Advances in Neural Information Processing Systems 23 (NIPS 2010)

• Agnostic Active Learning Without Constraints Alina Beygelzimer, Daniel J. Hsu, John Langford, Tong Zhang
• A Dirty Model for Multi-task Learning Ali Jalali, Sujay Sanghavi, Chao Ruan, Pradeep Ravikumar
• Generative Local Metric Learning for Nearest Neighbor Classification Yung-kyun Noh, Byoung-tak Zhang, Daniel Lee
• Relaxed Clipping: A Global Training Method for Robust Regression and Classification Min Yang, Linli Xu, Martha White, Dale Schuurmans, Yao-liang Yu
• Linear readout from a neural population with partial correlation data Adrien Wohrer, Ranulfo Romo, Christian K. Machens
• On Herding and the Perceptron Cycling Theorem Andrew Gelfand, Yutian Chen, Laurens Maaten, Max Welling
• Tiled convolutional neural networks Jiquan Ngiam, Zhenghao Chen, Daniel Chia, Pang Koh, Quoc Le, Andrew Ng
• Decomposing Isotonic Regression for Efficiently Solving Large Problems Ronny Luss, Saharon Rosset, Moni Shahar
• Learning Kernels with Radiuses of Minimum Enclosing Balls Kun Gai, Guangyun Chen, Chang-shui Zhang
• Label Embedding Trees for Large Multi-Class Tasks Samy Bengio, Jason Weston, David Grangier
• Deep Coding Network Yuanqing Lin, Tong Zhang, Shenghuo Zhu, Kai Yu
• Transduction with Matrix Completion: Three Birds with One Stone Andrew Goldberg, Ben Recht, Junming Xu, Robert Nowak, Jerry Zhu
• Extended Bayesian Information Criteria for Gaussian Graphical Models Rina Foygel, Mathias Drton
• Estimating Spatial Layout of Rooms using Volumetric Reasoning about Objects and Surfaces Abhinav Gupta, Martial Hebert, Takeo Kanade, David Blei
• A Computational Decision Theory for Interactive Assistants Alan Fern, Prasad Tadepalli
• Large Margin Multi-Task Metric Learning Shibin Parameswaran, Kilian Q. Weinberger
• Evaluating neuronal codes for inference using Fisher information Haefner Ralf, Matthias Bethge
• Guaranteed Rank Minimization via Singular Value Projection Prateek Jain, Raghu Meka, Inderjit Dhillon
• Double Q-learning Hado Hasselt
• Generalized roof duality and bisubmodular functions Vladimir Kolmogorov
• Efficient and Robust Feature Selection via Joint ℓ2,1-Norms Minimization Feiping Nie, Heng Huang, Xiao Cai, Chris Ding
• Repeated Games against Budgeted Adversaries Jacob D. Abernethy, Manfred K. K. Warmuth
• Switching state space model for simultaneously estimating state transitions and nonstationary firing rates Ken Takiyama, Masato Okada
• Why are some word orders more common than others? A uniform information density account Luke Maurits, Dan Navarro, Amy Perfors
• Getting lost in space: Large sample analysis of the resistance distance Ulrike Luxburg, Agnes Radl, Matthias Hein
• Multiparty Differential Privacy via Aggregation of Locally Trained Classifiers Manas Pathak, Shantanu Rane, Bhiksha Raj
• Convex Multiple-Instance Learning by Estimating Likelihood Ratio Fuxin Li, Cristian Sminchisescu
• Short-term memory in neuronal networks through dynamical compressed sensing Surya Ganguli, Haim Sompolinsky
• The Multidimensional Wisdom of Crowds Peter Welinder, Steve Branson, Pietro Perona, Serge Belongie
• Boosting Classifier Cascades Nuno Vasconcelos, Mohammad Saberian
• Implicitly Constrained Gaussian Process Regression for Monocular Non-Rigid Pose Estimation Mathieu Salzmann, Raquel Urtasun
• Effects of Synaptic Weight Diffusion on Learning in Decision Making Networks Kentaro Katahira, Kazuo Okanoya, Masato Okada
• Interval Estimation for Reinforcement-Learning Algorithms in Continuous-State Domains Martha White, Adam White
• Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification Li-jia Li, Hao Su, Li Fei-fei, Eric Xing
• Using body-anchored priors for identifying actions in single images Leonid Karlinsky, Michael Dinerstein, Shimon Ullman
• Reward Design via Online Gradient Ascent Jonathan Sorg, Richard L. Lewis, Satinder Singh
• Universal Consistency of Multi-Class Support Vector Classification Tobias Glasmachers
• Supervised Clustering Pranjal Awasthi, Reza Zadeh
• Inferring Stimulus Selectivity from the Spatial Structure of Neural Network Dynamics Kanaka Rajan, L Abbott, Haim Sompolinsky
• Distributionally Robust Markov Decision Processes Huan Xu, Shie Mannor
• Empirical Risk Minimization with Approximations of Probabilistic Grammars Noah A. Smith, Shay Cohen
• MAP Estimation for Graphical Models by Likelihood Maximization Akshat Kumar, Shlomo Zilberstein
• Identifying graph-structured activation patterns in networks James Sharpnack, Aarti Singh
• Size Matters: Metric Visual Search Constraints from Monocular Metadata Mario Fritz, Kate Saenko, Trevor Darrell
• Near-Optimal Bayesian Active Learning with Noisy Observations Daniel Golovin, Andreas Krause, Debajyoti Ray
• Probabilistic Belief Revision with Structural Constraints Peter Jones, Venkatesh Saligrama, Sanjoy Mitter
• Structured Determinantal Point Processes Alex Kulesza, Ben Taskar
• b-Bit Minwise Hashing for Estimating Three-Way Similarities Ping Li, Arnd Konig, Wenhao Gui
• Feature Transitions with Saccadic Search: Size, Color, and Orientation Are Not Alike Stella Yu
• Auto-Regressive HMM Inference with Incomplete Data for Short-Horizon Wind Forecasting Chris Barber, Joseph Bockhorst, Paul Roebber
• Link Discovery using Graph Feature Tracking Emile Richard, Nicolas Baskiotis, Theodoros Evgeniou, Nicolas Vayatis
• A VLSI Implementation of the Adaptive Exponential Integrate-and-Fire Neuron Model Sebastian Millner, Andreas Grübl, Karlheinz Meier, Johannes Schemmel, Marc-olivier Schwartz
• Sparse Inverse Covariance Selection via Alternating Linearization Methods Katya Scheinberg, Shiqian Ma, Donald Goldfarb
• Constructing Skill Trees for Reinforcement Learning Agents from Demonstration Trajectories George Konidaris, Scott Kuindersma, Roderic Grupen, Andrew Barto
• Trading off Mistakes and Don't-Know Predictions Amin Sayedi, Morteza Zadimoghaddam, Avrim Blum
• Evaluation of Rarity of Fingerprints in Forensics Chang Su, Sargur Srihari
• (RF)^2 -- Random Forest Random Field Nadia Payet, Sinisa Todorovic
• Online Learning in The Manifold of Low-Rank Matrices Uri Shalit, Daphna Weinshall, Gal Chechik
• Variational bounds for mixed-data factor analysis Mohammad Emtiyaz E. Khan, Guillaume Bouchard, Kevin P. Murphy, Benjamin M. Marlin
• Copula Bayesian Networks Gal Elidan
• Evidence-Specific Structures for Rich Tractable CRFs Anton Chechetka, Carlos Guestrin
• Beyond Actions: Discriminative Models for Contextual Group Activities Tian Lan, Yang Wang, Weilong Yang, Greg Mori
• Decoding Ipsilateral Finger Movements from ECoG Signals in Humans Yuzong Liu, Mohit Sharma, Charles Gaona, Jonathan Breshears, Jarod Roland, Zachary Freudenburg, Eric Leuthardt, Kilian Q. Weinberger
• New Adaptive Algorithms for Online Classification Francesco Orabona, Koby Crammer
• Phoneme Recognition with Large Hierarchical Reservoirs Fabian Triefenbach, Azarakhsh Jalalvand, Benjamin Schrauwen, Jean-pierre Martens
• Learning Multiple Tasks using Manifold Regularization Arvind Agarwal, Samuel Gerber, Hal Daume
• Group Sparse Coding with a Laplacian Scale Mixture Prior Pierre Garrigues, Bruno Olshausen
• Fractionally Predictive Spiking Neurons Jaldert Rombouts, Sander Bohte
• Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models Han Liu, Kathryn Roeder, Larry Wasserman
• More data means less inference: A pseudo-max approach to structured learning David Sontag, Ofer Meshi, Amir Globerson, Tommi Jaakkola
• Lifted Inference Seen from the Other Side : The Tractable Features Abhay Jha, Vibhav Gogate, Alexandra Meliou, Dan Suciu
• Predictive State Temporal Difference Learning Byron Boots, Geoffrey J. Gordon
• Identifying Dendritic Processing Aurel A. Lazar, Yevgeniy Slutskiy
• On a Connection between Importance Sampling and the Likelihood Ratio Policy Gradient Tang Jie, Pieter Abbeel
• Functional Geometry Alignment and Localization of Brain Areas Georg Langs, Yanmei Tie, Laura Rigolo, Alexandra Golby, Polina Golland
• Multi-View Active Learning in the Non-Realizable Case Wei Wang, Zhi-Hua Zhou
• Epitome driven 3-D Diffusion Tensor image segmentation: on extracting specific structures Kamiya Motwani, Nagesh Adluru, Chris Hinrichs, Andrew Alexander, Vikas Singh
• Over-complete representations on recurrent neural networks can support persistent percepts Shaul Druckmann, Dmitri Chklovskii
• Non-Stochastic Bandit Slate Problems Satyen Kale, Lev Reyzin, Robert E. Schapire
• Large Margin Learning of Upstream Scene Understanding Models Jun Zhu, Li-jia Li, Li Fei-fei, Eric Xing
• Switched Latent Force Models for Movement Segmentation Mauricio Alvarez, Jan Peters, Neil Lawrence, Bernhard Schölkopf
• Adaptive Multi-Task Lasso: with Application to eQTL Detection Seunghak Lee, Jun Zhu, Eric Xing
• Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations Danial Lashkari, Ramesh Sridharan, Polina Golland
• Random Projection Trees Revisited Aman Dhesi, Purushottam Kar
• Joint Analysis of Time-Evolving Binary Matrices and Associated Documents Eric Wang, Dehong Liu, Jorge Silva, Lawrence Carin, David Dunson
• Discriminative Clustering by Regularized Information Maximization Andreas Krause, Pietro Perona, Ryan Gomes
• Learning to localise sounds with spiking neural networks Dan Goodman, Romain Brette
• Dynamic Infinite Relational Model for Time-varying Relational Data Analysis Katsuhiko Ishiguro, Tomoharu Iwata, Naonori Ueda, Joshua Tenenbaum
• Exact learning curves for Gaussian process regression on large random graphs Matthew Urry, Peter Sollich
• Sparse Instrumental Variables (SPIV) for Genome-Wide Studies Paul Mckeigue, Jon Krohn, Amos J. Storkey, Felix Agakov
• Natural Policy Gradient Methods with Parameter-based Exploration for Control Tasks Atsushi Miyamae, Yuichi Nagata, Isao Ono, Shigenobu Kobayashi
• Kernel Descriptors for Visual Recognition Liefeng Bo, Xiaofeng Ren, Dieter Fox
• Computing Marginal Distributions over Continuous Markov Networks for Statistical Relational Learning Matthias Broecheler, Lise Getoor
• Gaussian Process Preference Elicitation Shengbo Guo, Scott Sanner, Edwin V. Bonilla
• A Theory of Multiclass Boosting Indraneel Mukherjee, Robert E. Schapire
• Hashing Hyperplane Queries to Near Points with Applications to Large-Scale Active Learning Prateek Jain, Sudheendra Vijayanarasimhan, Kristen Grauman
• Bootstrapping Apprenticeship Learning Abdeslam Boularias, Brahim Chaib-draa
• Co-regularization Based Semi-supervised Domain Adaptation Abhishek Kumar, Avishek Saha, Hal Daume
• Structured sparsity-inducing norms through submodular functions Francis Bach
• Optimal Web-Scale Tiering as a Flow Problem Gilbert Leung, Novi Quadrianto, Kostas Tsioutsiouliklis, Alex Smola
• Slice sampling covariance hyperparameters of latent Gaussian models Iain Murray, Ryan P. Adams
• Efficient Optimization for Discriminative Latent Class Models Armand Joulin, Jean Ponce, Francis Bach
• Universal Kernels on Non-Standard Input Spaces Andreas Christmann, Ingo Steinwart
• Worst-Case Linear Discriminant Analysis Yu Zhang, Dit-Yan Yeung
• Learning Multiple Tasks with a Sparse Matrix-Normal Penalty Yi Zhang, Jeff Schneider
• Network Flow Algorithms for Structured Sparsity Julien Mairal, Rodolphe Jenatton, Francis Bach, Guillaume R. Obozinski
• Active Learning by Querying Informative and Representative Examples Sheng-jun Huang, Rong Jin, Zhi-Hua Zhou
• Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Sets Paolo Viappiani, Craig Boutilier
• Semi-Supervised Learning with Adversarially Missing Label Information Umar Syed, Ben Taskar
• Gated Softmax Classification Roland Memisevic, Christopher Zach, Marc Pollefeys, Geoffrey E. Hinton
• Estimation of Rényi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs Dávid Pál, Barnabás Póczos, Csaba Szepesvári
• Causal discovery in multiple models from different experiments Tom Claassen, Tom Heskes
• Learning Bounds for Importance Weighting Corinna Cortes, Yishay Mansour, Mehryar Mohri
• A Reduction from Apprenticeship Learning to Classification Umar Syed, Robert E. Schapire
• Extensions of Generalized Binary Search to Group Identification and Exponential Costs Gowtham Bellala, Suresh Bhavnani, Clayton Scott
• Feature Set Embedding for Incomplete Data David Grangier, Iain Melvin
• Improving Human Judgments by Decontaminating Sequential Dependencies Michael C. Mozer, Harold Pashler, Matthew Wilder, Robert V. Lindsey, Matt Jones, Michael N. Jones
• Scrambled Objects for Least-Squares Regression Odalric Maillard, Rémi Munos
• Subgraph Detection Using Eigenvector L1 Norms Benjamin Miller, Nadya Bliss, Patrick Wolfe
• Error Propagation for Approximate Policy and Value Iteration Amir-massoud Farahmand, Csaba Szepesvári, Rémi Munos
• PAC-Bayesian Model Selection for Reinforcement Learning M. Fard, Joelle Pineau
• Learning to combine foveal glimpses with a third-order Boltzmann machine Hugo Larochelle, Geoffrey E. Hinton
• Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm Nathan Srebro, Russ R. Salakhutdinov
• Worst-case bounds on the quality of max-product fixed-points Meritxell Vinyals, Jes\'us Cerquides, Alessandro Farinelli, Juan Rodríguez-aguilar
• A POMDP Extension with Belief-dependent Rewards Mauricio Araya, Olivier Buffet, Vincent Thomas, Françcois Charpillet
• Infinite Relational Modeling of Functional Connectivity in Resting State fMRI Morten Mørup, Kristoffer Madsen, Anne-marie Dogonowski, Hartwig Siebner, Lars K. Hansen
• An Alternative to Low-level-Sychrony-Based Methods for Speech Detection Javier Movellan, Paul Ruvolo
• A rational decision making framework for inhibitory control Pradeep Shenoy, Angela J. Yu, Rajesh PN Rao
• Policy gradients in linearly-solvable MDPs Emanuel Todorov
• Sufficient Conditions for Generating Group Level Sparsity in a Robust Minimax Framework Hongbo Zhou, Qiang Cheng
• Learning concept graphs from text with stick-breaking priors America Chambers, Padhraic Smyth, Mark Steyvers
• Fast Large-scale Mixture Modeling with Component-specific Data Partitions Bo Thiesson, Chong Wang
• Improvements to the Sequence Memoizer Jan Gasthaus, Yee Teh
• Simultaneous Object Detection and Ranking with Weak Supervision Matthew Blaschko, Andrea Vedaldi, Andrew Zisserman
• Generating more realistic images using gated MRF's Marc'aurelio Ranzato, Volodymyr Mnih, Geoffrey E. Hinton
• Efficient Minimization of Decomposable Submodular Functions Peter Stobbe, Andreas Krause
• Implicit Differentiation by Perturbation Justin Domke
• The Maximal Causes of Natural Scenes are Edge Filters Jose Puertas, Joerg Bornschein, Jörg Lücke
• Regularized estimation of image statistics by Score Matching Durk P. Kingma, Yann Cun
• Identifying Patients at Risk of Major Adverse Cardiovascular Events Using Symbolic Mismatch Zeeshan Syed, John Guttag
• Movement extraction by detecting dynamics switches and repetitions Silvia Chiappa, Jan Peters
• Exact inference and learning for cumulative distribution functions on loopy graphs Nebojsa Jojic, Chris Meek, Jim Huang
• Spectral Regularization for Support Estimation Ernesto Vito, Lorenzo Rosasco, Alessandro Toigo
• Online Learning for Latent Dirichlet Allocation Matthew Hoffman, Francis Bach, David Blei
• Random Projections for $k$-means Clustering Christos Boutsidis, Anastasios Zouzias, Petros Drineas
• Inference and communication in the game of Password Yang Xu, Charles Kemp
• Smoothness, Low Noise and Fast Rates Nathan Srebro, Karthik Sridharan, Ambuj Tewari
• Energy Disaggregation via Discriminative Sparse Coding J. Kolter, Siddharth Batra, Andrew Ng
• Random Conic Pursuit for Semidefinite Programming Ariel Kleiner, Ali Rahimi, Michael Jordan
• Deterministic Single-Pass Algorithm for LDA Issei Sato, Kenichi Kurihara, Hiroshi Nakagawa
• A Bayesian Framework for Figure-Ground Interpretation Vicky Froyen, Jacob Feldman, Manish Singh
• Online Markov Decision Processes under Bandit Feedback Gergely Neu, Andras Antos, András György, Csaba Szepesvári
• A Log-Domain Implementation of the Diffusion Network in Very Large Scale Integration Yi-da Wu, Shi-jie Lin, Hsin Chen
• SpikeAnts, a spiking neuron network modelling the emergence of organization in a complex system Sylvain Chevallier, Hél\`ene Paugam-moisy, Michele Sebag
• Permutation Complexity Bound on Out-Sample Error Malik Magdon-Ismail
• Fast global convergence rates of gradient methods for high-dimensional statistical recovery Alekh Agarwal, Sahand Negahban, Martin J. Wainwright
• Attractor Dynamics with Synaptic Depression K. Wong, He Wang, Si Wu, Chi Fung
• Layer-wise analysis of deep networks with Gaussian kernels Grégoire Montavon, Klaus-Robert Müller, Mikio Braun
• Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake Stefan Harmeling, Hirsch Michael, Bernhard Schölkopf
• Sodium entry efficiency during action potentials: A novel single-parameter family of Hodgkin-Huxley models Anand Singh, Renaud Jolivet, Pierre Magistretti, Bruno Weber
• Global Analytic Solution for Variational Bayesian Matrix Factorization Shinichi Nakajima, Masashi Sugiyama, Ryota Tomioka
• Improving the Asymptotic Performance of Markov Chain Monte-Carlo by Inserting Vortices Yi Sun, Jürgen Schmidhuber, Faustino Gomez
• Linear Complementarity for Regularized Policy Evaluation and Improvement Jeffrey Johns, Christopher Painter-wakefield, Ronald Parr
• Inter-time segment information sharing for non-homogeneous dynamic Bayesian networks Dirk Husmeier, Frank Dondelinger, Sophie Lebre
• Graph-Valued Regression Han Liu, Xi Chen, Larry Wasserman, John Lafferty
• Probabilistic Multi-Task Feature Selection Yu Zhang, Dit-Yan Yeung, Qian Xu
• Penalized Principal Component Regression on Graphs for Analysis of Subnetworks Ali Shojaie, George Michailidis
• Bayesian Action-Graph Games Albert Jiang, Kevin Leyton-brown
• A Family of Penalty Functions for Structured Sparsity Jean Morales, Charles Micchelli, Massimiliano Pontil
• Spike timing-dependent plasticity as dynamic filter Joscha Schmiedt, Christian Albers, Klaus Pawelzik
• The Neural Costs of Optimal Control Samuel Gershman, Robert Wilson
• Sample Complexity of Testing the Manifold Hypothesis Hariharan Narayanan, Sanjoy Mitter
• A biologically plausible network for the computation of orientation dominance Kritika Muralidharan, Nuno Vasconcelos
• A Primal-Dual Algorithm for Group Sparse Regularization with Overlapping Groups Sofia Mosci, Silvia Villa, Alessandro Verri, Lorenzo Rosasco
• Heavy-Tailed Process Priors for Selective Shrinkage Fabian L. Wauthier, Michael Jordan
• A New Probabilistic Model for Rank Aggregation Tao Qin, Xiubo Geng, Tie-yan Liu
• Learning Networks of Stochastic Differential Equations José Pereira, Morteza Ibrahimi, Andrea Montanari
• Predictive Subspace Learning for Multi-view Data: a Large Margin Approach Ning Chen, Jun Zhu, Eric Xing
• A Bayesian Approach to Concept Drift Stephen Bach, Mark Maloof
• Multivariate Dyadic Regression Trees for Sparse Learning Problems Han Liu, Xi Chen
• A novel family of non-parametric cumulative based divergences for point processes Sohan Seth, Park Il, Austin Brockmeier, Mulugeta Semework, John Choi, Joseph Francis, Jose Principe
• Sidestepping Intractable Inference with Structured Ensemble Cascades David Weiss, Benjamin Sapp, Ben Taskar
• Cross Species Expression Analysis using a Dirichlet Process Mixture Model with Latent Matchings Ziv Bar-joseph, Hai-son Le
• t-logistic regression Nan Ding, S.v.n. Vishwanathan
• Occlusion Detection and Motion Estimation with Convex Optimization Alper Ayvaci, Michalis Raptis, Stefano Soatto
• Exploiting weakly-labeled Web images to improve object classification: a domain adaptation approach Alessandro Bergamo, Lorenzo Torresani
• Predicting Execution Time of Computer Programs Using Sparse Polynomial Regression Ling Huang, Jinzhu Jia, Bin Yu, Byung-gon Chun, Petros Maniatis, Mayur Naik
• Humans Learn Using Manifolds, Reluctantly Tim Rogers, Chuck Kalish, Joseph Harrison, Jerry Zhu, Bryan Gibson
• Sphere Embedding: An Application to Part-of-Speech Induction Yariv Maron, Michael Lamar, Elie Bienenstock
• Tight Sample Complexity of Large-Margin Learning Sivan Sabato, Nathan Srebro, Naftali Tishby
• Minimum Average Cost Clustering Kiyohito Nagano, Yoshinobu Kawahara, Satoru Iwata
• Online Classification with Specificity Constraints Andrey Bernstein, Shie Mannor, Nahum Shimkin
• Construction of Dependent Dirichlet Processes based on Poisson Processes Dahua Lin, Eric Grimson, John Fisher
• Practical Large-Scale Optimization for Max-norm Regularization Jason D. Lee, Ben Recht, Nathan Srebro, Joel Tropp, Russ R. Salakhutdinov
• Deciphering subsampled data: adaptive compressive sampling as a principle of brain communication Guy Isely, Christopher Hillar, Fritz Sommer
• Learning Convolutional Feature Hierarchies for Visual Recognition Koray Kavukcuoglu, Pierre Sermanet, Y-lan Boureau, Karol Gregor, Michael Mathieu, Yann Cun
• Multiple Kernel Learning and the SMO Algorithm Zhaonan Sun, Nawanol Ampornpunt, Manik Varma, S.v.n. Vishwanathan
• Segmentation as Maximum-Weight Independent Set William Brendel, Sinisa Todorovic
• Static Analysis of Binary Executables Using Structural SVMs Nikos Karampatziakis
• Factorized Latent Spaces with Structured Sparsity Yangqing Jia, Mathieu Salzmann, Trevor Darrell
• A unified model of short-range and long-range motion perception Shuang Wu, Xuming He, Hongjing Lu, Alan L. Yuille
• Structural epitome: a way to summarize one’s visual experience Nebojsa Jojic, Alessandro Perina, Vittorio Murino
• Tree-Structured Stick Breaking for Hierarchical Data Zoubin Ghahramani, Michael Jordan, Ryan P. Adams
• LSTD with Random Projections Mohammad Ghavamzadeh, Alessandro Lazaric, Odalric Maillard, Rémi Munos
• Feature Construction for Inverse Reinforcement Learning Sergey Levine, Zoran Popovic, Vladlen Koltun
• An analysis on negative curvature induced by singularity in multi-layer neural-network learning Eiji Mizutani, Stuart Dreyfus
• Joint Cascade Optimization Using A Product Of Boosted Classifiers Leonidas Lefakis, Francois Fleuret
• Parallelized Stochastic Gradient Descent Martin Zinkevich, Markus Weimer, Lihong Li, Alex Smola
• Shadow Dirichlet for Restricted Probability Modeling Bela Frigyik, Maya Gupta, Yihua Chen
• MAP estimation in Binary MRFs via Bipartite Multi-cuts Sashank J. Reddi, Sunita Sarawagi, Sundar Vishwanathan
• Approximate Inference by Compilation to Arithmetic Circuits Daniel Lowd, Pedro Domingos
• Random Walk Approach to Regret Minimization Hariharan Narayanan, Alexander Rakhlin
• Unsupervised Kernel Dimension Reduction Meihong Wang, Fei Sha, Michael Jordan
• Multitask Learning without Label Correspondences Novi Quadrianto, James Petterson, Tibério Caetano, Alex Smola, S.v.n. Vishwanathan
• CUR from a Sparse Optimization Viewpoint Jacob Bien, Ya Xu, Michael W. Mahoney
• Robust Clustering as Ensembles of Affinity Relations Hairong Liu, Longin Latecki, Shuicheng Yan
• Optimal learning rates for Kernel Conjugate Gradient regression Gilles Blanchard, Nicole Krämer
• Rates of convergence for the cluster tree Kamalika Chaudhuri, Sanjoy Dasgupta
• Throttling Poisson Processes Uwe Dick, Peter Haider, Thomas Vanck, Michael Brückner, Tobias Scheffer
• An Approximate Inference Approach to Temporal Optimization in Optimal Control Konrad Rawlik, Marc Toussaint, Sethu Vijayakumar
• Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine George Dahl, Marc'aurelio Ranzato, Abdel-rahman Mohamed, Geoffrey E. Hinton
• Sparse Coding for Learning Interpretable Spatio-Temporal Primitives Taehwan Kim, Gregory Shakhnarovich, Raquel Urtasun
• Block Variable Selection in Multivariate Regression and High-dimensional Causal Inference Vikas Sindhwani, Aurelie C. Lozano
• Reverse Multi-Label Learning James Petterson, Tibério Caetano
• Efficient Relational Learning with Hidden Variable Detection Ni Lao, Jun Zhu, Liu Liu, Yandong Liu, William W. Cohen
• Learning from Logged Implicit Exploration Data Alex Strehl, John Langford, Lihong Li, Sham M. Kakade
• Parametric Bandits: The Generalized Linear Case Sarah Filippi, Olivier Cappe, Aurélien Garivier, Csaba Szepesvári
• Basis Construction from Power Series Expansions of Value Functions Sridhar Mahadevan, Bo Liu
• A Novel Kernel for Learning a Neuron Model from Spike Train Data Nicholas Fisher, Arunava Banerjee
• Nonparametric Bayesian Policy Priors for Reinforcement Learning Finale Doshi-velez, David Wingate, Nicholas Roy, Joshua Tenenbaum
• On the Theory of Learnining with Privileged Information Dmitry Pechyony, Vladimir Vapnik
• Accounting for network effects in neuronal responses using L1 regularized point process models Ryan Kelly, Matthew Smith, Robert Kass, Tai Lee
• Probabilistic latent variable models for distinguishing between cause and effect Oliver Stegle, Dominik Janzing, Kun Zhang, Joris M. Mooij, Bernhard Schölkopf
• Two-Layer Generalization Analysis for Ranking Using Rademacher Average Wei Chen, Tie-yan Liu, Zhi-ming Ma
• Learning from Candidate Labeling Sets Jie Luo, Francesco Orabona
• Direct Loss Minimization for Structured Prediction Tamir Hazan, Joseph Keshet, David McAllester
• Variational Inference over Combinatorial Spaces Alexandre Bouchard-côté, Michael Jordan
• Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development Diane Hu, Laurens Maaten, Youngmin Cho, Sorin Lerner, Lawrence Saul
• Avoiding False Positive in Multi-Instance Learning Yanjun Han, Qing Tao, Jue Wang
• Individualized ROI Optimization via Maximization of Group-wise Consistency of Structural and Functional Profiles Kaiming Li, Lei Guo, Carlos Faraco, Dajiang Zhu, Fan Deng, Tuo Zhang, Xi Jiang, Degang Zhang, Hanbo Chen, Xintao Hu, Steve Miller, Tianming Liu
• On the Convexity of Latent Social Network Inference Seth Myers, Jure Leskovec
• Global seismic monitoring as probabilistic inference Nimar Arora, Stuart J. Russell, Paul Kidwell, Erik Sudderth
• Gaussian sampling by local perturbations George Papandreou, Alan L. Yuille
• Hallucinations in Charles Bonnet Syndrome Induced by Homeostasis: a Deep Boltzmann Machine Model Peggy Series, David Reichert, Amos J. Storkey
• Moreau-Yosida Regularization for Grouped Tree Structure Learning Jun Liu, Jieping Ye
• Pose-Sensitive Embedding by Nonlinear NCA Regression Graham W. Taylor, Rob Fergus, George Williams, Ian Spiro, Christoph Bregler
• Synergies in learning words and their referents Mark Johnson, Katherine Demuth, Bevan Jones, Michael Black
• Approximate inference in continuous time Gaussian-Jump processes Manfred Opper, Andreas Ruttor, Guido Sanguinetti
• Empirical Bernstein Inequalities for U-Statistics Thomas Peel, Sandrine Anthoine, Liva Ralaivola
• Active Estimation of F-Measures Christoph Sawade, Niels Landwehr, Tobias Scheffer
• Inductive Regularized Learning of Kernel Functions Prateek Jain, Brian Kulis, Inderjit Dhillon
• Active Learning Applied to Patient-Adaptive Heartbeat Classification Jenna Wiens, John Guttag
• Large-Scale Matrix Factorization with Missing Data under Additional Constraints Kaushik Mitra, Sameer Sheorey, Rama Chellappa
• Probabilistic Deterministic Infinite Automata David Pfau, Nicholas Bartlett, Frank Wood
• Brain covariance selection: better individual functional connectivity models using population prior Gael Varoquaux, Alexandre Gramfort, Jean-baptiste Poline, Bertrand Thirion
• Word Features for Latent Dirichlet Allocation James Petterson, Wray Buntine, Shravan Narayanamurthy, Tibério Caetano, Alex Smola
• A Primal-Dual Message-Passing Algorithm for Approximated Large Scale Structured Prediction Tamir Hazan, Raquel Urtasun
• Efficient algorithms for learning kernels from multiple similarity matrices with general convex loss functions Achintya Kundu, Vikram Tankasali, Chiranjib Bhattacharyya, Aharon Ben-tal
• Online Learning: Random Averages, Combinatorial Parameters, and Learnability Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
• Variable margin losses for classifier design Hamed Masnadi-shirazi, Nuno Vasconcelos
• Nonparametric Density Estimation for Stochastic Optimization with an Observable State Variable Lauren Hannah, Warren Powell, David Blei
• Mixture of time-warped trajectory models for movement decoding Elaine Corbett, Eric Perreault, Konrad Koerding
• A Discriminative Latent Model of Image Region and Object Tag Correspondence Yang Wang, Greg Mori
• Lower Bounds on Rate of Convergence of Cutting Plane Methods Xinhua Zhang, Ankan Saha, S.v.n. Vishwanathan
• Self-Paced Learning for Latent Variable Models M. Kumar, Benjamin Packer, Daphne Koller
• Learning Efficient Markov Networks Vibhav Gogate, William Webb, Pedro Domingos
• Multi-Stage Dantzig Selector Ji Liu, Peter Wonka, Jieping Ye
• Batch Bayesian Optimization via Simulation Matching Javad Azimi, Alan Fern, Xiaoli Fern
• Inference with Multivariate Heavy-Tails in Linear Models Danny Bickson, Carlos Guestrin
• Towards Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models Congcong Li, Adarsh Kowdle, Ashutosh Saxena, Tsuhan Chen
• Learning the context of a category Dan Navarro
• Learning via Gaussian Herding Koby Crammer, Daniel Lee
• Monte-Carlo Planning in Large POMDPs David Silver, Joel Veness
• Spatial and anatomical regularization of SVM for brain image analysis Remi Cuingnet, Marie Chupin, Habib Benali, Olivier Colliot
• Divisive Normalization: Justification and Effectiveness as Efficient Coding Transform Siwei Lyu
• Rescaling, thinning or complementing? On goodness-of-fit procedures for point process models and Generalized Linear Models Felipe Gerhard, Wulfram Gerstner
• Active Instance Sampling via Matrix Partition Yuhong Guo
• Functional form of motion priors in human motion perception Hongjing Lu, Tungyou Lin, Alan Lee, Luminita Vese, Alan L. Yuille
• The LASSO risk: asymptotic results and real world examples Mohsen Bayati, José Pereira, Andrea Montanari
• Layered image motion with explicit occlusions, temporal consistency, and depth ordering Deqing Sun, Erik Sudderth, Michael Black
• Towards Property-Based Classification of Clustering Paradigms Margareta Ackerman, Shai Ben-David, David Loker
• Implicit encoding of prior probabilities in optimal neural populations Deep Ganguli, Eero Simoncelli
• Distributed Dual Averaging In Networks Alekh Agarwal, Martin J. Wainwright, John C. Duchi
• Probabilistic Inference and Differential Privacy Oliver Williams, Frank Mcsherry
• Copula Processes Andrew G. Wilson, Zoubin Ghahramani
• Learning invariant features using the Transformed Indian Buffet Process Joseph Austerweil, Thomas Griffiths
• An Inverse Power Method for Nonlinear Eigenproblems with Applications in 1-Spectral Clustering and Sparse PCA Matthias Hein, Thomas Bühler
• Fast detection of multiple change-points shared by many signals using group LARS Jean-philippe Vert, Kevin Bleakley
• Learning To Count Objects in Images Victor Lempitsky, Andrew Zisserman
• Robust PCA via Outlier Pursuit Huan Xu, Constantine Caramanis, Sujay Sanghavi
• Multi-label Multiple Kernel Learning by Stochastic Approximation: Application to Visual Object Recognition Serhat Bucak, Rong Jin, Anil Jain
• Learning sparse dynamic linear systems using stable spline kernels and exponential hyperpriors Alessandro Chiuso, Gianluigi Pillonetto