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ORCIDNIPS 2007: Vancouver, British Columbia, Canada
- John C. Platt, Daphne Koller, Yoram Singer, Sam T. Roweis:
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 - Misha B. Ahrens, Maneesh Sahani:
Inferring Elapsed Time from Stochastic Neural Processes. 1-8 - András Antos, Rémi Munos, Csaba Szepesvári:
Fitted Q-iteration in continuous action-space MDPs. 9-16 - Cédric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford, John Shawe-Taylor:
Variational Inference for Diffusion Processes. 17-24 - Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil, Yiming Ying:
A Spectral Regularization Framework for Multi-Task Structure Learning. 25-32 - Christopher G. Atkeson, Benjamin J. Stephens:
Random Sampling of States in Dynamic Programming. 33-40 - Jean-Yves Audibert:
Progressive mixture rules are deviation suboptimal. 41-48 - Francis R. Bach, Zaïd Harchaoui:
DIFFRAC: a discriminative and flexible framework for clustering. 49-56 - Marco Barreno, Alvaro A. Cárdenas, J. Doug Tygar:
Optimal ROC Curve for a Combination of Classifiers. 57-64 - Peter L. Bartlett, Elad Hazan, Alexander Rakhlin:
Adaptive Online Gradient Descent. 65-72 - Zafer Barutçuoglu, Philip M. Long, Rocco A. Servedio:
One-Pass Boosting. 73-80 - Ulrik R. Beierholm, Konrad P. Körding, Ladan Shams, Wei Ji Ma:
Comparing Bayesian models for multisensory cue combination without mandatory integration. 81-88 - Pietro Berkes
, Richard E. Turner, Maneesh Sahani:
On Sparsity and Overcompleteness in Image Models. 89-96 - Matthias Bethge, Philipp Berens:
Near-Maximum Entropy Models for Binary Neural Representations of Natural Images. 97-104 - Shalabh Bhatnagar, Richard S. Sutton, Mohammad Ghavamzadeh, Mark Lee:
Incremental Natural Actor-Critic Algorithms. 105-112 - Benjamin Blankertz, Motoaki Kawanabe, Ryota Tomioka, Friederike U. Hohlefeld, Vadim V. Nikulin, Klaus-Robert Müller:
Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing. 113-120 - David M. Blei, Jon D. McAuliffe:
Supervised Topic Models. 121-128 - John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman:
Learning Bounds for Domain Adaptation. 129-136 - Ben Blum, Michael I. Jordan, David E. Kim, Rhiju Das, Philip Bradley, David Baker:
Feature Selection Methods for Improving Protein Structure Prediction with Rosetta. 137-144 - Edwin V. Bonilla, Kian Ming Adam Chai, Christopher K. I. Williams:
Multi-task Gaussian Process Prediction. 153-160 - Léon Bottou, Olivier Bousquet:
The Tradeoffs of Large Scale Learning. 161-168 - Alexandre Bouchard-Côté, Percy Liang, Thomas L. Griffiths, Dan Klein:
A Probabilistic Approach to Language Change. 169-176 - Sabri Boutemedjet, Djemel Ziou, Nizar Bouguila:
Unsupervised Feature Selection for Accurate Recommendation of High-Dimensional Image Data. 177-184 - Joseph K. Bradley, Robert E. Schapire:
FilterBoost: Regression and Classification on Large Datasets. 185-192 - Lars Buesing, Wolfgang Maass:
Simplified Rules and Theoretical Analysis for Information Bottleneck Optimization and PCA with Spiking Neurons. 193-200 - Gertjan J. Burghouts, Arnold W. M. Smeulders, Jan-Mark Geusebroek:
The Distribution Family of Similarity Distances. 201-208 - William M. Campbell, Fred S. Richardson:
Discriminative Keyword Selection Using Support Vector Machines. 209-216 - Ben Carterette, Rosie Jones:
Evaluating Search Engines by Modeling the Relationship Between Relevance and Clicks. 217-224 - Gonzalo Carvajal, Waldo Valenzuela, Miguel E. Figueroa:
Subspace-Based Face Recognition in Analog VLSI. 225-232 - Lawrence Cayton, Sanjoy Dasgupta:
A learning framework for nearest neighbor search. 233-240 - Moran Cerf, Jonathan Harel, Wolfgang Einhäuser, Christof Koch:
Predicting human gaze using low-level saliency combined with face detection. 241-248 - Venkat Chandrasekaran, Jason K. Johnson, Alan S. Willsky:
Adaptive Embedded Subgraph Algorithms using Walk-Sum Analysis. 249-256 - Edward Y. Chang, Kaihua Zhu, Hao Wang, Hongjie Bai, Jian Li, Zhihuan Qiu, Hang Cui:
Parallelizing Support Vector Machines on Distributed Computers. 257-264 - Nicolas Chapados, Yoshua Bengio:
Augmented Functional Time Series Representation and Forecasting with Gaussian Processes. 265-272 - Anton Chechetka, Carlos Guestrin:
Efficient Principled Learning of Thin Junction Trees. 273-280 - Ke Chen, Shihai Wang:
Regularized Boost for Semi-Supervised Learning. 281-288 - 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. 289-296 - Hai Leong Chieu, Wee Sun Lee, Yee Whye Teh:
Cooled and Relaxed Survey Propagation for MRFs. 297-304 - Andreas Christmann, Ingo Steinwart:
How SVMs can estimate quantiles and the median. 305-312 - Christoforos Christoforou, Paul Sajda, Lucas C. Parra:
Second Order Bilinear Discriminant Analysis for single trial EEG analysis. 313-320 - Claudia Clopath, André Longtin, Wulfram Gerstner:
An online Hebbian learning rule that performs Independent Component Analysis. 321-328 - John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani:
Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes. 329-336 - Pierre Dangauthier, Ralf Herbrich, Tom Minka, Thore Graepel:
TrueSkill Through Time: Revisiting the History of Chess. 337-344 - Varsha Dani, Thomas P. Hayes, Sham M. Kakade:
The Price of Bandit Information for Online Optimization. 345-352 - Sanjoy Dasgupta, Daniel J. Hsu, Claire Monteleoni:
A general agnostic active learning algorithm. 353-360 - Justin Dauwels, François B. Vialatte, Tomasz M. Rutkowski, Andrzej Cichocki:
Measuring Neural Synchrony by Message Passing. 361-368 - Nathaniel D. Daw, Aaron C. Courville:
The rat as particle filter. 369-376 - Chuong B. Do, Chuan-Sheng Foo, Andrew Y. Ng:
Efficient multiple hyperparameter learning for log-linear models. 377-384 - Douglas Eck, Paul Lamere, Thierry Bertin-Mahieux, Stephen J. Green:
Automatic Generation of Social Tags for Music Recommendation. 385-392 - Dominik Endres, Mike W. Oram, Johannes E. Schindelin, Peter Földiák:
Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms. 393-400 - Gwenn Englebienne, Timothy F. Cootes, Magnus Rattray:
A probabilistic model for generating realistic lip movements from speech. 401-408 - Eric Brochu, Nando de Freitas, Abhijeet Ghosh:
Active Preference Learning with Discrete Choice Data. 409-416 - Tim van Erven, Peter Grunwald, Steven de Rooij:
Catching Up Faster in Bayesian Model Selection and Model Averaging. 417-424 - Saher Esmeir, Shaul Markovitch:
Anytime Induction of Cost-sensitive Trees. 425-432 - Vittorio Ferrari, Andrew Zisserman:
Learning Visual Attributes. 433-440 - Pierre W. Ferrez, José del R. Millán:
EEG-Based Brain-Computer Interaction: Improved Accuracy by Automatic Single-Trial Error Detection. 441-448 - Brian Fischer:
Optimal models of sound localization by barn owls. 449-456 - Michael C. Frank, Noah D. Goodman, Joshua B. Tenenbaum:
A Bayesian Framework for Cross-Situational Word-Learning. 457-464 - Peter I. Frazier, Angela J. Yu:
Sequential Hypothesis Testing under Stochastic Deadlines. 465-472 - Yoav Freund, Sanjoy Dasgupta, Mayank Kabra, Nakul Verma:
Learning the structure of manifolds using random projections. 473-480 - Charlie Frogner, Avi Pfeffer:
Discovering Weakly-Interacting Factors in a Complex Stochastic Process. 481-488 - Kenji Fukumizu, Arthur Gretton, Xiaohai Sun, Bernhard Schölkopf:
Kernel Measures of Conditional Dependence. 489-496 - Dashan Gao, Vijay Mahadevan, Nuno Vasconcelos:
The discriminant center-surround hypothesis for bottom-up saliency. 497-504 - Pierre Garrigues, Bruno A. Olshausen:
Learning Horizontal Connections in a Sparse Coding Model of Natural Images. 505-512 - Michael Gashler, Dan Ventura, Tony R. Martinez:
Iterative Non-linear Dimensionality Reduction with Manifold Sculpting. 513-520 - Claudio Gentile, Fabio Vitale, Cristian Brotto:
On higher-order perceptron algorithms. 521-528 - Sebastian Gerwinn, Jakob H. Macke, Matthias W. Seeger, Matthias Bethge:
Bayesian Inference for Spiking Neuron Models with a Sparsity Prior. 529-536 - Sennay Ghebreab, Arnold W. M. Smeulders, Pieter W. Adriaans:
Predicting Brain States from fMRI Data: Incremental Functional Principal Component Regression. 537-544 - 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 . 545-552 - Amir Globerson, Tommi S. Jaakkola:
Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations. 553-560 - Judy Goldsmith, Martin Mundhenk:
Competition Adds Complexity. 561-568 - João Graça, Kuzman Ganchev, Ben Taskar:
Expectation Maximization and Posterior Constraints. 569-576 - Alex Graves, Santiago Fernández, Marcus Liwicki, Horst Bunke, Jürgen Schmidhuber:
Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks. 577-584 - Arthur Gretton, Kenji Fukumizu, Choon Hui Teo, Le Song, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Statistical Test of Independence. 585-592 - Yuhong Guo, Dale Schuurmans:
Discriminative Batch Mode Active Learning. 593-600 - Yuhong Guo, Dale Schuurmans:
Convex Relaxations of Latent Variable Training. 601-608 - Zaïd Harchaoui, Francis R. Bach, Eric Moulines:
Testing for Homogeneity with Kernel Fisher Discriminant Analysis. 609-616 - Zaïd Harchaoui, Céline Lévy-Leduc:
Catching Change-points with Lasso. 617-624 - Elad Hazan, Satyen Kale:
Computational Equivalence of Fixed Points and No Regret Algorithms, and Convergence to Equilibria. 625-632 - Jingrui He, Jaime G. Carbonell:
Nearest-Neighbor-Based Active Learning for Rare Category Detection. 633-640 - Chinmay Hegde, Michael B. Wakin, Richard G. Baraniuk:
Random Projections for Manifold Learning. 641-648 - José Miguel Hernández-Lobato, Tjeerd Dijkstra, Tom Heskes:
Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach. 649-656 - Peter D. Hoff:
Modeling homophily and stochastic equivalence in symmetric relational data. 657-664 - Matt Hoffman, Arnaud Doucet, Nando de Freitas, Ajay Jasra:
Bayesian Policy Learning with Trans-Dimensional MCMC. 665-672 - Michael P. Holmes, Alexander G. Gray, Charles Lee Isbell Jr.:
Ultrafast Monte Carlo for Statistical Summations. 673-680 - Andrew G. Howard, Tony Jebara:
Learning Monotonic Transformations for Classification. 681-688 - David Hsu, Wee Sun Lee, Nan Rong:
What makes some POMDP problems easy to approximate? 689-696 - Jonathan Huang, Carlos Guestrin, Leonidas J. Guibas:
Efficient Inference for Distributions on Permutations. 697-704 - Marcus Hutter, Shane Legg:
Temporal Difference Updating without a Learning Rate. 705-712 - Tony Jebara, Yingbo Song, Kapil Thadani:
Density Estimation under Independent Similarly Distributed Sampling Assumptions. 713-720 - Michael Johanson, Martin Zinkevich, Michael H. Bowling:
Computing Robust Counter-Strategies. 721-728 - Kyomin Jung, Devavrat Shah:
Local Algorithms for Approximate Inference in Minor-Excluded Graphs. 729-736 - Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama, Kiyoshi Asai:
Multi-Task Learning via Conic Programming. 737-744 - Michael J. Kearns, Jinsong Tan, Jennifer Wortman:
Privacy-Preserving Belief Propagation and Sampling. 745-752 - Charles Kemp, Noah D. Goodman, Joshua B. Tenenbaum:
Learning and using relational theories. 753-760 - Sergey Kirshner:
Learning with Tree-Averaged Densities and Distributions. 761-768 - J. Zico Kolter, Pieter Abbeel, Andrew Y. Ng:
Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion. 769-776 - Andreas Krause, H. Brendan McMahan, Carlos Guestrin, Anupam Gupta:
Selecting Observations against Adversarial Objectives. 777-784 - Alex Kulesza, Fernando Pereira:
Structured Learning with Approximate Inference. 785-792 - Krishnan Kumar, Chiru Bhattacharyya, Ramesh Hariharan:
A Randomized Algorithm for Large Scale Support Vector Learning. 793-800 - John D. Lafferty, Larry A. Wasserman:
Statistical Analysis of Semi-Supervised Regression. 801-808 - Yiu Man Lam, Bertram E. Shi:
Extending position/phase-shift tuning to motion energy neurons improves velocity discrimination. 809-816 - John Langford, Tong Zhang:
The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information. 817-824 - Danial Lashkari, Polina Golland:
Convex Clustering with Exemplar-Based Models. 825-832 - Alessandro Lazaric, Marcello Restelli, Andrea Bonarini:
Reinforcement Learning in Continuous Action Spaces through Sequential Monte Carlo Methods. 833-840 - Guy Lebanon, Yi Mao:
Non-parametric Modeling of Partially Ranked Data. 857-864 - Andrea Lecchini-Visintini, John Lygeros, Jan M. Maciejowski:
Simulated Annealing: Rigorous finite-time guarantees for optimization on continuous domains. 865-872 - Honglak Lee, Chaitanya Ekanadham, Andrew Y. Ng:
Sparse deep belief net model for visual area V2. 873-880 - Robert Legenstein, Dejan Pecevski, Wolfgang Maass:
Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity. 881-888 - Máté Lengyel, Peter Dayan:
Hippocampal Contributions to Control: The Third Way. 889-896 - Ping Li, Trevor Hastie:
A Unified Near-Optimal Estimator For Dimension Reduction in lalpha(0 < alpha <= 2) Using Stable Random Projections. 905-912 - Ping Li, Christopher J. C. Burges, Qiang Wu:
McRank: Learning to Rank Using Multiple Classification and Gradient Boosting. 897-904 - Percy Liang, Dan Klein, Michael I. Jordan:
Agreement-Based Learning. 913-920 - Yuanqing Lin, Jingdong Chen, Youngmoo E. Kim, Daniel D. Lee:
Blind channel identification for speech dereverberation using l1-norm sparse learning. 921-928 - Erik Linstead, Paul Rigor, Sushil Krishna Bajracharya, Cristina Videira Lopes, Pierre Baldi:
Mining Internet-Scale Software Repositories. 929-936 - Qiuhua Liu, Xuejun Liao, Lawrence Carin:
Semi-Supervised Multitask Learning. 937-944 - Philip M. Long, Rocco A. Servedio:
Boosting the Area under the ROC Curve. 945-952 - Zhengdong Lu, Miguel Á. Carreira-Perpiñán, Cristian Sminchisescu:
People Tracking with the Laplacian Eigenmaps Latent Variable Model. 1705-1712 - Ronny Luss, Alexandre d'Aspremont:
Support Vector Machine Classification with Indefinite Kernels. 953-960 - Ulrike von Luxburg, Sébastien Bubeck, Stefanie Jegelka, Michael Kaufmann:
Consistent Minimization of Clustering Objective Functions. 961-968 - Jakob H. Macke, Guenther Zeck, Matthias Bethge:
Receptive Fields without Spike-Triggering. 969-976 - Maryam Mahdaviani, Tanzeem Choudhury:
Fast and Scalable Training of Semi-Supervised CRFs with Application to Activity Recognition. 977-984 - M. M. Hassan Mahmud, Sylvian R. Ray:
Transfer Learning using Kolmogorov Complexity: Basic Theory and Empirical Evaluations. 985-992 - Victoria Manfredi, Jim Kurose:
Scan Strategies for Meteorological Radars. 993-1000 - Omer Bobrowski, Ron Meir, Shy Shoham, Yonina C. Eldar:
A neural network implementing optimal state estimation based on dynamic spike train decoding. 145-152 - François G. Meyer, Greg J. Stephens:
Locality and low-dimensions in the prediction of natural experience from fMRI. 1001-1008 - Srinjoy Mitra, Giacomo Indiveri, Stefano Fusi:
Learning to classify complex patterns using a VLSI network of spiking neurons. 1009-1016 - Daichi Mochihashi, Eiichiro Sumita:
The Infinite Markov Model. 1017-1024 - Mehryar Mohri, Afshin Rostamizadeh:
Stability Bounds for Non-i.i.d. Processes. 1025-1032 - Michael Mozer, David Baldwin:
Experience-Guided Search: A Theory of Attentional Control. 1033-1040 - Pawan Kumar Mudigonda, Vladimir Kolmogorov, Philip H. S. Torr:
An Analysis of Convex Relaxations for MAP Estimation. 1041-1048 - Lawrence Murray, Amos J. Storkey:
Continuous Time Particle Filtering for fMRI. 1049-1056 - Andrew Naish-Guzman, Sean B. Holden:
Robust Regression with Twinned Gaussian Processes. 1065-1072 - Andrew Naish-Guzman, Sean B. Holden:
The Generalized FITC Approximation. 1057-1064 - Emre Neftci, Elisabetta Chicca, Giacomo Indiveri, Jean-Jacques E. Slotine, Rodney J. Douglas:
Contraction Properties of VLSI Cooperative Competitive Neural Networks of Spiking Neurons. 1073-1080 - David Newman, Arthur U. Asuncion, Padhraic Smyth, Max Welling:
Distributed Inference for Latent Dirichlet Allocation. 1081-1088 - XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan:
Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization. 1089-1096 - Shigeyuki Oba, Motoaki Kawanabe, Klaus-Robert Müller, Shin Ishii:
Heterogeneous Component Analysis. 1097-1104 - Manfred Opper, Guido Sanguinetti:
Variational inference for Markov jump processes. 1105-1112 - Luis E. Ortiz:
CPR for CSPs: A Probabilistic Relaxation of Constraint Propagation. 1113-1120 - Simon Osindero, Geoffrey E. Hinton:
Modeling image patches with a directed hierarchy of Markov random fields. 1121-1128 - Mehul Parsana, Sourangshu Bhattacharya, Chiru Bhattacharyya, K. R. Ramakrishnan:
Kernels on Attributed Pointsets with Applications. 1129-1136 - Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor:
A Risk Minimization Principle for a Class of Parzen Estimators. 1137-1144 - Robert J. Peters, Laurent Itti:
Congruence between model and human attention reveals unique signatures of critical visual events. 1145-1152 - Slav Petrov, Dan Klein:
Discriminative Log-Linear Grammars with Latent Variables. 1153-1160 - Jonathan W. Pillow, Peter E. Latham:
Neural characterization in partially observed populations of spiking neurons. 1161-1168 - John C. Platt, Emre Kiciman, David A. Maltz:
Fast Variational Inference for Large-scale Internet Diagnosis. 1169-1176 - Ali Rahimi, Benjamin Recht:
Random Features for Large-Scale Kernel Machines. 1177-1184 - Marc'Aurelio Ranzato, Y-Lan Boureau, Yann LeCun:
Sparse Feature Learning for Deep Belief Networks. 1185-1192 - Vinayak A. Rao, Marc W. Howard:
Retrieved context and the discovery of semantic structure. 1193-1200 - Pradeep Ravikumar, Han Liu, John D. Lafferty, Larry A. Wasserman:
SpAM: Sparse Additive Models. 1201-1208 - Vikas C. Raykar, Harald Steck, Balaji Krishnapuram, Cary Dehing-Oberije, Philippe Lambin:
On Ranking in Survival Analysis: Bounds on the Concordance Index. 1209-1216 - Michael Ross, Andrew Cohen:
GRIFT: A graphical model for inferring visual classification features from human data. 1217-1224 - Stéphane Ross, Brahim Chaib-draa, Joelle Pineau:
Bayes-Adaptive POMDPs. 1225-1232 - Stéphane Ross, Joelle Pineau, Brahim Chaib-draa:
Theoretical Analysis of Heuristic Search Methods for Online POMDPs. 1233-1240 - Nicolas Le Roux, Yoshua Bengio, Pascal Lamblin, Marc Joliveau, Balázs Kégl:
Learning the 2-D Topology of Images. 841-848 - Nicolas Le Roux, Pierre-Antoine Manzagol, Yoshua Bengio:
Topmoumoute Online Natural Gradient Algorithm. 849-856 - Bryan C. Russell, Antonio Torralba, Ce Liu, Robert Fergus, William T. Freeman:
Object Recognition by Scene Alignment. 1241-1248 - Ruslan Salakhutdinov, Andriy Mnih:
Probabilistic Matrix Factorization. 1257-1264 - Ruslan Salakhutdinov, Geoffrey E. Hinton:
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes. 1249-1256 - Adam Sanborn, Thomas L. Griffiths:
Markov Chain Monte Carlo with People. 1265-1272 - Sujay Sanghavi, Dmitry M. Malioutov, Alan S. Willsky:
Linear programming analysis of loopy belief propagation for weighted matching. 1273-1280 - Sujay Sanghavi, Devavrat Shah, Alan S. Willsky:
Message Passing for Max-weight Independent Set. 1281-1288 - Burr Settles, Mark Craven, Soumya Ray:
Multiple-Instance Active Learning. 1289-1296 - Ohad Shamir, Naftali Tishby:
Cluster Stability for Finite Samples. 1297-1304 - Tatyana O. Sharpee:
Better than least squares: comparison of objective functions for estimating linear-nonlinear models. 1305-1312 - Madhusudana V. S. Shashanka, Bhiksha Raj, Paris Smaragdis:
Sparse Overcomplete Latent Variable Decomposition of Counts Data. 1313-1320 - Daniel Sheldon, M. A. Saleh Elmohamed, Dexter Kozen:
Collective Inference on Markov Models for Modeling Bird Migration. 1321-1328 - Sajid M. Siddiqi, Byron Boots, Geoffrey J. Gordon:
A Constraint Generation Approach to Learning Stable Linear Dynamical Systems. 1329-1336 - Leonid Sigal, Alexandru O. Balan, Michael J. Black:
Combined discriminative and generative articulated pose and non-rigid shape estimation. 1337-1344 - Ricardo Bezerra de Andrade e Silva, Wei Chu, Zoubin Ghahramani:
Hidden Common Cause Relations in Relational Learning. 1345-1352 - Vikas Singh, Lopamudra Mukherjee, Jiming Peng, Jinhui Xu:
Ensemble Clustering using Semidefinite Programming. 1353-1360 - Kaushik Sinha, Mikhail Belkin:
The Value of Labeled and Unlabeled Examples when the Model is Imperfect. 1361-1368 - Fabian H. Sinz, Olivier Chapelle, Alekh Agarwal, Bernhard Schölkopf:
An Analysis of Inference with the Universum. 1369-1376 - Alexander J. Smola, S. V. N. Vishwanathan, Quoc V. Le:
Bundle Methods for Machine Learning. 1377-1384 - Le Song, Alexander J. Smola, Karsten M. Borgwardt, Arthur Gretton:
Colored Maximum Variance Unfolding. 1385-1392 - David A. Sontag, Tommi S. Jaakkola:
New Outer Bounds on the Marginal Polytope. 1393-1400 - Devarajan Sridharan, Brian Percival, John V. Arthur, Kwabena Boahen:
An in-silico Neural Model of Dynamic Routing through Neuronal Coherence. 1401-1408 - Alan Stocker, Eero P. Simoncelli:
A Bayesian Model of Conditioned Perception. 1409-1416 - Alexander L. Strehl, Michael L. Littman:
Online Linear Regression and Its Application to Model-Based Reinforcement Learning. 1417-1424 - Erik B. Sudderth, Martin J. Wainwright, Alan S. Willsky:
Loop Series and Bethe Variational Bounds in Attractive Graphical Models. 1425-1432 - Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul von Bünau, Motoaki Kawanabe:
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation. 1433-1440 - Özgür Sümer, Umut A. Acar, Alexander Ihler, Ramgopal R. Mettu:
Efficient Bayesian Inference for Dynamically Changing Graphs. 1441-1448 - Umar Syed, Robert E. Schapire:
A Game-Theoretic Approach to Apprenticeship Learning. 1449-1456 - Marie Szafranski, Yves Grandvalet, Pierre Morizet-Mahoudeaux:
Hierarchical Penalization. 1457-1464 - Yuval Tassa, Tom Erez, William D. Smart:
Receding Horizon Differential Dynamic Programming. 1465-1472 - Yee Whye Teh, Hal Daumé III, Daniel M. Roy:
Bayesian Agglomerative Clustering with Coalescents. 1473-1480 - Yee Whye Teh, Kenichi Kurihara, Max Welling:
Collapsed Variational Inference for HDP. 1481-1488 - Choon Hui Teo, Amir Globerson, Sam T. Roweis, Alexander J. Smola:
Convex Learning with Invariances. 1489-1496 - Gerald Tesauro, Rajarshi Das, Hoi Y. Chan, Jeffrey O. Kephart, David W. Levine, Freeman L. Rawson III, Charles Lefurgy:
Managing Power Consumption and Performance of Computing Systems Using Reinforcement Learning. 1497-1504 - Ambuj Tewari, Peter L. Bartlett:
Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs. 1505-1512 - Michalis K. Titsias:
The Infinite Gamma-Poisson Feature Model. 1513-1520 - Kristina Toutanova, Mark Johnson:
A Bayesian LDA-based model for semi-supervised part-of-speech tagging. 1521-1528 - Duan Tran, David A. Forsyth:
Configuration Estimates Improve Pedestrian Finding. 1529-1536 - Eric K. C. Tsang, Bertram Emil Shi:
Estimating disparity with confidence from energy neurons. 1537-1544 - Richard E. Turner, Maneesh Sahani:
Modeling Natural Sounds with Modulation Cascade Processes. 1545-1552 - Jakob Verbeek, Bill Triggs:
Scene Segmentation with CRFs Learned from Partially Labeled Images. 1553-1560 - Christian Walder, Olivier Chapelle:
Learning with Transformation Invariant Kernels. 1561-1568 - Tao Wang, Daniel J. Lizotte, Michael H. Bowling, Dale Schuurmans:
Stable Dual Dynamic Programming. 1569-1576 - Xiaogang Wang, Eric Grimson:
Spatial Latent Dirichlet Allocation. 1577-1584 - Manfred K. Warmuth, Karen A. Glocer, Gunnar Rätsch:
Boosting Algorithms for Maximizing the Soft Margin. 1585-1592 - Markus Weimer, Alexandros Karatzoglou, Quoc V. Le, Alexander J. Smola:
COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking . 1593-1600 - Max Welling, Ian Porteous, Evgeniy Bart:
Infinite State Bayes-Nets for Structured Domains. 1601-1608 - Ben H. Williams, Marc Toussaint, Amos J. Storkey:
Modelling motion primitives and their timing in biologically executed movements. 1609-1616 - David Wingate, Satinder Singh:
Exponential Family Predictive Representations of State. 1617-1624 - David P. Wipf, Srikantan S. Nagarajan:
A New View of Automatic Relevance Determination. 1625-1632 - John Wright, Yangyu Tao, Zhouchen Lin, Yi Ma, Heung-Yeung Shum:
Classification via Minimum Incremental Coding Length (MICL). 1633-1640 - Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, Michael R. Lyu:
Efficient Convex Relaxation for Transductive Support Vector Machine. 1641-1648 - Jieping Ye, Zheng Zhao, Mingrui Wu:
Discriminative K-means for Clustering. 1649-1656 - Kai Yu, Wei Chu:
Gaussian Process Models for Link Analysis and Transfer Learning. 1657-1664 - Shipeng Yu, Balaji Krishnapuram, Rómer Rosales, Harald Steck, R. Bharat Rao:
Bayesian Co-Training. 1665-1672 - Alan L. Yuille, Hongjing Lu:
The Noisy-Logical Distribution and its Application to Causal Inference. 1673-1680 - Cha Zhang, Paul A. Viola:
Multiple-Instance Pruning For Learning Efficient Cascade Detectors. 1681-1688 - Bing Zhao, Eric P. Xing:
HM-BiTAM: Bilingual Topic Exploration, Word Alignment, and Translation. 1689-1696 - 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. 1697-1704 - Shuheng Zhou, John D. Lafferty, Larry A. Wasserman:
Compressed Regression. 1713-1720 - Shenghuo Zhu, Kai Yu, Yihong Gong:
Predictive Matrix-Variate t Models. 1721-1728 - Martin Zinkevich, Michael Johanson, Michael H. Bowling, Carmelo Piccione:
Regret Minimization in Games with Incomplete Information. 1729-1736
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