Book
Advances in Neural Information Processing Systems 24 (NIPS 2011)
Edited by:
J. Shawe-Taylor and R. Zemel and P. Bartlett and F. Pereira and K.Q. Weinberger
- Emergence of Multiplication in a Biophysical Model of a Wide-Field Visual Neuron for Computing Object Approaches: Dynamics, Peaks, & Fits Matthias Keil
- Action-Gap Phenomenon in Reinforcement Learning Amir-massoud Farahmand
- Learning Higher-Order Graph Structure with Features by Structure Penalty Shilin Ding, Grace Wahba, Jerry Zhu
- Efficient Methods for Overlapping Group Lasso Lei Yuan, Jun Liu, Jieping Ye
- Priors over Recurrent Continuous Time Processes Ardavan Saeedi, Alexandre Bouchard-côté
- The Kernel Beta Process Lu Ren, Yingjian Wang, Lawrence Carin, David Dunson
- Lower Bounds for Passive and Active Learning Maxim Raginsky, Alexander Rakhlin
- k-NN Regression Adapts to Local Intrinsic Dimension Samory Kpotufe
- Variational Learning for Recurrent Spiking Networks Danilo Rezende, Daan Wierstra, Wulfram Gerstner
- Rapid Deformable Object Detection using Dual-Tree Branch-and-Bound Iasonas Kokkinos
- Probabilistic Modeling of Dependencies Among Visual Short-Term Memory Representations Emin Orhan, Robert Jacobs
- Bayesian Bias Mitigation for Crowdsourcing Fabian L. Wauthier, Michael Jordan
- Phase transition in the family of p-resistances Morteza Alamgir, Ulrike Luxburg
- On the Analysis of Multi-Channel Neural Spike Data Bo Chen, David Carlson, Lawrence Carin
- Hashing Algorithms for Large-Scale Learning Ping Li, Anshumali Shrivastava, Joshua Moore, Arnd König
- Active learning of neural response functions with Gaussian processes Mijung Park, Greg Horwitz, Jonathan Pillow
- Nonstandard Interpretations of Probabilistic Programs for Efficient Inference David Wingate, Noah Goodman, Andreas Stuhlmueller, Jeffrey Siskind
- The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers Luca Oneto, Davide Anguita, Alessandro Ghio, Sandro Ridella
- Regularized Laplacian Estimation and Fast Eigenvector Approximation Patrick Perry, Michael W. Mahoney
- The Doubly Correlated Nonparametric Topic Model Dae Kim, Erik Sudderth
- Generalized Lasso based Approximation of Sparse Coding for Visual Recognition Nobuyuki Morioka, Shin'ichi Satoh
- SpaRCS: Recovering low-rank and sparse matrices from compressive measurements Andrew Waters, Aswin Sankaranarayanan, Richard Baraniuk
- Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors Chun-Nam Yu, Russell Greiner, Hsiu-Chin Lin, Vickie Baracos
- Prediction strategies without loss Michael Kapralov, Rina Panigrahy
- Maximum Margin Multi-Instance Learning Hua Wang, Heng Huang, Farhad Kamangar, Feiping Nie, Chris Ding
- Anatomically Constrained Decoding of Finger Flexion from Electrocorticographic Signals Zuoguan Wang, Gerwin Schalk, Qiang Ji
- Efficient coding of natural images with a population of noisy Linear-Nonlinear neurons Yan Karklin, Eero Simoncelli
- Multilinear Subspace Regression: An Orthogonal Tensor Decomposition Approach Qibin Zhao, Cesar F. Caiafa, Danilo Mandic, Liqing Zhang, Tonio Ball, Andreas Schulze-bonhage, Andrzej Cichocki
- Large-Scale Sparse Principal Component Analysis with Application to Text Data Youwei Zhang, Laurent Ghaoui
- Nearest Neighbor based Greedy Coordinate Descent Inderjit Dhillon, Pradeep Ravikumar, Ambuj Tewari
- Convergent Bounds on the Euclidean Distance Yoonho Hwang, Hee-kap Ahn
- Video Annotation and Tracking with Active Learning Carl Vondrick, Deva Ramanan
- PiCoDes: Learning a Compact Code for Novel-Category Recognition Alessandro Bergamo, Lorenzo Torresani, Andrew Fitzgibbon
- Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation Zhouchen Lin, Risheng Liu, Zhixun Su
- Sparse Filtering Jiquan Ngiam, Zhenghao Chen, Sonia Bhaskar, Pang Koh, Andrew Ng
- Beyond Spectral Clustering - Tight Relaxations of Balanced Graph Cuts Matthias Hein, Simon Setzer
- Why The Brain Separates Face Recognition From Object Recognition Joel Z. Leibo, Jim Mutch, Tomaso Poggio
- Analytical Results for the Error in Filtering of Gaussian Processes Alex K. Susemihl, Ron Meir, Manfred Opper
- Active Learning with a Drifting Distribution Liu Yang
- Evaluating the inverse decision-making approach to preference learning Alan Jern, Christopher Lucas, Charles Kemp
- Policy Gradient Coagent Networks Philip S. Thomas
- Periodic Finite State Controllers for Efficient POMDP and DEC-POMDP Planning Joni Pajarinen, Jaakko Peltonen
- Spectral Methods for Learning Multivariate Latent Tree Structure Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song, Tong Zhang
- Query-Aware MCMC Michael Wick, Andrew McCallum
- Hogwild!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent Benjamin Recht, Christopher Re, Stephen Wright, Feng Niu
- ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning Quoc Le, Alexandre Karpenko, Jiquan Ngiam, Andrew Ng
- Sparse Recovery with Brownian Sensing Alexandra Carpentier, Odalric-ambrym Maillard, Rémi Munos
- Learning Anchor Planes for Classification Ziming Zhang, Lubor Ladicky, Philip Torr, Amir Saffari
- Ranking annotators for crowdsourced labeling tasks Vikas C. Raykar, Shipeng Yu
- Metric Learning with Multiple Kernels Jun Wang, Huyen T., Adam Woznica, Alexandros Kalousis
- A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm Julie Dethier, Paul Nuyujukian, Chris Eliasmith, Terrence Stewart, Shauki Elasaad, Krishna V. Shenoy, Kwabena A. Boahen
- Understanding the Intrinsic Memorability of Images Phillip Isola, Devi Parikh, Antonio Torralba, Aude Oliva
- Two is better than one: distinct roles for familiarity and recollection in retrieving palimpsest memories Cristina Savin, Peter Dayan, Máté Lengyel
- Automated Refinement of Bayes Networks' Parameters based on Test Ordering Constraints Omar Khan, Pascal Poupart, John-mark Agosta
- Structure Learning for Optimization Shulin Yang, Ali Rahimi
- Multiclass Boosting: Theory and Algorithms Mohammad Saberian, Nuno Vasconcelos
- Composite Multiclass Losses Elodie Vernet, Mark D. Reid, Robert C. Williamson
- Scalable Training of Mixture Models via Coresets Dan Feldman, Matthew Faulkner, Andreas Krause
- Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance Carsten Rother, Martin Kiefel, Lumin Zhang, Bernhard Schölkopf, Peter Gehler
- Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation Cho-jui Hsieh, Inderjit Dhillon, Pradeep Ravikumar, Mátyás Sustik
- Testing a Bayesian Measure of Representativeness Using a Large Image Database Joshua T. Abbott, Katherine A. Heller, Zoubin Ghahramani, Thomas Griffiths
- Dynamical segmentation of single trials from population neural data Biljana Petreska, Byron M. Yu, John P. Cunningham, Gopal Santhanam, Stephen Ryu, Krishna V. Shenoy, Maneesh Sahani
- Approximating Semidefinite Programs in Sublinear Time Dan Garber, Elad Hazan
- Active Classification based on Value of Classifier Tianshi Gao, Daphne Koller
- Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression Sham M. Kakade, Varun Kanade, Ohad Shamir, Adam Kalai
- Co-regularized Multi-view Spectral Clustering Abhishek Kumar, Piyush Rai, Hal Daume
- A concave regularization technique for sparse mixture models Martin Larsson, Johan Ugander
- Image Parsing with Stochastic Scene Grammar Yibiao Zhao, Song-chun Zhu
- Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection Richard Socher, Eric Huang, Jeffrey Pennin, Christopher D. Manning, Andrew Ng
- Trace Lasso: a trace norm regularization for correlated designs Edouard Grave, Guillaume R. Obozinski, Francis Bach
- Linear Submodular Bandits and their Application to Diversified Retrieval Yisong Yue, Carlos Guestrin
- Learning Eigenvectors for Free Wouter M. Koolen, Wojciech Kotlowski, Manfred K. K. Warmuth
- Neural Reconstruction with Approximate Message Passing (NeuRAMP) Alyson K. Fletcher, Sundeep Rangan, Lav R. Varshney, Aniruddha Bhargava
- Bayesian Partitioning of Large-Scale Distance Data David Adametz, Volker Roth
- Dimensionality Reduction Using the Sparse Linear Model Ioannis Gkioulekas, Todd Zickler
- RTRMC: A Riemannian trust-region method for low-rank matrix completion Nicolas Boumal, Pierre-antoine Absil
- Complexity of Inference in Latent Dirichlet Allocation David Sontag, Dan Roy
- A Denoising View of Matrix Completion Weiran Wang, Miguel Carreira-Perpiñán, Zhengdong Lu
- Generalization Bounds and Consistency for Latent Structural Probit and Ramp Loss Joseph Keshet, David McAllester
- Budgeted Optimization with Concurrent Stochastic-Duration Experiments Javad Azimi, Alan Fern, Xiaoli Fern
- Data Skeletonization via Reeb Graphs Xiaoyin Ge, Issam Safa, Mikhail Belkin, Yusu Wang
- MAP Inference for Bayesian Inverse Reinforcement Learning Jaedeug Choi, Kee-eung Kim
- Learning Sparse Representations of High Dimensional Data on Large Scale Dictionaries Zhen Xiang, Hao Xu, Peter J. Ramadge
- Efficient Online Learning via Randomized Rounding Nicolò Cesa-bianchi, Ohad Shamir
- Spatial distance dependent Chinese restaurant processes for image segmentation Soumya Ghosh, Andrei Ungureanu, Erik Sudderth, David Blei
- History distribution matching method for predicting effectiveness of HIV combination therapies Jasmina Bogojeska
- Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning Eric Moulines, Francis Bach
- A Non-Parametric Approach to Dynamic Programming Oliver Kroemer, Jan Peters
- Extracting Speaker-Specific Information with a Regularized Siamese Deep Network Ke Chen, Ahmad Salman
- Variance Penalizing AdaBoost Pannagadatta Shivaswamy, Tony Jebara
- Autonomous Learning of Action Models for Planning Neville Mehta, Prasad Tadepalli, Alan Fern
- Hierarchically Supervised Latent Dirichlet Allocation Adler Perotte, Frank Wood, Noemie Elhadad, Nicholas Bartlett
- Agnostic Selective Classification Yair Wiener, Ran El-Yaniv
- Additive Gaussian Processes David K. Duvenaud, Hannes Nickisch, Carl Rasmussen
- A Collaborative Mechanism for Crowdsourcing Prediction Problems Jacob D. Abernethy, Rafael Frongillo
- Sparse Bayesian Multi-Task Learning Shengbo Guo, Onno Zoeter, Cédric Archambeau
- Orthogonal Matching Pursuit with Replacement Prateek Jain, Ambuj Tewari, Inderjit Dhillon
- High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions Animashree Anandkumar, Vincent Tan, Alan Willsky
- Optimal learning rates for least squares SVMs using Gaussian kernels Mona Eberts, Ingo Steinwart
- Fast and Accurate k-means For Large Datasets Michael Shindler, Alex Wong, Adam Meyerson
- Message-Passing for Approximate MAP Inference with Latent Variables Jiarong Jiang, Piyush Rai, Hal Daume
- A More Powerful Two-Sample Test in High Dimensions using Random Projection Miles Lopes, Laurent Jacob, Martin J. Wainwright
- Kernel Bayes' Rule Kenji Fukumizu, Le Song, Arthur Gretton
- ShareBoost: Efficient multiclass learning with feature sharing Shai Shalev-shwartz, Yonatan Wexler, Amnon Shashua
- Heavy-tailed Distances for Gradient Based Image Descriptors Yangqing Jia, Trevor Darrell
- An Application of Tree-Structured Expectation Propagation for Channel Decoding Pablo Olmos, Luis Salamanca, Juan Fuentes, Fernando Pérez-Cruz
- PAC-Bayesian Analysis of Contextual Bandits Yevgeny Seldin, Peter Auer, John Shawe-taylor, Ronald Ortner, François Laviolette
- Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition Jia Deng, Sanjeev Satheesh, Alexander Berg, Fei Li
- Divide-and-Conquer Matrix Factorization Lester Mackey, Michael Jordan, Ameet Talwalkar
- Non-conjugate Variational Message Passing for Multinomial and Binary Regression David Knowles, Tom Minka
- Im2Text: Describing Images Using 1 Million Captioned Photographs Vicente Ordonez, Girish Kulkarni, Tamara Berg
- Modelling Genetic Variations using Fragmentation-Coagulation Processes Yee Teh, Charles Blundell, Lloyd Elliott
- Uniqueness of Belief Propagation on Signed Graphs Yusuke Watanabe
- Improving Topic Coherence with Regularized Topic Models David Newman, Edwin V. Bonilla, Wray Buntine
- Beating SGD: Learning SVMs in Sublinear Time Elad Hazan, Tomer Koren, Nati Srebro
- Inferring spike-timing-dependent plasticity from spike train data Ian Stevenson, Konrad Koerding
- Bayesian Spike-Triggered Covariance Analysis Il Memming Park, Jonathan Pillow
- Adaptive Hedge Tim Erven, Wouter M. Koolen, Steven Rooij, Peter Grünwald
- Matrix Completion for Multi-label Image Classification Ricardo Cabral, Fernando Torre, Joao P. Costeira, Alexandre Bernardino
- Continuous-Time Regression Models for Longitudinal Networks Duy Vu, David Hunter, Padhraic Smyth, Arthur Asuncion
- Stochastic convex optimization with bandit feedback Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin
- Online Learning: Stochastic, Constrained, and Smoothed Adversaries Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
- Similarity-based Learning via Data Driven Embeddings Purushottam Kar, Prateek Jain
- Maximum Margin Multi-Label Structured Prediction Christoph H. Lampert
- Active Ranking using Pairwise Comparisons Kevin G. Jamieson, Robert Nowak
- Selecting Receptive Fields in Deep Networks Adam Coates, Andrew Ng
- Learning Auto-regressive Models from Sequence and Non-sequence Data Tzu-kuo Huang, Jeff Schneider
- Multi-View Learning of Word Embeddings via CCA Paramveer Dhillon, Dean P. Foster, Lyle Ungar
- Projection onto A Nonnegative Max-Heap Jun Liu, Liang Sun, Jieping Ye
- Learning to Learn with Compound HD Models Antonio Torralba, Joshua Tenenbaum, Russ R. Salakhutdinov
- Object Detection with Grammar Models Ross Girshick, Pedro Felzenszwalb, David McAllester
- Inductive reasoning about chimeric creatures Charles Kemp
- Empirical models of spiking in neural populations Jakob H. Macke, Lars Buesing, John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani
- An Exact Algorithm for F-Measure Maximization Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier
- Exploiting spatial overlap to efficiently compute appearance distances between image windows Bogdan Alexe, Viviana Petrescu, Vittorio Ferrari
- Signal Estimation Under Random Time-Warpings and Nonlinear Signal Alignment Sebastian Kurtek, Anuj Srivastava, Wei Wu
- Multi-armed bandits on implicit metric spaces Aleksandrs Slivkins
- Predicting response time and error rates in visual search Bo Chen, Vidhya Navalpakkam, Pietro Perona
- Sequence learning with hidden units in spiking neural networks Johanni Brea, Walter Senn, Jean-pascal Pfister
- Convergent Fitted Value Iteration with Linear Function Approximation Daniel Lizotte
- Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint Kenji Fukumizu, Gert Lanckriet, Bharath K. Sriperumbudur
- Predicting Dynamic Difficulty Olana Missura, Thomas Gärtner
- Optimistic Optimization of a Deterministic Function without the Knowledge of its Smoothness Rémi Munos
- Robust Multi-Class Gaussian Process Classification Daniel Hernández-lobato, Jose Hernández-lobato, Pierre Dupont
- Practical Variational Inference for Neural Networks Alex Graves
- Penalty Decomposition Methods for Rank Minimization Yong Zhang, Zhaosong Lu
- Accelerated Adaptive Markov Chain for Partition Function Computation Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman
- On Strategy Stitching in Large Extensive Form Multiplayer Games Richard Gibson, Duane Szafron
- Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity Nir Ailon
- Nonnegative dictionary learning in the exponential noise model for adaptive music signal representation Onur Dikmen, Cédric Févotte
- From Stochastic Nonlinear Integrate-and-Fire to Generalized Linear Models Skander Mensi, Richard Naud, Wulfram Gerstner
- Generalised Coupled Tensor Factorisation Kenan Yılmaz, Ali Cemgil, Umut Simsekli
- Prismatic Algorithm for Discrete D.C. Programming Problem Yoshinobu Kawahara, Takashi Washio
- On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic Inference Guy Broeck
- Analysis and Improvement of Policy Gradient Estimation Tingting Zhao, Hirotaka Hachiya, Gang Niu, Masashi Sugiyama
- On Tracking The Partition Function Guillaume Desjardins, Yoshua Bengio, Aaron C. Courville
- Portmanteau Vocabularies for Multi-Cue Image Representation Fahad Khan, Joost Weijer, Andrew Bagdanov, Maria Vanrell
- Algorithms for Hyper-Parameter Optimization James Bergstra, Rémi Bardenet, Yoshua Bengio, Balázs Kégl
- Finite Time Analysis of Stratified Sampling for Monte Carlo Alexandra Carpentier, Rémi Munos
- Online Submodular Set Cover, Ranking, and Repeated Active Learning Andrew Guillory, Jeff A. Bilmes
- The Fast Convergence of Boosting Matus Telgarsky
- See the Tree Through the Lines: The Shazoo Algorithm Fabio Vitale, Nicolò Cesa-bianchi, Claudio Gentile, Giovanni Zappella
- t-divergence Based Approximate Inference Nan Ding, Yuan Qi, S.v.n. Vishwanathan
- Boosting with Maximum Adaptive Sampling Charles Dubout, Francois Fleuret
- Inverting Grice's Maxims to Learn Rules from Natural Language Extractions Mohammad Sorower, Janardhan Doppa, Walker Orr, Prasad Tadepalli, Thomas Dietterich, Xiaoli Fern
- Efficient anomaly detection using bipartite k-NN graphs Kumar Sricharan, Alfred Hero
- Shallow vs. Deep Sum-Product Networks Olivier Delalleau, Yoshua Bengio
- Expressive Power and Approximation Errors of Restricted Boltzmann Machines Guido F. Montufar, Johannes Rauh, Nihat Ay
- Directed Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type Operators Dominique Perrault-joncas, Marina Meila
- Information Rates and Optimal Decoding in Large Neural Populations Kamiar Rad, Liam Paninski
- Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization Mark Schmidt, Nicolas Roux, Francis Bach
- Co-Training for Domain Adaptation Minmin Chen, Kilian Q. Weinberger, John Blitzer
- Learning to Agglomerate Superpixel Hierarchies Viren Jain, Srinivas C. Turaga, K Briggman, Moritz Helmstaedter, Winfried Denk, H. Seung
- Structured Learning for Cell Tracking Xinghua Lou, Fred A. Hamprecht
- Hierarchical Topic Modeling for Analysis of Time-Evolving Personal Choices Xianxing Zhang, Lawrence Carin, David Dunson
- Selecting the State-Representation in Reinforcement Learning Odalric-ambrym Maillard, Daniil Ryabko, Rémi Munos
- A Reinforcement Learning Theory for Homeostatic Regulation Mehdi Keramati, Boris Gutkin
- Semantic Labeling of 3D Point Clouds for Indoor Scenes Hema Koppula, Abhishek Anand, Thorsten Joachims, Ashutosh Saxena
- Higher-Order Correlation Clustering for Image Segmentation Sungwoong Kim, Sebastian Nowozin, Pushmeet Kohli, Chang Yoo
- Learning large-margin halfspaces with more malicious noise Phil Long, Rocco Servedio
- The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning Marius Kloft, Gilles Blanchard
- A Global Structural EM Algorithm for a Model of Cancer Progression Ali Tofigh, Erik Sj̦lund, Mattias H̦glund, Jens Lagergren
- Infinite Latent SVM for Classification and Multi-task Learning Jun Zhu, Ning Chen, Eric Xing
- On U-processes and clustering performance Stéphan Clémençcon
- Robust Lasso with missing and grossly corrupted observations Nasser Nasrabadi, Trac Tran, Nam Nguyen
- Unifying Non-Maximum Likelihood Learning Objectives with Minimum KL Contraction Siwei Lyu
- Select and Sample - A Model of Efficient Neural Inference and Learning Jacquelyn Shelton, Abdul Sheikh, Pietro Berkes, Joerg Bornschein, Jörg Lücke
- Clustered Multi-Task Learning Via Alternating Structure Optimization Jiayu Zhou, Jianhui Chen, Jieping Ye
- On Learning Discrete Graphical Models using Greedy Methods Ali Jalali, Christopher Johnson, Pradeep Ravikumar
- Learning person-object interactions for action recognition in still images Vincent Delaitre, Josef Sivic, Ivan Laptev
- Efficient inference in matrix-variate Gaussian models with \iid observation noise Oliver Stegle, Christoph Lippert, Joris M. Mooij, Neil Lawrence, Karsten Borgwardt
- Confidence Sets for Network Structure David Choi, Patrick Wolfe, Edo M. Airoldi
- Structural equations and divisive normalization for energy-dependent component analysis Jun-ichiro Hirayama, Aapo Hyvärinen
- Gaussian Process Training with Input Noise Andrew Mchutchon, Carl Rasmussen
- On the Universality of Online Mirror Descent Nati Srebro, Karthik Sridharan, Ambuj Tewari
- Unifying Framework for Fast Learning Rate of Non-Sparse Multiple Kernel Learning Taiji Suzuki
- Speedy Q-Learning Mohammad Ghavamzadeh, Hilbert Kappen, Mohammad Azar, Rémi Munos
- High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity Po-ling Loh, Martin J. Wainwright
- Greedy Model Averaging Dong Dai, Tong Zhang
- Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation Nico Goernitz, Christian Widmer, Georg Zeller, Andre Kahles, Gunnar Rätsch, Sören Sonnenburg
- Generalized Beta Mixtures of Gaussians Artin Armagan, Merlise Clyde, David Dunson
- Variational Gaussian Process Dynamical Systems Andreas Damianou, Michalis Titsias, Neil Lawrence
- Statistical Tests for Optimization Efficiency Levi Boyles, Anoop Korattikara, Deva Ramanan, Max Welling
- Statistical Performance of Convex Tensor Decomposition Ryota Tomioka, Taiji Suzuki, Kohei Hayashi, Hisashi Kashima
- A Machine Learning Approach to Predict Chemical Reactions Matthew Kayala, Pierre Baldi
- Sparse Features for PCA-Like Linear Regression Christos Boutsidis, Petros Drineas, Malik Magdon-Ismail
- Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning Michalis Titsias, Miguel Lázaro-Gredilla
- Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability David Reichert, Peggy Series, Amos J. Storkey
- Reinforcement Learning using Kernel-Based Stochastic Factorization Andre Barreto, Doina Precup, Joelle Pineau
- Better Mini-Batch Algorithms via Accelerated Gradient Methods Andrew Cotter, Ohad Shamir, Nati Srebro, Karthik Sridharan
- Generalizing from Several Related Classification Tasks to a New Unlabeled Sample Gilles Blanchard, Gyemin Lee, Clayton Scott
- Energetically Optimal Action Potentials Martin Stemmler, Biswa Sengupta, Simon Laughlin, Jeremy Niven
- Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent Shinichi Nakajima, Masashi Sugiyama, S. Babacan
- Algorithms and hardness results for parallel large margin learning Phil Long, Rocco Servedio
- Semi-supervised Regression via Parallel Field Regularization Binbin Lin, Chiyuan Zhang, Xiaofei He
- Thinning Measurement Models and Questionnaire Design Ricardo Silva
- Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials Philipp Krähenbühl, Vladlen Koltun
- Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC Trung Pham, Tat-jun Chin, Jin Yu, David Suter
- Active dendrites: adaptation to spike-based communication Balazs Ujfalussy, Máté Lengyel
- Shaping Level Sets with Submodular Functions Francis Bach
- Probabilistic amplitude and frequency demodulation Richard Turner, Maneesh Sahani
- Multi-Bandit Best Arm Identification Victor Gabillon, Mohammad Ghavamzadeh, Alessandro Lazaric, Sébastien Bubeck
- Nonlinear Inverse Reinforcement Learning with Gaussian Processes Sergey Levine, Zoran Popovic, Vladlen Koltun
- EigenNet: A Bayesian hybrid of generative and conditional models for sparse learning Feng Yan, Yuan Qi
- Iterative Learning for Reliable Crowdsourcing Systems David Karger, Sewoong Oh, Devavrat Shah
- Minimax Localization of Structural Information in Large Noisy Matrices Mladen Kolar, Sivaraman Balakrishnan, Alessandro Rinaldo, Aarti Singh
- Crowdclustering Ryan Gomes, Peter Welinder, Andreas Krause, Pietro Perona
- Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs Armen Allahverdyan, Aram Galstyan
- Environmental statistics and the trade-off between model-based and TD learning in humans Dylan Simon, Nathaniel Daw
- A Model for Temporal Dependencies in Event Streams Asela Gunawardana, Christopher Meek, Puyang Xu
- Inferring Interaction Networks using the IBP applied to microRNA Target Prediction Hai-son Le, Ziv Bar-joseph
- Learning unbelievable probabilities Zachary Pitkow, Yashar Ahmadian, Ken Miller
- Relative Density-Ratio Estimation for Robust Distribution Comparison Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Masashi Sugiyama
- The Manifold Tangent Classifier Salah Rifai, Yann N. Dauphin, Pascal Vincent, Yoshua Bengio, Xavier Muller
- Manifold Precis: An Annealing Technique for Diverse Sampling of Manifolds Nitesh Shroff, Pavan Turaga, Rama Chellappa
- Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines Matthew Zeiler, Graham W. Taylor, Leonid Sigal, Iain Matthews, Rob Fergus
- Committing Bandits Loc Bui, Ramesh Johari, Shie Mannor
- Large-Scale Category Structure Aware Image Categorization Bin Zhao, Fei Li, Eric Xing
- On Causal Discovery with Cyclic Additive Noise Models Joris M. Mooij, Dominik Janzing, Tom Heskes, Bernhard Schölkopf
- Sparse recovery by thresholded non-negative least squares Martin Slawski, Matthias Hein
- A Two-Stage Weighting Framework for Multi-Source Domain Adaptation Qian Sun, Rita Chattopadhyay, Sethuraman Panchanathan, Jieping Ye
- Variance Reduction in Monte-Carlo Tree Search Joel Veness, Marc Lanctot, Michael Bowling
- Advice Refinement in Knowledge-Based SVMs Gautam Kunapuli, Richard Maclin, Jude Shavlik
- On fast approximate submodular minimization Stefanie Jegelka, Hui Lin, Jeff A. Bilmes
- Multiple Instance Filtering Kamil Wnuk, Stefano Soatto
- A reinterpretation of the policy oscillation phenomenon in approximate policy iteration Paul Wagner
- $\theta$-MRF: Capturing Spatial and Semantic Structure in the Parameters for Scene Understanding Congcong Li, Ashutosh Saxena, Tsuhan Chen
- Maximum Covariance Unfolding : Manifold Learning for Bimodal Data Vijay Mahadevan, Chi Wong, Jose Pereira, Tom Liu, Nuno Vasconcelos, Lawrence Saul
- Noise Thresholds for Spectral Clustering Sivaraman Balakrishnan, Min Xu, Akshay Krishnamurthy, Aarti Singh
- Kernel Embeddings of Latent Tree Graphical Models Le Song, Eric Xing, Ankur Parikh
- Learning a Distance Metric from a Network Blake Shaw, Bert Huang, Tony Jebara
- Selective Prediction of Financial Trends with Hidden Markov Models Dmitry Pidan, Ran El-Yaniv
- Blending Autonomous Exploration and Apprenticeship Learning Thomas Walsh, Daniel Hewlett, Clayton Morrison
- Transfer Learning by Borrowing Examples for Multiclass Object Detection Joseph J. Lim, Russ R. Salakhutdinov, Antonio Torralba
- A blind sparse deconvolution method for neural spike identification Chaitanya Ekanadham, Daniel Tranchina, Eero Simoncelli
- Clustering via Dirichlet Process Mixture Models for Portable Skill Discovery Scott Niekum, Andrew Barto
- Unsupervised learning models of primary cortical receptive fields and receptive field plasticity Maneesh Bhand, Ritvik Mudur, Bipin Suresh, Andrew Saxe, Andrew Ng
- Improved Algorithms for Linear Stochastic Bandits Yasin Abbasi-yadkori, Dávid Pál, Csaba Szepesvári
- From Bandits to Experts: On the Value of Side-Observations Shie Mannor, Ohad Shamir
- Optimal Reinforcement Learning for Gaussian Systems Philipp Hennig
- An Empirical Evaluation of Thompson Sampling Olivier Chapelle, Lihong Li
- Efficient Offline Communication Policies for Factored Multiagent POMDPs João Messias, Matthijs Spaan, Pedro Lima
- Maximal Cliques that Satisfy Hard Constraints with Application to Deformable Object Model Learning Xinggang Wang, Xiang Bai, Xingwei Yang, Wenyu Liu, Longin Latecki
- Quasi-Newton Methods for Markov Chain Monte Carlo Yichuan Zhang, Charles Sutton
- Inference in continuous-time change-point models Florian Stimberg, Manfred Opper, Guido Sanguinetti, Andreas Ruttor
- Universal low-rank matrix recovery from Pauli measurements Yi-kai Liu
- A Convergence Analysis of Log-Linear Training Simon Wiesler, Hermann Ney
- On the accuracy of l1-filtering of signals with block-sparse structure Fatma Karzan, Arkadi S. Nemirovski, Boris Polyak, Anatoli Juditsky
- Group Anomaly Detection using Flexible Genre Models Liang Xiong, Barnabás Póczos, Jeff Schneider
- Randomized Algorithms for Comparison-based Search Dominique Tschopp, Suhas Diggavi, Payam Delgosha, Soheil Mohajer
- Multiple Instance Learning on Structured Data Dan Zhang, Yan Liu, Luo Si, Jian Zhang, Richard Lawrence
- Probabilistic Joint Image Segmentation and Labeling Adrian Ion, Joao Carreira, Cristian Sminchisescu
- Learning to Search Efficiently in High Dimensions Zhen Li, Huazhong Ning, Liangliang Cao, Tong Zhang, Yihong Gong, Thomas S. Huang
- Learning a Tree of Metrics with Disjoint Visual Features Kristen Grauman, Fei Sha, Sung Hwang
- Monte Carlo Value Iteration with Macro-Actions Zhan Lim, Lee Sun, David Hsu
- Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels Vikas Sindhwani, Aurelie C. Lozano
- Distributed Delayed Stochastic Optimization Alekh Agarwal, John C. Duchi
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