Book
Advances in Neural Information Processing Systems 22 (NIPS 2009)
Edited by:
Y. Bengio and D. Schuurmans and J. Lafferty and C. Williams and A. Culotta
- Monte Carlo Sampling for Regret Minimization in Extensive Games Marc Lanctot, Kevin Waugh, Martin Zinkevich, Michael Bowling
- DUOL: A Double Updating Approach for Online Learning Peilin Zhao, Steven Hoi, Rong Jin
- Compressed Least-Squares Regression Odalric Maillard, Rémi Munos
- White Functionals for Anomaly Detection in Dynamical Systems Marco Cuturi, Jean-philippe Vert, Alexandre D'aspremont
- Learning models of object structure Joseph Schlecht, Kobus Barnard
- Neurometric function analysis of population codes Philipp Berens, Sebastian Gerwinn, Alexander Ecker, Matthias Bethge
- Slow, Decorrelated Features for Pretraining Complex Cell-like Networks Yoshua Bengio, James Bergstra
- Code-specific policy gradient rules for spiking neurons Henning Sprekeler, Guillaume Hennequin, Wulfram Gerstner
- Positive Semidefinite Metric Learning with Boosting Chunhua Shen, Junae Kim, Lei Wang, Anton Hengel
- Spatial Normalized Gamma Processes Vinayak Rao, Yee Teh
- Entropic Graph Regularization in Non-Parametric Semi-Supervised Classification Amarnag Subramanya, Jeff A. Bilmes
- Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks Stefan Klampfl, Wolfgang Maass
- Free energy score space Alessandro Perina, Marco Cristani, Umberto Castellani, Vittorio Murino, Nebojsa Jojic
- Multi-Label Prediction via Sparse Infinite CCA Piyush Rai, Hal Daume
- Fast subtree kernels on graphs Nino Shervashidze, Karsten Borgwardt
- Directed Regression Yi-hao Kao, Benjamin Roy, Xiang Yan
- Nonparametric Greedy Algorithms for the Sparse Learning Problem Han Liu, Xi Chen
- Rethinking LDA: Why Priors Matter Hanna Wallach, David Mimno, Andrew McCallum
- Augmenting Feature-driven fMRI Analyses: Semi-supervised learning and resting state activity Andreas Bartels, Matthew Blaschko, Jacquelyn Shelton
- Differential Use of Implicit Negative Evidence in Generative and Discriminative Language Learning Anne Hsu, Thomas Griffiths
- Non-stationary continuous dynamic Bayesian networks Marco Grzegorczyk, Dirk Husmeier
- Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data Shuai Huang, Jing Li, Liang Sun, Jun Liu, Teresa Wu, Kewei Chen, Adam Fleisher, Eric Reiman, Jieping Ye
- Potential-Based Agnostic Boosting Varun Kanade, Adam Kalai
- Gaussian process regression with Student-t likelihood Jarno Vanhatalo, Pasi Jylänki, Aki Vehtari
- Bilinear classifiers for visual recognition Hamed Pirsiavash, Deva Ramanan, Charless Fowlkes
- Zero-shot Learning with Semantic Output Codes Mark Palatucci, Dean Pomerleau, Geoffrey E. Hinton, Tom M. Mitchell
- Learning Label Embeddings for Nearest-Neighbor Multi-class Classification with an Application to Speech Recognition Natasha Singh-miller, Michael Collins
- Semi-Supervised Learning in Gigantic Image Collections Rob Fergus, Yair Weiss, Antonio Torralba
- Sensitivity analysis in HMMs with application to likelihood maximization Pierre-arnaud Coquelin, Romain Deguest, Rémi Munos
- Who’s Doing What: Joint Modeling of Names and Verbs for Simultaneous Face and Pose Annotation Jie Luo, Barbara Caputo, Vittorio Ferrari
- Streaming Pointwise Mutual Information Benjamin Durme, Ashwin Lall
- Nonparametric Bayesian Models for Unsupervised Event Coreference Resolution Cosmin Bejan, Matthew Titsworth, Andrew Hickl, Sanda Harabagiu
- A Stochastic approximation method for inference in probabilistic graphical models Peter Carbonetto, Matthew King, Firas Hamze
- Factor Modeling for Advertisement Targeting Ye Chen, Michael Kapralov, John Canny, Dmitry Pavlov
- Sparse Estimation Using General Likelihoods and Non-Factorial Priors David Wipf, Srikantan Nagarajan
- Modeling the spacing effect in sequential category learning Hongjing Lu, Matthew Weiden, Alan L. Yuille
- An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism Douglas Eck, Yoshua Bengio, Aaron C. Courville
- Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process Finale Doshi-velez, Shakir Mohamed, Zoubin Ghahramani, David Knowles
- Matrix Completion from Power-Law Distributed Samples Raghu Meka, Prateek Jain, Inderjit Dhillon
- Filtering Abstract Senses From Image Search Results Kate Saenko, Trevor Darrell
- Heavy-Tailed Symmetric Stochastic Neighbor Embedding Zhirong Yang, Irwin King, Zenglin Xu, Erkki Oja
- Which graphical models are difficult to learn? Andrea Montanari, Jose Pereira
- Information-theoretic lower bounds on the oracle complexity of convex optimization Alekh Agarwal, Martin J. Wainwright, Peter Bartlett, Pradeep Ravikumar
- Linear-time Algorithms for Pairwise Statistical Problems Parikshit Ram, Dongryeol Lee, William March, Alexander Gray
- From PAC-Bayes Bounds to KL Regularization Pascal Germain, Alexandre Lacasse, Mario Marchand, Sara Shanian, François Laviolette
- On Stochastic and Worst-case Models for Investing Elad Hazan, Satyen Kale
- Structured output regression for detection with partial truncation Andrea Vedaldi, Andrew Zisserman
- On Invariance in Hierarchical Models Jake Bouvrie, Lorenzo Rosasco, Tomaso Poggio
- Submanifold density estimation Arkadas Ozakin, Alexander Gray
- Nonlinear Learning using Local Coordinate Coding Kai Yu, Tong Zhang, Yihong Gong
- No evidence for active sparsification in the visual cortex Pietro Berkes, Ben White, Jozsef Fiser
- Distribution Matching for Transduction Novi Quadrianto, James Petterson, Alex Smola
- Canonical Time Warping for Alignment of Human Behavior Feng Zhou, Fernando Torre
- A Parameter-free Hedging Algorithm Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu
- Learning Bregman Distance Functions and Its Application for Semi-Supervised Clustering Lei Wu, Rong Jin, Steven Hoi, Jianke Zhu, Nenghai Yu
- Ranking Measures and Loss Functions in Learning to Rank Wei Chen, Tie-yan Liu, Yanyan Lan, Zhi-ming Ma, Hang Li
- Constructing Topological Maps using Markov Random Fields and Loop-Closure Detection Roy Anati, Kostas Daniilidis
- Replicated Softmax: an Undirected Topic Model Geoffrey E. Hinton, Russ R. Salakhutdinov
- Fast, smooth and adaptive regression in metric spaces Samory Kpotufe
- Bayesian Belief Polarization Alan Jern, Kai-min Chang, Charles Kemp
- Linearly constrained Bayesian matrix factorization for blind source separation Mikkel Schmidt
- Sparse and Locally Constant Gaussian Graphical Models Jean Honorio, Dimitris Samaras, Nikos Paragios, Rita Goldstein, Luis E. Ortiz
- Sparse Metric Learning via Smooth Optimization Yiming Ying, Kaizhu Huang, Colin Campbell
- The 'tree-dependent components' of natural scenes are edge filters Daniel Zoran, Yair Weiss
- Bootstrapping from Game Tree Search Joel Veness, David Silver, Alan Blair, William Uther
- Individuation, Identification and Object Discovery Charles Kemp, Alan Jern, Fei Xu
- Modeling Social Annotation Data with Content Relevance using a Topic Model Tomoharu Iwata, Takeshi Yamada, Naonori Ueda
- Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation Shalabh Bhatnagar, Doina Precup, David Silver, Richard S. Sutton, Hamid Maei, Csaba Szepesvári
- Group Sparse Coding Samy Bengio, Fernando Pereira, Yoram Singer, Dennis Strelow
- Decoupling Sparsity and Smoothness in the Discrete Hierarchical Dirichlet Process Chong Wang, David Blei
- Fast Image Deconvolution using Hyper-Laplacian Priors Dilip Krishnan, Rob Fergus
- Compositionality of optimal control laws Emanuel Todorov
- AUC optimization and the two-sample problem Nicolas Vayatis, Marine Depecker, Stéphan Clémençcon
- Measuring Invariances in Deep Networks Ian Goodfellow, Honglak Lee, Quoc Le, Andrew Saxe, Andrew Ng
- Nonparametric Latent Feature Models for Link Prediction Kurt Miller, Michael Jordan, Thomas Griffiths
- Asymptotically Optimal Regularization in Smooth Parametric Models Percy S. Liang, Guillaume Bouchard, Francis Bach, Michael Jordan
- Variational Gaussian-process factor analysis for modeling spatio-temporal data Jaakko Luttinen, Alexander Ilin
- Lattice Regression Eric Garcia, Maya Gupta
- Sharing Features among Dynamical Systems with Beta Processes Emily Fox, Michael Jordan, Erik Sudderth, Alan Willsky
- The Wisdom of Crowds in the Recollection of Order Information Mark Steyvers, Brent Miller, Pernille Hemmer, Michael Lee
- Efficient Recovery of Jointly Sparse Vectors Liang Sun, Jun Liu, Jianhui Chen, Jieping Ye
- Efficient Match Kernel between Sets of Features for Visual Recognition Liefeng Bo, Cristian Sminchisescu
- Clustering sequence sets for motif discovery Jong Kim, Seungjin Choi
- Bayesian Nonparametric Models on Decomposable Graphs Francois Caron, Arnaud Doucet
- Correlation Coefficients are Insufficient for Analyzing Spike Count Dependencies Arno Onken, Steffen Grünewälder, Klaus Obermayer
- Streaming k-means approximation Nir Ailon, Ragesh Jaiswal, Claire Monteleoni
- Help or Hinder: Bayesian Models of Social Goal Inference Tomer Ullman, Chris Baker, Owen Macindoe, Owain Evans, Noah Goodman, Joshua Tenenbaum
- Sequential effects reflect parallel learning of multiple environmental regularities Matthew Wilder, Matt Jones, Michael C. Mozer
- Noisy Generalized Binary Search Robert Nowak
- Solving Stochastic Games Liam Dermed, Charles Isbell
- Modelling Relational Data using Bayesian Clustered Tensor Factorization Ilya Sutskever, Joshua Tenenbaum, Russ R. Salakhutdinov
- Kernel Methods for Deep Learning Youngmin Cho, Lawrence Saul
- Optimal context separation of spiking haptic signals by second-order somatosensory neurons Romain Brasselet, Roland Johansson, Angelo Arleo
- Heterogeneous multitask learning with joint sparsity constraints Xiaolin Yang, Seyoung Kim, Eric Xing
- Learning with Compressible Priors Volkan Cevher
- Inter-domain Gaussian Processes for Sparse Inference using Inducing Features Miguel Lázaro-Gredilla, Aníbal Figueiras-Vidal
- Particle-based Variational Inference for Continuous Systems Andrew Frank, Padhraic Smyth, Alexander Ihler
- Efficient Learning using Forward-Backward Splitting Yoram Singer, John C. Duchi
- $L_1$-Penalized Robust Estimation for a Class of Inverse Problems Arising in Multiview Geometry Arnak Dalalyan, Renaud Keriven
- Learning to Hash with Binary Reconstructive Embeddings Brian Kulis, Trevor Darrell
- Multi-Label Prediction via Compressed Sensing Daniel J. Hsu, Sham M. Kakade, John Langford, Tong Zhang
- Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions Kenji Fukumizu, Arthur Gretton, Gert Lanckriet, Bernhard Schölkopf, Bharath K. Sriperumbudur
- Statistical Analysis of Semi-Supervised Learning: The Limit of Infinite Unlabelled Data Boaz Nadler, Nathan Srebro, Xueyuan Zhou
- Maximin affinity learning of image segmentation Kevin Briggman, Winfried Denk, Sebastian Seung, Moritz Helmstaedter, Srinivas C. Turaga
- Perceptual Multistability as Markov Chain Monte Carlo Inference Samuel Gershman, Ed Vul, Joshua Tenenbaum
- Probabilistic Relational PCA Wu-jun Li, Dit-Yan Yeung, Zhihua Zhang
- Adapting to the Shifting Intent of Search Queries Umar Syed, Aleksandrs Slivkins, Nina Mishra
- Predicting the Optimal Spacing of Study: A Multiscale Context Model of Memory Harold Pashler, Nicholas Cepeda, Robert V. Lindsey, Ed Vul, Michael C. Mozer
- A Game-Theoretic Approach to Hypergraph Clustering Samuel Bulò, Marcello Pelillo
- Dirichlet-Bernoulli Alignment: A Generative Model for Multi-Class Multi-Label Multi-Instance Corpora Shuang-hong Yang, Hongyuan Zha, Bao-gang Hu
- Adaptive Design Optimization in Experiments with People Daniel Cavagnaro, Jay Myung, Mark Pitt
- Riffled Independence for Ranked Data Jonathan Huang, Carlos Guestrin
- A Neural Implementation of the Kalman Filter Robert Wilson, Leif Finkel
- An LP View of the M-best MAP problem Menachem Fromer, Amir Globerson
- Speeding up Magnetic Resonance Image Acquisition by Bayesian Multi-Slice Adaptive Compressed Sensing Matthias Seeger
- Toward Provably Correct Feature Selection in Arbitrary Domains Dimitris Margaritis
- 3D Object Recognition with Deep Belief Nets Vinod Nair, Geoffrey E. Hinton
- Improving Existing Fault Recovery Policies Guy Shani, Christopher Meek
- Convex Relaxation of Mixture Regression with Efficient Algorithms Novi Quadrianto, John Lim, Dale Schuurmans, Tibério Caetano
- Hierarchical Learning of Dimensional Biases in Human Categorization Adam Sanborn, Nick Chater, Katherine A. Heller
- Polynomial Semantic Indexing Bing Bai, Jason Weston, David Grangier, Ronan Collobert, Kunihiko Sadamasa, Yanjun Qi, Corinna Cortes, Mehryar Mohri
- Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling Lei Shi, Thomas Griffiths
- Discriminative Network Models of Schizophrenia Irina Rish, Benjamin Thyreau, Bertrand Thirion, Marion Plaze, Marie-laure Paillere-martinot, Catherine Martelli, Jean-luc Martinot, Jean-baptiste Poline, Guillermo Cecchi
- Sparsistent Learning of Varying-coefficient Models with Structural Changes Mladen Kolar, Le Song, Eric Xing
- Dual Averaging Method for Regularized Stochastic Learning and Online Optimization Lin Xiao
- Analysis of SVM with Indefinite Kernels Yiming Ying, Colin Campbell, Mark Girolami
- Quantification and the language of thought Charles Kemp
- Exploring Functional Connectivities of the Human Brain using Multivariate Information Analysis Barry Chai, Dirk Walther, Diane Beck, Li Fei-fei
- Semi-supervised Learning using Sparse Eigenfunction Bases Kaushik Sinha, Mikhail Belkin
- On Learning Rotations Raman Arora
- A Gaussian Tree Approximation for Integer Least-Squares Jacob Goldberger, Amir Leshem
- Nonlinear directed acyclic structure learning with weakly additive noise models Arthur Gretton, Peter Spirtes, Robert Tillman
- FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs Andrew McCallum, Karl Schultz, Sameer Singh
- Exponential Family Graph Matching and Ranking James Petterson, Jin Yu, Julian Mcauley, Tibério Caetano
- Extending Phase Mechanism to Differential Motion Opponency for Motion Pop-out Yicong Meng, Bertram Shi
- Periodic Step Size Adaptation for Single Pass On-line Learning Chun-nan Hsu, Yu-ming Chang, Hanshen Huang, Yuh-jye Lee
- Construction of Nonparametric Bayesian Models from Parametric Bayes Equations Peter Orbanz
- Adaptive Regularization for Transductive Support Vector Machine Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, Michael Lyu, Zhirong Yang
- Subject independent EEG-based BCI decoding Siamac Fazli, Cristian Grozea, Marton Danoczy, Benjamin Blankertz, Florin Popescu, Klaus-Robert Müller
- On the Convergence of the Concave-Convex Procedure Gert Lanckriet, Bharath K. Sriperumbudur
- Orthogonal Matching Pursuit From Noisy Random Measurements: A New Analysis Sundeep Rangan, Alyson K. Fletcher
- Noise Characterization, Modeling, and Reduction for In Vivo Neural Recording Zhi Yang, Qi Zhao, Edward Keefer, Wentai Liu
- Adaptive Regularization of Weight Vectors Koby Crammer, Alex Kulesza, Mark Dredze
- Posterior vs Parameter Sparsity in Latent Variable Models Kuzman Ganchev, Ben Taskar, Fernando Pereira, João Gama
- Reconstruction of Sparse Circuits Using Multi-neuronal Excitation (RESCUME) Tao Hu, Anthony Leonardo, Dmitri Chklovskii
- Label Selection on Graphs Andrew Guillory, Jeff A. Bilmes
- A Fast, Consistent Kernel Two-Sample Test Arthur Gretton, Kenji Fukumizu, Zaïd Harchaoui, Bharath K. Sriperumbudur
- Robust Nonparametric Regression with Metric-Space Valued Output Matthias Hein
- Kernels and learning curves for Gaussian process regression on random graphs Peter Sollich, Matthew Urry, Camille Coti
- Thresholding Procedures for High Dimensional Variable Selection and Statistical Estimation Shuheng Zhou
- Beyond Categories: The Visual Memex Model for Reasoning About Object Relationships Tomasz Malisiewicz, Alyosha Efros
- Conditional Random Fields with High-Order Features for Sequence Labeling Nan Ye, Wee Lee, Hai Chieu, Dan Wu
- Abstraction and Relational learning Charles Kemp, Alan Jern
- Fast Graph Laplacian Regularized Kernel Learning via Semidefinite–Quadratic–Linear Programming Xiao-ming Wu, Anthony So, Zhenguo Li, Shuo-yen Li
- A Data-Driven Approach to Modeling Choice Vivek Farias, Srikanth Jagabathula, Devavrat Shah
- Anomaly Detection with Score functions based on Nearest Neighbor Graphs Manqi Zhao, Venkatesh Saligrama
- Submodularity Cuts and Applications Yoshinobu Kawahara, Kiyohito Nagano, Koji Tsuda, Jeff A. Bilmes
- Bayesian Sparse Factor Models and DAGs Inference and Comparison Ricardo Henao, Ole Winther
- A Sparse Non-Parametric Approach for Single Channel Separation of Known Sounds Paris Smaragdis, Madhusudana Shashanka, Bhiksha Raj
- Data-driven calibration of linear estimators with minimal penalties Sylvain Arlot, Francis Bach
- fMRI-Based Inter-Subject Cortical Alignment Using Functional Connectivity Bryan Conroy, Ben Singer, James Haxby, Peter J. Ramadge
- Unsupervised feature learning for audio classification using convolutional deep belief networks Honglak Lee, Peter Pham, Yan Largman, Andrew Ng
- Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models Baback Moghaddam, Emtiyaz Khan, Kevin P. Murphy, Benjamin M. Marlin
- Learning transport operators for image manifolds Benjamin Culpepper, Bruno Olshausen
- Manifold Embeddings for Model-Based Reinforcement Learning under Partial Observability Keith Bush, Joelle Pineau
- Ensemble Nystrom Method Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar
- Manifold Regularization for SIR with Rate Root-n Convergence Wei Bian, Dacheng Tao
- STDP enables spiking neurons to detect hidden causes of their inputs Bernhard Nessler, Michael Pfeiffer, Wolfgang Maass
- Locality-sensitive binary codes from shift-invariant kernels Maxim Raginsky, Svetlana Lazebnik
- Regularized Distance Metric Learning:Theory and Algorithm Rong Jin, Shijun Wang, Yang Zhou
- Time-Varying Dynamic Bayesian Networks Le Song, Mladen Kolar, Eric Xing
- A General Projection Property for Distribution Families Yao-liang Yu, Yuxi Li, Dale Schuurmans, Csaba Szepesvári
- Region-based Segmentation and Object Detection Stephen Gould, Tianshi Gao, Daphne Koller
- Hierarchical Mixture of Classification Experts Uncovers Interactions between Brain Regions Bangpeng Yao, Dirk Walther, Diane Beck, Li Fei-fei
- Unsupervised Detection of Regions of Interest Using Iterative Link Analysis Gunhee Kim, Antonio Torralba
- Fast Learning from Non-i.i.d. Observations Ingo Steinwart, Andreas Christmann
- Evaluating multi-class learning strategies in a generative hierarchical framework for object detection Sanja Fidler, Marko Boben, Ales Leonardis
- Optimal Scoring for Unsupervised Learning Zhihua Zhang, Guang Dai
- Matrix Completion from Noisy Entries Raghunandan Keshavan, Andrea Montanari, Sewoong Oh
- A Generalized Natural Actor-Critic Algorithm Tetsuro Morimura, Eiji Uchibe, Junichiro Yoshimoto, Kenji Doya
- Efficient Moments-based Permutation Tests Chunxiao Zhou, Huixia Wang, Yongmei Wang
- Maximum likelihood trajectories for continuous-time Markov chains Theodore Perkins
- Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness Garvesh Raskutti, Bin Yu, Martin J. Wainwright
- Sufficient Conditions for Agnostic Active Learnable Liwei Wang
- The Ordered Residual Kernel for Robust Motion Subspace Clustering Tat-jun Chin, Hanzi Wang, David Suter
- Learning to Rank by Optimizing NDCG Measure Hamed Valizadegan, Rong Jin, Ruofei Zhang, Jianchang Mao
- Slow Learners are Fast Martin Zinkevich, John Langford, Alex Smola
- Tracking Dynamic Sources of Malicious Activity at Internet Scale Shobha Venkataraman, Avrim Blum, Dawn Song, Subhabrata Sen, Oliver Spatscheck
- Graph Zeta Function in the Bethe Free Energy and Loopy Belief Propagation Yusuke Watanabe, Kenji Fukumizu
- Learning in Markov Random Fields using Tempered Transitions Russ R. Salakhutdinov
- Statistical Consistency of Top-k Ranking Fen Xia, Tie-yan Liu, Hang Li
- Speaker Comparison with Inner Product Discriminant Functions Zahi Karam, Douglas Sturim, William Campbell
- Asymptotic Analysis of MAP Estimation via the Replica Method and Compressed Sensing Sundeep Rangan, Vivek Goyal, Alyson K. Fletcher
- Statistical Models of Linear and Nonlinear Contextual Interactions in Early Visual Processing Ruben Coen-cagli, Peter Dayan, Odelia Schwartz
- A joint maximum-entropy model for binary neural population patterns and continuous signals Sebastian Gerwinn, Philipp Berens, Matthias Bethge
- Hierarchical Modeling of Local Image Features through $L_p$-Nested Symmetric Distributions Matthias Bethge, Eero Simoncelli, Fabian Sinz
- Optimizing Multi-Class Spatio-Spectral Filters via Bayes Error Estimation for EEG Classification Wenming Zheng, Zhouchen Lin
- Nash Equilibria of Static Prediction Games Michael Brückner, Tobias Scheffer
- Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization John Wright, Arvind Ganesh, Shankar Rao, Yigang Peng, Yi Ma
- Unsupervised Feature Selection for the $k$-means Clustering Problem Christos Boutsidis, Petros Drineas, Michael W. Mahoney
- Multi-Step Dyna Planning for Policy Evaluation and Control Hengshuai Yao, Shalabh Bhatnagar, Dongcui Diao, Richard S. Sutton, Csaba Szepesvári
- Variational Inference for the Nested Chinese Restaurant Process Chong Wang, David Blei
- A Biologically Plausible Model for Rapid Natural Scene Identification Sennay Ghebreab, Steven Scholte, Victor Lamme, Arnold Smeulders
- Learning from Neighboring Strokes: Combining Appearance and Context for Multi-Domain Sketch Recognition Tom Ouyang, Randall Davis
- Approximating MAP by Compensating for Structural Relaxations Arthur Choi, Adnan Darwiche
- Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations Mingyuan Zhou, Haojun Chen, Lu Ren, Guillermo Sapiro, Lawrence Carin, John Paisley
- Discrete MDL Predicts in Total Variation Marcus Hutter
- Efficient and Accurate Lp-Norm Multiple Kernel Learning Marius Kloft, Ulf Brefeld, Pavel Laskov, Klaus-Robert Müller, Alexander Zien, Sören Sonnenburg
- Occlusive Components Analysis Jörg Lücke, Richard Turner, Maneesh Sahani, Marc Henniges
- Distribution-Calibrated Hierarchical Classification Ofer Dekel
- A Smoothed Approximate Linear Program Vijay Desai, Vivek Farias, Ciamac C. Moallemi
- Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model Ed Vul, George Alvarez, Joshua Tenenbaum, Michael Black
- Graph-based Consensus Maximization among Multiple Supervised and Unsupervised Models Jing Gao, Feng Liang, Wei Fan, Yizhou Sun, Jiawei Han
- Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models Ryan Mcdonald, Mehryar Mohri, Nathan Silberman, Dan Walker, Gideon Mann
- On the Algorithmics and Applications of a Mixed-norm based Kernel Learning Formulation Saketha Jagarlapudi, Dinesh G, Raman S, Chiranjib Bhattacharyya, Aharon Ben-tal, Ramakrishnan K.r.
- Estimating image bases for visual image reconstruction from human brain activity Yusuke Fujiwara, Yoichi Miyawaki, Yukiyasu Kamitani
- A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers Sahand Negahban, Bin Yu, Martin J. Wainwright, Pradeep Ravikumar
- Grouped Orthogonal Matching Pursuit for Variable Selection and Prediction Grzegorz Swirszcz, Naoki Abe, Aurelie C. Lozano
- Rank-Approximate Nearest Neighbor Search: Retaining Meaning and Speed in High Dimensions Parikshit Ram, Dongryeol Lee, Hua Ouyang, Alexander Gray
- Online Learning of Assignments Matthew Streeter, Daniel Golovin, Andreas Krause
- Skill Discovery in Continuous Reinforcement Learning Domains using Skill Chaining George Konidaris, Andrew Barto
- Strategy Grafting in Extensive Games Kevin Waugh, Nolan Bard, Michael Bowling
- Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference Khashayar Rohanimanesh, Sameer Singh, Andrew McCallum, Michael Black
- Learning a Small Mixture of Trees M. Kumar, Daphne Koller
- An Additive Latent Feature Model for Transparent Object Recognition Mario Fritz, Gary Bradski, Sergey Karayev, Trevor Darrell, Michael Black
- Know Thy Neighbour: A Normative Theory of Synaptic Depression Jean-pascal Pfister, Peter Dayan, Máté Lengyel
- Randomized Pruning: Efficiently Calculating Expectations in Large Dynamic Programs Alexandre Bouchard-côté, Slav Petrov, Dan Klein
- A Rate Distortion Approach for Semi-Supervised Conditional Random Fields Yang Wang, Gholamreza Haffari, Shaojun Wang, Greg Mori
- Learning to Explore and Exploit in POMDPs Chenghui Cai, Xuejun Liao, Lawrence Carin
- Multiple Incremental Decremental Learning of Support Vector Machines Masayuki Karasuyama, Ichiro Takeuchi
- Bayesian Source Localization with the Multivariate Laplace Prior Marcel Gerven, Botond Cseke, Robert Oostenveld, Tom Heskes
- Learning Non-Linear Combinations of Kernels Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh
- Conditional Neural Fields Jian Peng, Liefeng Bo, Jinbo Xu
- An Online Algorithm for Large Scale Image Similarity Learning Gal Chechik, Uri Shalit, Varun Sharma, Samy Bengio
- Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning Steven Chase, Andrew Schwartz, Wolfgang Maass, Robert Legenstein
- The Infinite Partially Observable Markov Decision Process Finale Doshi-velez
- Accelerated Gradient Methods for Stochastic Optimization and Online Learning Chonghai Hu, Weike Pan, James Kwok
- Robust Value Function Approximation Using Bilinear Programming Marek Petrik, Shlomo Zilberstein
- Parallel Inference for Latent Dirichlet Allocation on Graphics Processing Units Feng Yan, Ningyi Xu, Yuan Qi
- Local Rules for Global MAP: When Do They Work ? Kyomin Jung, Pushmeet Kohli, Devavrat Shah
- Bayesian estimation of orientation preference maps Sebastian Gerwinn, Leonard White, Matthias Kaschube, Matthias Bethge, Jakob H. Macke
- A Bayesian Analysis of Dynamics in Free Recall Richard Socher, Samuel Gershman, Per Sederberg, Kenneth Norman, Adler Perotte, David Blei
- Structural inference affects depth perception in the context of potential occlusion Ian Stevenson, Konrad Koerding
- Indian Buffet Processes with Power-law Behavior Yee Teh, Dilan Gorur
- Boosting with Spatial Regularization Yongxin Xi, Uri Hasson, Peter J. Ramadge, Zhen Xiang
- Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models Jonathan Pillow
- Semi-supervised Regression using Hessian energy with an application to semi-supervised dimensionality reduction Kwang Kim, Florian Steinke, Matthias Hein
- Localizing Bugs in Program Executions with Graphical Models Laura Dietz, Valentin Dallmeier, Andreas Zeller, Tobias Scheffer
- Human Rademacher Complexity Jerry Zhu, Bryan Gibson, Timothy T. Rogers
- Beyond Convexity: Online Submodular Minimization Elad Hazan, Satyen Kale
- Learning from Multiple Partially Observed Views - an Application to Multilingual Text Categorization Massih R. Amini, Nicolas Usunier, Cyril Goutte
- Measuring model complexity with the prior predictive Wolf Vanpaemel
- Nonparametric Bayesian Texture Learning and Synthesis Long Zhu, Yuanahao Chen, Bill Freeman, Antonio Torralba
- A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation Lan Du, Lu Ren, Lawrence Carin, David Dunson
- Whose Vote Should Count More: Optimal Integration of Labels from Labelers of Unknown Expertise Jacob Whitehill, Ting-fan Wu, Jacob Bergsma, Javier Movellan, Paul Ruvolo
- Reading Tea Leaves: How Humans Interpret Topic Models Jonathan Chang, Sean Gerrish, Chong Wang, Jordan Boyd-graber, David Blei
- Segmenting Scenes by Matching Image Composites Bryan Russell, Alyosha Efros, Josef Sivic, Bill Freeman, Andrew Zisserman
- Generalization Errors and Learning Curves for Regression with Multi-task Gaussian Processes Kian Chai
- Breaking Boundaries Between Induction Time and Diagnosis Time Active Information Acquisition Ashish Kapoor, Eric Horvitz
- An Integer Projected Fixed Point Method for Graph Matching and MAP Inference Marius Leordeanu, Martial Hebert, Rahul Sukthankar
- Efficient Bregman Range Search Lawrence Cayton
- Complexity of Decentralized Control: Special Cases Martin Allen, Shlomo Zilberstein
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