lafferty
- Adaptive Martingale Boosting Phil Long, Rocco Servedio
- A rational model of preference learning and choice prediction by children Christopher G. Lucas, Thomas L. Griffiths, Fei Xu, Christine Fawcett
- A computational model of hippocampal function in trace conditioning Elliot A. Ludvig, Richard S. Sutton, Eric Verbeek, E. J. Kehoe
- Stress, noradrenaline, and realistic prediction of mouse behaviour using reinforcement learning Carmen Sandi, Wulfram Gerstner, Gediminas Lukšys
- Reducing statistical dependencies in natural signals using radial Gaussianization Siwei Lyu, Eero P. Simoncelli
- Deflation Methods for Sparse PCA Lester W. Mackey
- Influence of graph construction on graph-based clustering measures Markus Maier, Ulrike V. Luxburg, Matthias Hein
- Supervised Dictionary Learning Julien Mairal, Jean Ponce, Guillermo Sapiro, Andrew Zisserman, Francis R. Bach
- Domain Adaptation with Multiple Sources Yishay Mansour, Mehryar Mohri, Afshin Rostamizadeh
- On the Design of Loss Functions for Classification: theory, robustness to outliers, and SavageBoost Hamed Masnadi-shirazi, Nuno Vasconcelos
- Robust Near-Isometric Matching via Structured Learning of Graphical Models Alex J. Smola, Julian J. Mcauley, Tibério S. Caetano
- MDPs with Non-Deterministic Policies Mahdi M. Fard, Joelle Pineau
- Gates Tom Minka, John Winn
- A Scalable Hierarchical Distributed Language Model Andriy Mnih, Geoffrey E. Hinton
- Bayesian Exponential Family PCA Shakir Mohamed, Zoubin Ghahramani, Katherine A. Heller
- Rademacher Complexity Bounds for Non-I.I.D. Processes Mehryar Mohri, Afshin Rostamizadeh
- Bounds on marginal probability distributions Joris M. Mooij, Hilbert J. Kappen
- Automatic online tuning for fast Gaussian summation Vlad I. Morariu, Balaji V. Srinivasan, Vikas C. Raykar, Ramani Duraiswami, Larry S. Davis
- Artificial Olfactory Brain for Mixture Identification Mehmet K. Muezzinoglu, Alexander Vergara, Ramon Huerta, Thomas Nowotny, Nikolai Rulkov, Henry Abarbanel, Allen Selverston, Mikhail Rabinovich
- Relative Performance Guarantees for Approximate Inference in Latent Dirichlet Allocation Indraneel Mukherjee, David M. Blei
- Evaluating probabilities under high-dimensional latent variable models Iain Murray, Ruslan R. Salakhutdinov
- Implicit Mixtures of Restricted Boltzmann Machines Vinod Nair, Geoffrey E. Hinton
- Characterizing response behavior in multisensory perception with conflicting cues Rama Natarajan, Iain Murray, Ladan Shams, Richard S. Zemel
- Phase transitions for high-dimensional joint support recovery Sahand Negahban, Martin J. Wainwright
- Hebbian Learning of Bayes Optimal Decisions Bernhard Nessler, Michael Pfeiffer, Wolfgang Maass
- Fitted Q-iteration by Advantage Weighted Regression Gerhard Neumann, Jan R. Peters
- Robust Kernel Principal Component Analysis Minh H. Nguyen, Fernando Torre
- Local Gaussian Process Regression for Real Time Online Model Learning Duy Nguyen-tuong, Jan R. Peters, Matthias Seeger
- On the Efficient Minimization of Classification Calibrated Surrogates Richard Nock, Frank Nielsen
- Multi-resolution Exploration in Continuous Spaces Ali Nouri, Michael L. Littman
- High-dimensional support union recovery in multivariate regression Guillaume R. Obozinski, Martin J. Wainwright, Michael I. Jordan
- A general framework for investigating how far the decoding process in the brain can be simplified Masafumi Oizumi, Toshiyuki Ishii, Kazuya Ishibashi, Toshihiko Hosoya, Masato Okada
- Modeling Short-term Noise Dependence of Spike Counts in Macaque Prefrontal Cortex Arno Onken, Steffen Grünewälder, Matthias Munk, Klaus Obermayer
- Improving on Expectation Propagation Manfred Opper, Ulrich Paquet, Ole Winther
- Finding Latent Causes in Causal Networks: an Efficient Approach Based on Markov Blankets Jean-philippe Pellet, André Elisseeff
- Estimation of Information Theoretic Measures for Continuous Random Variables Fernando Pérez-Cruz
- Biasing Approximate Dynamic Programming with a Lower Discount Factor Marek Petrik, Bruno Scherrer
- Cell Assemblies in Large Sparse Inhibitory Networks of Biologically Realistic Spiking Neurons Adam Ponzi, Jeff Wickens
- Global Ranking Using Continuous Conditional Random Fields Tao Qin, Tie-yan Liu, Xu-dong Zhang, De-sheng Wang, Hang Li
- Kernelized Sorting Novi Quadrianto, Le Song, Alex J. Smola
- A mixture model for the evolution of gene expression in non-homogeneous datasets Gerald Quon, Yee W. Teh, Esther Chan, Timothy Hughes, Michael Brudno, Quaid D. Morris
- Near-minimax recursive density estimation on the binary hypercube Maxim Raginsky, Svetlana Lazebnik, Rebecca Willett, Jorge Silva
- Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning Ali Rahimi, Benjamin Recht
- The Infinite Hierarchical Factor Regression Model Piyush Rai, Hal Daume
- Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of \boldmath\ell_1-regularized MLE Garvesh Raskutti, Bin Yu, Martin J. Wainwright, Pradeep K. Ravikumar
- Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images Vincent Q. Vu, Bin Yu, Thomas Naselaris, Kendrick Kay, Jack Gallant, Pradeep K. Ravikumar
- Bayesian Model of Behaviour in Economic Games Debajyoti Ray, Brooks King-casas, P. R. Montague, Peter Dayan
- Temporal Dynamics of Cognitive Control Jeremy Reynolds, Michael C. Mozer
- Signal-to-Noise Ratio Analysis of Policy Gradient Algorithms John W. Roberts, Russ Tedrake
- Non-stationary dynamic Bayesian networks Joshua W. Robinson, Alexander J. Hartemink
- The Mondrian Process Daniel M. Roy, Yee W. Teh
- Optimization on a Budget: A Reinforcement Learning Approach Paul L. Ruvolo, Ian Fasel, Javier R. Movellan
- Unsupervised Learning of Visual Sense Models for Polysemous Words Kate Saenko, Trevor Darrell
- Regularized Learning with Networks of Features Ted Sandler, John Blitzer, Partha P. Talukdar, Lyle H. Ungar
- Generative versus discriminative training of RBMs for classification of fMRI images Tanya Schmah, Geoffrey E. Hinton, Steven L. Small, Stephen Strother, Richard S. Zemel
- Efficient Exact Inference in Planar Ising Models Nicol N. Schraudolph, Dmitry Kamenetsky
- On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing Benjamin Schrauwen, Lars Buesing, Robert A. Legenstein
- An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis Gabriele Schweikert, Gunnar Rätsch, Christian Widmer, Bernhard Schölkopf
- Bayesian Experimental Design of Magnetic Resonance Imaging Sequences Hannes Nickisch, Rolf Pohmann, Bernhard Schölkopf, Matthias Seeger
- Risk Bounds for Randomized Sample Compressed Classifiers Mohak Shah
- Mind the Duality Gap: Logarithmic regret algorithms for online optimization Shai Shalev-shwartz, Sham M. Kakade
- On the Reliability of Clustering Stability in the Large Sample Regime Ohad Shamir, Naftali Tishby
- PSDBoost: Matrix-Generation Linear Programming for Positive Semidefinite Matrices Learning Chunhua Shen, Alan Welsh, Lei Wang
- Relative Margin Machines Tony Jebara, Pannagadatta K. Shivaswamy
- Kernel-ARMA for Hand Tracking and Brain-Machine interfacing During 3D Motor Control Lavi Shpigelman, Hagai Lalazar, Eilon Vaadia
- Skill Characterization Based on Betweenness Özgür Şimşek, Andrew G. Barto
- Regularized Co-Clustering with Dual Supervision Vikas Sindhwani, Jianying Hu, Aleksandra Mojsilovic
- Unlabeled data: Now it helps, now it doesn't Aarti Singh, Robert Nowak, Xiaojin Zhu
- The Conjoint Effect of Divisive Normalization and Orientation Selectivity on Redundancy Reduction Fabian H. Sinz, Matthias Bethge
- Convergence and Rate of Convergence of a Manifold-Based Dimension Reduction Algorithm Andrew Smith, Hongyuan Zha, Xiao-ming Wu
- Clusters and Coarse Partitions in LP Relaxations David Sontag, Amir Globerson, Tommi S. Jaakkola
- Fast Rates for Regularized Objectives Karthik Sridharan, Shai Shalev-shwartz, Nathan Srebro
- Grouping Contours Via a Related Image Praveen Srinivasan, Liming Wang, Jianbo Shi
- Non-parametric Regression Between Manifolds Florian Steinke, Matthias Hein
- Sparsity of SVMs that use the epsilon-insensitive loss Ingo Steinwart, Andreas Christmann
- An Online Algorithm for Maximizing Submodular Functions Matthew Streeter, Daniel Golovin
- Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes Erik B. Sudderth, Michael I. Jordan
- Using matrices to model symbolic relationship Ilya Sutskever, Geoffrey E. Hinton
- The Recurrent Temporal Restricted Boltzmann Machine Ilya Sutskever, Geoffrey E. Hinton, Graham W. Taylor
- A Convergent O(n) Temporal-difference Algorithm for Off-policy Learning with Linear Function Approximation Richard S. Sutton, Hamid R. Maei, Csaba Szepesvári
- Simple Local Models for Complex Dynamical Systems Erik Talvitie, Satinder P. Singh
- Breaking Audio CAPTCHAs Jennifer Tam, Jiri Simsa, Sean Hyde, Luis V. Ahn
- Correlated Bigram LSA for Unsupervised Language Model Adaptation Yik-cheung Tam, Tanja Schultz
- Playing Pinball with non-invasive BCI Matthias Krauledat, Konrad Grzeska, Max Sagebaum, Benjamin Blankertz, Carmen Vidaurre, Klaus-Robert Müller, Michael Schröder
- Bounding Performance Loss in Approximate MDP Homomorphisms Jonathan Taylor, Doina Precup, Prakash Panagaden
- Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data Tran T. Truyen, Dinh Phung, Hung Bui, Svetha Venkatesh
- Integrating Locally Learned Causal Structures with Overlapping Variables David Danks, Clark Glymour, Robert E. Tillman
- Bayesian Kernel Shaping for Learning Control Jo-anne Ting, Mrinal Kalakrishnan, Sethu Vijayakumar, Stefan Schaal
- Efficient Sampling for Gaussian Process Inference using Control Variables Neil D. Lawrence, Magnus Rattray, Michalis K. Titsias
- Learning to Use Working Memory in Partially Observable Environments through Dopaminergic Reinforcement Michael T. Todd, Yael Niv, Jonathan D. Cohen
- The Infinite Factorial Hidden Markov Model Jurgen V. Gael, Yee W. Teh, Zoubin Ghahramani
- Multi-Level Active Prediction of Useful Image Annotations for Recognition Sudheendra Vijayanarasimhan, Kristen Grauman
- Diffeomorphic Dimensionality Reduction Christian Walder, Bernhard Schölkopf
- Learning a discriminative hidden part model for human action recognition Yang Wang, Greg Mori
- Algorithms for Infinitely Many-Armed Bandits Yizao Wang, Jean-yves Audibert, Rémi Munos
- Large Margin Taxonomy Embedding for Document Categorization Kilian Q. Weinberger, Olivier Chapelle
- Beyond Novelty Detection: Incongruent Events, when General and Specific Classifiers Disagree Daphna Weinshall, Hynek Hermansky, Alon Zweig, Jie Luo, Holly Jimison, Frank Ohl, Misha Pavel
- Spectral Hashing Yair Weiss, Antonio Torralba, Rob Fergus
- MAS: a multiplicative approximation scheme for probabilistic inference Ydo Wexler, Christopher Meek
- Dependence of Orientation Tuning on Recurrent Excitation and Inhibition in a Network Model of V1 Klaus Wimmer, Marcel Stimberg, Robert Martin, Lars Schwabe, Jorge Mariño, James Schummers, David C. Lyon, Mriganka Sur, Klaus Obermayer
- Estimating the Location and Orientation of Complex, Correlated Neural Activity using MEG Julia Owen, Hagai T. Attias, Kensuke Sekihara, Srikantan S. Nagarajan, David P. Wipf
- Localized Sliced Inverse Regression Qiang Wu, Sayan Mukherjee, Feng Liang
- Model selection and velocity estimation using novel priors for motion patterns Shuang Wu, Hongjing Lu, Alan L. Yuille
- Robust Regression and Lasso Huan Xu, Constantine Caramanis, Shie Mannor
- How memory biases affect information transmission: A rational analysis of serial reproduction Jing Xu, Thomas L. Griffiths
- Short-Term Depression in VLSI Stochastic Synapse Peng Xu, Timothy K. Horiuchi, Pamela A. Abshire
- An Extended Level Method for Efficient Multiple Kernel Learning Zenglin Xu, Rong Jin, Irwin King, Michael Lyu
- Bayesian Network Score Approximation using a Metagraph Kernel Benjamin Yackley, Eduardo Corona, Terran Lane
- Supervised Bipartite Graph Inference Yoshihiro Yamanishi
- Learning with Consistency between Inductive Functions and Kernels Haixuan Yang, Irwin King, Michael Lyu
- Semi-supervised Learning with Weakly-Related Unlabeled Data : Towards Better Text Categorization Liu Yang, Rong Jin, Rahul Sukthankar
- Spike Feature Extraction Using Informative Samples Zhi Yang, Qi Zhao, Wentai Liu
- Sequential effects: Superstition or rational behavior? Angela J. Yu, Jonathan D. Cohen
- Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity Byron M. Yu, John P. Cunningham, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani
- Deep Learning with Kernel Regularization for Visual Recognition Kai Yu, Wei Xu, Yihong Gong
- Variational Mixture of Gaussian Process Experts Chao Yuan, Claus Neubauer
- Multi-Agent Filtering with Infinitely Nested Beliefs Luke Zettlemoyer, Brian Milch, Leslie P. Kaelbling
- Fast Computation of Posterior Mode in Multi-Level Hierarchical Models Liang Zhang, Deepak Agarwal
- Adaptive Forward-Backward Greedy Algorithm for Sparse Learning with Linear Models Tong Zhang
- Multi-stage Convex Relaxation for Learning with Sparse Regularization Tong Zhang
- Kernel Measures of Independence for non-iid Data Xinhua Zhang, Le Song, Arthur Gretton, Alex J. Smola
- Learning the Semantic Correlation: An Alternative Way to Gain from Unlabeled Text Yi Zhang, Artur Dubrawski, Jeff G. Schneider
- Cyclizing Clusters via Zeta Function of a Graph Deli Zhao, Xiaoou Tang
- Hierarchical Fisher Kernels for Longitudinal Data Zhengdong Lu, Jeffrey Kaye, Todd K. Leen
- Posterior Consistency of the Silverman g-prior in Bayesian Model Choice Zhihua Zhang, Michael I. Jordan, Dit-Yan Yeung
- Partially Observed Maximum Entropy Discrimination Markov Networks Jun Zhu, Eric P. Xing, Bo Zhang
- Recursive Segmentation and Recognition Templates for 2D Parsing Leo Zhu, Yuanhao Chen, Yuan Lin, Chenxi Lin, Alan L. Yuille
- Stochastic Relational Models for Large-scale Dyadic Data using MCMC Shenghuo Zhu, Kai Yu, Yihong Gong