NIPS 2007: Vancouver, British Columbia, Canada
John C. Platt, Daphne Koller, Yoram Singer, Sam T. Roweis (Eds.): Advances in Neural Information Processing Systems 20, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 3-6, 2007. Curran Associates, Inc. 2008- Cédric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford, John Shawe-Taylor: Variational Inference for Diffusion Processes.
- Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil, Yiming Ying: A Spectral Regularization Framework for Multi-Task Structure Learning.
- Jean-Yves Audibert: Progressive mixture rules are deviation suboptimal.
- Marco Barreno, Alvaro A. Cárdenas, J. Doug Tygar: Optimal ROC Curve for a Combination of Classifiers.
- Ulrik R. Beierholm, Konrad P. Körding, Ladan Shams, Wei Ji Ma: Comparing Bayesian models for multisensory cue combination without mandatory integration.
- Matthias Bethge, Philipp Berens: Near-Maximum Entropy Models for Binary Neural Representations of Natural Images.
- Shalabh Bhatnagar, Richard S. Sutton, Mohammad Ghavamzadeh, Mark Lee: Incremental Natural Actor-Critic Algorithms.
- Benjamin Blankertz, Motoaki Kawanabe, Ryota Tomioka, Friederike Hohlefeld, Vadim V. Nikulin, Klaus-Robert Müller: Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing.
- John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman: Learning Bounds for Domain Adaptation.
- Ben Blum, Michael I. Jordan, David Kim, Rhiju Das, Philip Bradley, David Baker: Feature Selection Methods for Improving Protein Structure Prediction with Rosetta.
- Edwin V. Bonilla, Kian Ming Adam Chai, Christopher K. I. Williams: Multi-task Gaussian Process Prediction.
- Alexandre Bouchard-Côté, Percy Liang, Thomas L. Griffiths, Dan Klein: A Probabilistic Approach to Language Change.
- Sabri Boutemedjet, Djemel Ziou, Nizar Bouguila: Unsupervised Feature Selection for Accurate Recommendation of High-Dimensional Image Data.
- Joseph K. Bradley, Robert E. Schapire: FilterBoost: Regression and Classification on Large Datasets.
- Lars Buesing, Wolfgang Maass: Simplified Rules and Theoretical Analysis for Information Bottleneck Optimization and PCA with Spiking Neurons.
- Gertjan J. Burghouts, Arnold W. M. Smeulders, Jan-Mark Geusebroek: The Distribution Family of Similarity Distances.
- William M. Campbell, Fred S. Richardson: Discriminative Keyword Selection Using Support Vector Machines.
- Ben Carterette, Rosie Jones: Evaluating Search Engines by Modeling the Relationship Between Relevance and Clicks.
- Gonzalo Carvajal, Waldo Valenzuela, Miguel Figueroa: Subspace-Based Face Recognition in Analog VLSI.
- Moran Cerf, Jonathan Harel, Wolfgang Einhäuser, Christof Koch: Predicting human gaze using low-level saliency combined with face detection.
- Venkat Chandrasekaran, Jason K. Johnson, Alan S. Willsky: Adaptive Embedded Subgraph Algorithms using Walk-Sum Analysis.
- Edward Y. Chang, Kaihua Zhu, Hao Wang, Hongjie Bai, Jian Li, Zhihuan Qiu, Hang Cui: Parallelizing Support Vector Machines on Distributed Computers.
- Nicolas Chapados, Yoshua Bengio: Augmented Functional Time Series Representation and Forecasting with Gaussian Processes.
- Yuanhao Chen, Long Zhu, Chenxi Lin, Alan L. Yuille, HongJiang Zhang: Rapid Inference on a Novel AND/OR graph for Object Detection, Segmentation and Parsing.
- Christoforos Christoforou, Paul Sajda, Lucas C. Parra: Second Order Bilinear Discriminant Analysis for single trial EEG analysis.
- Claudia Clopath, André Longtin, Wulfram Gerstner: An online Hebbian learning rule that performs Independent Component Analysis.
- John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani: Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes.
- Pierre Dangauthier, Ralf Herbrich, Tom Minka, Thore Graepel: TrueSkill Through Time: Revisiting the History of Chess.
- Justin Dauwels, François B. Vialatte, Tomasz M. Rutkowski, Andrzej Cichocki: Measuring Neural Synchrony by Message Passing.
- Chuong B. Do, Chuan-Sheng Foo, Andrew Y. Ng: Efficient multiple hyperparameter learning for log-linear models.
- Douglas Eck, Paul Lamere, Thierry Bertin-Mahieux, Stephen Green: Automatic Generation of Social Tags for Music Recommendation.
- Dominik Endres, Mike W. Oram, Johannes E. Schindelin, Peter Földiák: Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms.
- Gwenn Englebienne, Timothy F. Cootes, Magnus Rattray: A probabilistic model for generating realistic lip movements from speech.
- Eric Brochu, Nando de Freitas, Abhijeet Ghosh: Active Preference Learning with Discrete Choice Data.
- Tim van Erven, Peter Grunwald, Steven de Rooij: Catching Up Faster in Bayesian Model Selection and Model Averaging.
- Pierre W. Ferrez, José del R. Millán: EEG-Based Brain-Computer Interaction: Improved Accuracy by Automatic Single-Trial Error Detection.
- Brian Fischer: Optimal models of sound localization by barn owls.
- Michael Frank, Noah D. Goodman, Joshua B. Tenenbaum: A Bayesian Framework for Cross-Situational Word-Learning.
- Yoav Freund, Sanjoy Dasgupta, Mayank Kabra, Nakul Verma: Learning the structure of manifolds using random projections.
- Charlie Frogner, Avi Pfeffer: Discovering Weakly-Interacting Factors in a Complex Stochastic Process.
- Kenji Fukumizu, Arthur Gretton, Xiaohai Sun, Bernhard Schölkopf: Kernel Measures of Conditional Dependence.
- Dashan Gao, Vijay Mahadevan, Nuno Vasconcelos: The discriminant center-surround hypothesis for bottom-up saliency.
- Pierre Garrigues, Bruno A. Olshausen: Learning Horizontal Connections in a Sparse Coding Model of Natural Images.
- Michael Gashler, Dan Ventura, Tony R. Martinez: Iterative Non-linear Dimensionality Reduction with Manifold Sculpting.
- Sebastian Gerwinn, Jakob H. Macke, Matthias Seeger, Matthias Bethge: Bayesian Inference for Spiking Neuron Models with a Sparsity Prior.
- Sennay Ghebreab, Arnold W. M. Smeulders, Pieter W. Adriaans: Predicting Brain States from fMRI Data: Incremental Functional Principal Component Regression.
- Massimiliano Giulioni, Mario Pannunzi, Davide Badoni, Vittorio Dante, Paolo Del Giudice: A configurable analog VLSI neural network with spiking neurons and self-regulating plastic synapses .
- Amir Globerson, Tommi Jaakkola: Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations.
- Alex Graves, Santiago Fernández, Marcus Liwicki, Horst Bunke, Jürgen Schmidhuber: Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks.
- Arthur Gretton, Kenji Fukumizu, Choon Hui Teo, Le Song, Bernhard Schölkopf, Alex J. Smola: A Kernel Statistical Test of Independence.
- Zaïd Harchaoui, Francis Bach, Eric Moulines: Testing for Homogeneity with Kernel Fisher Discriminant Analysis.
- Elad Hazan, Satyen Kale: Computational Equivalence of Fixed Points and No Regret Algorithms, and Convergence to Equilibria.
- José Miguel Hernández-Lobato, Tjeerd Dijkstra, Tom Heskes: Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach.
- Peter Hoff: Modeling homophily and stochastic equivalence in symmetric relational data.
- Matthew D. Hoffman, Arnaud Doucet, Nando de Freitas, Ajay Jasra: Bayesian Policy Learning with Trans-Dimensional MCMC.
- Michael P. Holmes, Alexander G. Gray, Charles Lee Isbell Jr.: Ultrafast Monte Carlo for Statistical Summations.
- Jonathan Huang, Carlos Guestrin, Leonidas J. Guibas: Efficient Inference for Distributions on Permutations.
- Tony Jebara, Yingbo Song, Kapil Thadani: Density Estimation under Independent Similarly Distributed Sampling Assumptions.
- Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama, Kiyoshi Asai: Multi-Task Learning via Conic Programming.
- Sergey Kirshner: Learning with Tree-Averaged Densities and Distributions.
- J. Zico Kolter, Pieter Abbeel, Andrew Y. Ng: Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion.
- Andreas Krause, H. Brendan McMahan, Carlos Guestrin, Anupam Gupta: Selecting Observations against Adversarial Objectives.
- Krishnan Kumar, Chiru Bhattacharyya, Ramesh Hariharan: A Randomized Algorithm for Large Scale Support Vector Learning.
- Yiu Man Lam, Bertram E. Shi: Extending position/phase-shift tuning to motion energy neurons improves velocity discrimination.
- John Langford, Tong Zhang: The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information.
- Alessandro Lazaric, Marcello Restelli, Andrea Bonarini: Reinforcement Learning in Continuous Action Spaces through Sequential Monte Carlo Methods.
- Andrea Lecchini-Visintini, John Lygeros, Jan M. Maciejowski: Simulated Annealing: Rigorous finite-time guarantees for optimization on continuous domains.
- Robert A. Legenstein, Dejan Pecevski, Wolfgang Maass: Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity.
- Ping Li, Trevor Hastie: A Unified Near-Optimal Estimator For Dimension Reduction in lalpha(0 < alpha <= 2) Using Stable Random Projections.
- Ping Li, Christopher J. C. Burges, Qiang Wu: McRank: Learning to Rank Using Multiple Classification and Gradient Boosting.
- Yuanqing Lin, Jingdong Chen, Youngmoo Kim, Daniel D. Lee: Blind channel identification for speech dereverberation using l1-norm sparse learning.
- Erik Linstead, Paul Rigor, Sushil Krishna Bajracharya, Cristina Videira Lopes, Pierre Baldi: Mining Internet-Scale Software Repositories.
- Zhengdong Lu, Miguel Á. Carreira-Perpiñán, Cristian Sminchisescu: People Tracking with the Laplacian Eigenmaps Latent Variable Model.
- Ulrike von Luxburg, Sébastien Bubeck, Stefanie Jegelka, Michael Kaufmann: Consistent Minimization of Clustering Objective Functions.
- Maryam Mahdaviani, Tanzeem Choudhury: Fast and Scalable Training of Semi-Supervised CRFs with Application to Activity Recognition.
- M. M. Mahmud, Sylvian R. Ray: Transfer Learning using Kolmogorov Complexity: Basic Theory and Empirical Evaluations.
- Omer Bobrowski, Ron Meir, Shy Shoham, Yonina C. Eldar: A neural network implementing optimal state estimation based on dynamic spike train decoding.
- Francois Meyer, Greg Stephens: Locality and low-dimensions in the prediction of natural experience from fMRI.
- Srinjoy Mitra, Giacomo Indiveri, Stefano Fusi: Learning to classify complex patterns using a VLSI network of spiking neurons.
- Pawan Mudigonda, Vladimir Kolmogorov, Philip H. S. Torr: An Analysis of Convex Relaxations for MAP Estimation.
- Emre Neftci, Elisabetta Chicca, Giacomo Indiveri, Jean-Jacques E. Slotine, Rodney J. Douglas: Contraction Properties of VLSI Cooperative Competitive Neural Networks of Spiking Neurons.
- David Newman, Arthur U. Asuncion, Padhraic Smyth, Max Welling: Distributed Inference for Latent Dirichlet Allocation.
- XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan: Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization.
- Luis E. Ortiz: CPR for CSPs: A Probabilistic Relaxation of Constraint Propagation.
- Simon Osindero, Geoffrey E. Hinton: Modeling image patches with a directed hierarchy of Markov random fields.
- Mehul Parsana, Sourangshu Bhattacharya, Chiru Bhattacharyya, K. R. Ramakrishnan: Kernels on Attributed Pointsets with Applications.
- Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor: A Risk Minimization Principle for a Class of Parzen Estimators.
- Robert J. Peters, Laurent Itti: Congruence between model and human attention reveals unique signatures of critical visual events.
- Jonathan W. Pillow, Peter E. Latham: Neural characterization in partially observed populations of spiking neurons.
- John C. Platt, Emre Kiciman, David A. Maltz: Fast Variational Inference for Large-scale Internet Diagnosis.
- Vikas C. Raykar, Harald Steck, Balaji Krishnapuram, Cary Dehing-Oberije, Philippe Lambin: On Ranking in Survival Analysis: Bounds on the Concordance Index.
- Michael Ross, Andrew Cohen: GRIFT: A graphical model for inferring visual classification features from human data.
- Stéphane Ross, Joelle Pineau, Brahim Chaib-draa: Theoretical Analysis of Heuristic Search Methods for Online POMDPs.
- Nicolas Le Roux, Yoshua Bengio, Pascal Lamblin, Marc Joliveau, Balázs Kégl: Learning the 2-D Topology of Images.
- Nicolas Le Roux, Pierre-Antoine Manzagol, Yoshua Bengio: Topmoumoute Online Natural Gradient Algorithm.
- Bryan C. Russell, Antonio Torralba, Ce Liu, Robert Fergus, William T. Freeman: Object Recognition by Scene Alignment.
- Ruslan Salakhutdinov, Geoffrey E. Hinton: Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes.
- Sujay Sanghavi, Dmitry M. Malioutov, Alan S. Willsky: Linear programming analysis of loopy belief propagation for weighted matching.
- Tatyana Sharpee: Better than least squares: comparison of objective functions for estimating linear-nonlinear models.
- Madhusudana V. S. Shashanka, Bhiksha Raj, Paris Smaragdis: Sparse Overcomplete Latent Variable Decomposition of Counts Data.
- Daniel Sheldon, M. A. Saleh Elmohamed, Dexter Kozen: Collective Inference on Markov Models for Modeling Bird Migration.
- Sajid M. Siddiqi, Byron Boots, Geoffrey J. Gordon: A Constraint Generation Approach to Learning Stable Linear Dynamical Systems.
- Leonid Sigal, Alexandru O. Balan, Michael J. Black: Combined discriminative and generative articulated pose and non-rigid shape estimation.
- Vikas Singh, Lopamudra Mukherjee, Jiming Peng, Jinhui Xu: Ensemble Clustering using Semidefinite Programming.
- Kaushik Sinha, Mikhail Belkin: The Value of Labeled and Unlabeled Examples when the Model is Imperfect.
- Fabian H. Sinz, Olivier Chapelle, Alekh Agarwal, Bernhard Schölkopf: An Analysis of Inference with the Universum.
- Devarajan Sridharan, Brian Percival, John V. Arthur, Kwabena Boahen: An in-silico Neural Model of Dynamic Routing through Neuronal Coherence.
- Alexander L. Strehl, Michael L. Littman: Online Linear Regression and Its Application to Model-Based Reinforcement Learning.
- Erik B. Sudderth, Martin J. Wainwright, Alan S. Willsky: Loop Series and Bethe Variational Bounds in Attractive Graphical Models.
- Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul von Bünau, Motoaki Kawanabe: Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation.
- Özgür Sümer, Umut A. Acar, Alexander T. Ihler, Ramgopal R. Mettu: Efficient Bayesian Inference for Dynamically Changing Graphs.
- Gerald Tesauro, Rajarshi Das, Hoi Chan, Jeffrey O. Kephart, David Levine, Freeman L. Rawson III, Charles Lefurgy: Managing Power Consumption and Performance of Computing Systems Using Reinforcement Learning.
- Ambuj Tewari, Peter L. Bartlett: Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs.
- Michalis K. Titsias: The Infinite Gamma-Poisson Feature Model.
- Kristina Toutanova, Mark Johnson: A Bayesian LDA-based model for semi-supervised part-of-speech tagging.
- Manfred K. Warmuth, Karen A. Glocer, Gunnar Rätsch: Boosting Algorithms for Maximizing the Soft Margin.
- Markus Weimer, Alexandros Karatzoglou, Quoc V. Le, Alex J. Smola: COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking .
- Ben H. Williams, Marc Toussaint, Amos J. Storkey: Modelling motion primitives and their timing in biologically executed movements.
- John Wright, Yangyu Tao, Zhouchen Lin, Yi Ma, Heung-Yeung Shum: Classification via Minimum Incremental Coding Length (MICL).
- Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, Michael R. Lyu: Efficient Convex Relaxation for Transductive Support Vector Machine.
- Alan L. Yuille, Hongjing Lu: The Noisy-Logical Distribution and its Application to Causal Inference.
- Zhaohui Zheng, Hongyuan Zha, Tong Zhang, Olivier Chapelle, Keke Chen, Gordon Sun: A General Boosting Method and its Application to Learning Ranking Functions for Web Search.
- Martin Zinkevich, Michael Johanson, Michael H. Bowling, Carmelo Piccione: Regret Minimization in Games with Incomplete Information.
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last updated on 2013-06-18T211851 by the dblp team