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ORCID24. NIPS 2011: Granada, Spain
- John Shawe-Taylor, Richard S. Zemel, Peter L. Bartlett, Fernando C. N. Pereira, Kilian Q. Weinberger:
Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, Granada, Spain. 2011 - Hua Wang, Heng Huang, Farhad Kamangar, Feiping Nie, Chris H. Q. Ding:
Maximum Margin Multi-Instance Learning. 1-9 - Francis R. Bach:
Shaping Level Sets with Submodular Functions. 10-18 - Sergey Levine, Zoran Popovic, Vladlen Koltun:
Nonlinear Inverse Reinforcement Learning with Gaussian Processes. 19-27 - Carl Vondrick, Deva Ramanan:
Video Annotation and Tracking with Active Learning. 28-36 - Stéphan Clémençon:
On U-processes and clustering performance. 37-45 - Yong Zhang, Zhaosong Lu:
Penalty Decomposition Methods for Rank Minimization. 46-54 - Ehsan Elhamifar, René Vidal:
Sparse Manifold Clustering and Embedding. 55-63 - Siwei Lyu:
Unifying Non-Maximum Likelihood Learning Objectives with Minimum KL Contraction. 64-72 - Yibiao Zhao, Song Chun Zhu:
Image Parsing with Stochastic Scene Grammar. 73-81 - Mehdi Keramati, Boris S. Gutkin:
A Reinforcement Learning Theory for Homeostatic Regulation. 82-90 - Philip M. Long, Rocco A. Servedio:
Learning large-margin halfspaces with more malicious noise. 91-99 - Richard G. Gibson, Duane Szafron:
On Strategy Stitching in Large Extensive Form Multiplayer Games. 100-108 - Philipp Krähenbühl, Vladlen Koltun:
Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials. 109-117 - Joseph J. Lim, Ruslan Salakhutdinov, Antonio Torralba:
Transfer Learning by Borrowing Examples for Multiclass Object Detection. 118-126 - Dylan A. Simon, Nathaniel D. Daw:
Environmental statistics and the trade-off between model-based and TD learning in humans. 127-135 - Danilo Jimenez Rezende, Daan Wierstra, Wulfram Gerstner:
Variational Learning for Recurrent Spiking Networks. 136-144 - Dan Zhang, Yan Liu, Luo Si, Jian Zhang, Richard D. Lawrence:
Multiple Instance Learning on Structured Data. 145-153 - Nitesh Shroff, Pavan K. Turaga
, Rama Chellappa:
Manifold Precis: An Annealing Technique for Diverse Sampling of Manifolds. 154-162 - Ali Tofigh, Erik Sjölund, Mattias Höglund, Jens Lagergren:
A Global Structural EM Algorithm for a Model of Cancer Progression. 163-171 - Amir Massoud Farahmand:
Action-Gap Phenomenon in Reinforcement Learning. 172-180 - Nobuyuki Morioka, Shin'ichi Satoh:
Generalized Lasso based Approximation of Sparse Coding for Visual Recognition. 181-189 - Ricardo Silveira Cabral, Fernando De la Torre, João Paulo Costeira, Alexandre Bernardino:
Matrix Completion for Multi-label Image Classification. 190-198 - Paramveer S. Dhillon, Dean P. Foster, Lyle H. Ungar:
Multi-View Learning of Word Embeddings via CCA. 199-207 - Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan:
Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent. 208-216 - Ryota Kobayashi, Yasuhiro Tsubo, Petr Lánský, Shigeru Shinomoto:
Estimating time-varying input signals and ion channel states from a single voltage trace of a neuron. 217-225 - David Duvenaud, Hannes Nickisch, Carl Edward Rasmussen:
Additive Gaussian Processes. 226-234 - Hai-Son Le, Ziv Bar-Joseph:
Inferring Interaction Networks using the IBP applied to microRNA Target Prediction. 235-243 - Hema Swetha Koppula, Abhishek Anand, Thorsten Joachims, Ashutosh Saxena:
Semantic Labeling of 3D Point Clouds for Indoor Scenes. 244-252 - Shilin Ding, Grace Wahba, Xiaojin (Jerry) Zhu:
Learning Higher-Order Graph Structure with Features by Structure Penalty. 253-261 - Tingting Zhao, Hirotaka Hachiya, Gang Niu, Masashi Sugiyama:
Analysis and Improvement of Policy Gradient Estimation. 262-270 - Ioannis Gkioulekas, Todd E. Zickler:
Dimensionality Reduction Using the Sparse Linear Model. 271-279 - Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Pierre Dupont:
Robust Multi-Class Gaussian Process Classification. 280-288 - Christoph H. Lampert:
Maximum Margin Multi-Label Structured Prediction. 289-297 - Ke Chen, Ahmad Salman:
Extracting Speaker-Specific Information with a Regularized Siamese Deep Network. 298-306 - Ricardo Bezerra de Andrade e Silva:
Thinning Measurement Models and Questionnaire Design. 307-315 - Charles Kemp:
Inductive reasoning about chimeric creatures. 316-324 - Philipp Hennig:
Optimal Reinforcement Learning for Gaussian Systems. 325-333 - Weiran Wang, Miguel Á. Carreira-Perpiñán, Zhengdong Lu:
A Denoising View of Matrix Completion. 334-342 - Nicolò Cesa-Bianchi, Ohad Shamir:
Efficient Online Learning via Randomized Rounding. 343-351 - Lei Yuan, Jun Liu, Jieping Ye:
Efficient Methods for Overlapping Group Lasso. 352-360 - Jing Lei:
Differentially Private M-Estimators. 361-369 - Kamil Wnuk, Stefano Soatto:
Multiple Instance Filtering. 370-378 - Morteza Alamgir, Ulrike von Luxburg:
Phase transition in the family of p-resistances. 379-387 - Yoonho Hwang, Hee-Kap Ahn:
Convergent Bounds on the Euclidean Distance. 388-396 - Yangqing Jia, Trevor Darrell:
Heavy-tailed Distances for Gradient Based Image Descriptors. 397-405 - Nicolas Boumal, Pierre-Antoine Absil:
RTRMC: A Riemannian trust-region method for low-rank matrix completion. 406-414 - Guido Montúfar, Johannes Rauh, Nihat Ay:
Expressive Power and Approximation Errors of Restricted Boltzmann Machines. 415-423 - Jasmina Bogojeska:
History distribution matching method for predicting effectiveness of HIV combination therapies. 424-432 - Binbin Lin, Chiyuan Zhang, Xiaofei He:
Semi-supervised Regression via Parallel Field Regularization. 433-441 - Ross B. Girshick, Pedro F. Felzenszwalb, David A. McAllester:
Object Detection with Grammar Models. 442-450 - Francis R. Bach, Eric Moulines:
Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning. 451-459 - Stefanie Jegelka, Hui Lin, Jeff A. Bilmes:
On fast approximate submodular minimization. 460-468 - Matthias S. Keil:
Emergence of Multiplication in a Biophysical Model of a Wide-Field Visual Neuron for Computing Object Approaches: Dynamics, Peaks, & Fits. 469-477 - Kumar Sricharan, Alfred O. Hero III:
Efficient anomaly detection using bipartite k-NN graphs. 478-486 - Jun Liu, Liang Sun, Jieping Ye:
Projection onto A Nonnegative Max-Heap. 487-495 - David Newman, Edwin V. Bonilla, Wray L. Buntine:
Improving Topic Coherence with Regularized Topic Models. 496-504 - Qian Sun, Rita Chattopadhyay, Sethuraman Panchanathan, Jieping Ye:
A Two-Stage Weighting Framework for Multi-Source Domain Adaptation. 505-513 - Joseph L. Austerweil, Abram L. Friesen, Thomas L. Griffiths:
An ideal observer model for identifying the reference frame of objects. 514-522 - Artin Armagan, David B. Dunson, Merlise A. Clyde:
Generalized Beta Mixtures of Gaussians. 523-531 - Youwei Zhang, Laurent El Ghaoui:
Large-Scale Sparse Principal Component Analysis with Application to Text Data. 532-539 - Trung-Thanh Pham, Tat-Jun Chin, Jin Yu, David Suter:
Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC. 540-548 - Congcong Li, Ashutosh Saxena, Tsuhan Chen:
$\theta$-MRF: Capturing Spatial and Semantic Structure in the Parameters for Scene Understanding. 549-557 - Ryan Gomes, Peter Welinder, Andreas Krause, Pietro Perona:
Crowdclustering. 558-566 - Jia Deng, Sanjeev Satheesh, Alexander C. Berg, Li Fei-Fei:
Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition. 567-575 - Ichiro Takeuchi, Masashi Sugiyama:
Target Neighbor Consistent Feature Weighting for Nearest Neighbor Classification. 576-584 - Luca Oneto, Davide Anguita, Alessandro Ghio, Sandro Ridella:
The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers. 585-593 - Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Masashi Sugiyama:
Relative Density-Ratio Estimation for Robust Distribution Comparison. 594-602 - Denis Deratani Mauá, Cassio Polpo de Campos:
Solving Decision Problems with Limited Information. 603-611 - Zhouchen Lin, Risheng Liu, Zhixun Su:
Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation. 612-620 - Sung Ju Hwang, Kristen Grauman, Fei Sha:
Learning a Tree of Metrics with Disjoint Visual Features. 621-629 - Oliver Stegle, Christoph Lippert, Joris M. Mooij, Neil D. Lawrence, Karsten M. Borgwardt:
Efficient inference in matrix-variate Gaussian models with \iid observation noise. 630-638 - Joris M. Mooij, Dominik Janzing, Tom Heskes, Bernhard Schölkopf:
On Causal Discovery with Cyclic Additive Noise Models. 639-647 - Viren Jain, Srinivas C. Turaga, Kevin L. Briggman, Moritz Helmstaedter, Winfried Denk, H. Sebastian Seung:
Learning to Agglomerate Superpixel Hierarchies. 648-656 - Simon Wiesler, Hermann Ney:
A Convergence Analysis of Log-Linear Training. 657-665 - Olivier Delalleau, Yoshua Bengio:
Shallow vs. Deep Sum-Product Networks. 666-674 - Sebastian Kurtek, Anuj Srivastava, Wei Wu:
Signal Estimation Under Random Time-Warpings and Nonlinear Signal Alignment. 675-683 - Shie Mannor, Ohad Shamir:
From Bandits to Experts: On the Value of Side-Observations. 684-692 - Benjamin Recht, Christopher Ré, Stephen J. Wright, Feng Niu:
Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent. 693-701 - Jiayu Zhou, Jianhui Chen, Jieping Ye:
Clustered Multi-Task Learning Via Alternating Structure Optimization. 702-710 - Joel Z. Leibo, Jim Mutch, Tomaso A. Poggio:
Why The Brain Separates Face Recognition From Object Recognition. 711-719 - André da Motta Salles Barreto, Doina Precup, Joelle Pineau:
Reinforcement Learning using Kernel-Based Stochastic Factorization. 720-728 - Samory Kpotufe:
k-NN Regression Adapts to Local Intrinsic Dimension. 729-737 - Xaq Pitkow, Yashar Ahmadian, Kenneth D. Miller:
Learning unbelievable probabilities. 738-746 - Matthew A. Kayala, Pierre Baldi:
A Machine Learning Approach to Predict Chemical Reactions. 747-755 - Biljana Petreska, Byron M. Yu, John P. Cunningham, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani:
Dynamical segmentation of single trials from population neural data. 756-764 - Peter V. Gehler, Carsten Rother, Martin Kiefel, Lumin Zhang, Bernhard Schölkopf:
Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance. 765-773 - Emin Orhan, Robert A. Jacobs:
Probabilistic Modeling of Dependencies Among Visual Short-Term Memory Representations. 774-782 - Rémi Munos:
Optimistic Optimization of a Deterministic Function without the Knowledge of its Smoothness. 783-791 - Flavio Chierichetti, Jon M. Kleinberg, David Liben-Nowell:
Reconstructing Patterns of Information Diffusion from Incomplete Observations. 792-800 - Richard Socher, Eric H. Huang, Jeffrey Pennington, Andrew Y. Ng, Christopher D. Manning:
Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection. 801-809 - Nir Ailon:
Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity. 810-818 - Yee Whye Teh, Charles Blundell, Lloyd T. Elliott:
Modelling Genetic Variations using Fragmentation-Coagulation Processes. 819-827 - Michael Kapralov, Rina Panigrahy:
Prediction strategies without loss. 828-836 - Xiaoyin Ge, Issam Safa, Mikhail Belkin, Yusu Wang:
Data Skeletonization via Reeb Graphs. 837-845 - Kamiar Rahnama Rad, Liam Paninski:
Information Rates and Optimal Decoding in Large Neural Populations. 846-854 - Dmitry Pidan, Ran El-Yaniv:
Selective Prediction of Financial Trends with Hidden Markov Models. 855-863 - Xinggang Wang, Xiang Bai, Xingwei Yang, Wenyu Liu, Longin Jan Latecki:
Maximal Cliques that Satisfy Hard Constraints with Application to Deformable Object Model Learning. 864-872 - Alekh Agarwal, John C. Duchi:
Distributed Delayed Stochastic Optimization. 873-881 - Ambuj Tewari, Pradeep Ravikumar, Inderjit S. Dhillon:
Greedy Algorithms for Structurally Constrained High Dimensional Problems. 882-890 - Elad Hazan, Satyen Kale:
Newtron: an Efficient Bandit algorithm for Online Multiclass Prediction. 891-899 - Zhen James Xiang, Hao Xu, Peter J. Ramadge:
Learning Sparse Representations of High Dimensional Data on Large Scale Dictionaries. 900-908 - Mladen Kolar, Sivaraman Balakrishnan, Alessandro Rinaldo, Aarti Singh:
Minimax Localization of Structural Information in Large Noisy Matrices. 909-917 - Vijay Mahadevan, Chi Wah Wong, José Costa Pereira, Tom Liu, Nuno Vasconcelos, Lawrence K. Saul:
Maximum Covariance Unfolding : Manifold Learning for Bimodal Data. 918-926 - Sham M. Kakade, Adam Kalai, Varun Kanade, Ohad Shamir:
Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression. 927-935 - Bo Chen, David E. Carlson, Lawrence Carin:
On the Analysis of Multi-Channel Neural Spike Data. 936-944 - Wouter M. Koolen, Wojciech Kotlowski, Manfred K. Warmuth:
Learning Eigenvectors for Free. 945-953 - Sivaraman Balakrishnan, Min Xu, Akshay Krishnamurthy, Aarti Singh:
Noise Thresholds for Spectral Clustering. 954-962 - Lu Ren, Yingjian Wang, David B. Dunson, Lawrence Carin:
The Kernel Beta Process. 963-971 - Ryota Tomioka, Taiji Suzuki, Kohei Hayashi, Hisashi Kashima:
Statistical Performance of Convex Tensor Decomposition. 972-980 - Richard E. Turner, Maneesh Sahani:
Probabilistic amplitude and frequency demodulation. 981-989 - Dominique C. Perrault-Joncas, Marina Meila:
Directed Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type Operators. 990-998 - Yan Karklin, Eero P. Simoncelli:
Efficient coding of natural images with a population of noisy Linear-Nonlinear neurons. 999-1007 - David A. Sontag, Daniel M. Roy:
Complexity of Inference in Latent Dirichlet Allocation. 1008-1016 - Quoc V. Le, Alexandre Karpenko, Jiquan Ngiam, Andrew Y. Ng:
ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning. 1017-1025 - Maxim Raginsky, Alexander Rakhlin:
Lower Bounds for Passive and Active Learning. 1026-1034 - Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin:
Stochastic convex optimization with bandit feedback. 1035-1043 - Shulin Yang, Ali Rahimi:
Structure Learning for Optimization. 1044-1052 - Shahed Sorower, Thomas G. Dietterich, Janardhan Rao Doppa, John Walker Orr, Prasad Tadepalli, Xiaoli Z. Fern:
Inverting Grice's Maxims to Learn Rules from Natural Language Extractions. 1053-1061 - Tianshi Gao, Daphne Koller:
Active Classification based on Value of Classifier. 1062-1070 - Liang Xiong, Barnabás Póczos, Jeff G. Schneider:
Group Anomaly Detection using Flexible Genre Models. 1071-1079 - Dan Garber, Elad Hazan:
Approximating Semidefinite Programs in Sublinear Time. 1080-1088 - Andrew E. Waters, Aswin C. Sankaranarayanan, Richard G. Baraniuk:
SpaRCS: Recovering low-rank and sparse matrices from compressive measurements. 1089-1097 - Javad Azimi, Alan Fern, Xiaoli Z. Fern:
Budgeted Optimization with Concurrent Stochastic-Duration Experiments. 1098-1106 - Andrew Guillory, Jeff A. Bilmes:
Online Submodular Set Cover, Ranking, and Repeated Active Learning. 1107-1115 - Arthur Szlam, Karol Gregor, Yann LeCun:
Structured sparse coding via lateral inhibition. 1116-1124 - Jiquan Ngiam, Pang Wei Koh, Zhenghao Chen, Sonia A. Bhaskar, Andrew Y. Ng:
Sparse Filtering. 1125-1133 - Lester W. Mackey, Ameet Talwalkar, Michael I. Jordan:
Divide-and-Conquer Matrix Factorization. 1134-1142 - Vicente Ordonez, Girish Kulkarni, Tamara L. Berg:
Im2Text: Describing Images Using 1 Million Captioned Photographs. 1143-1151 - David Wingate, Noah D. Goodman, Andreas Stuhlmüller, Jeffrey Mark Siskind:
Nonstandard Interpretations of Probabilistic Programs for Efficient Inference. 1152-1160 - Daniel Sheldon, Thomas G. Dietterich:
Collective Graphical Models. 1161-1169 - Jun Wang, Huyen Do, Adam Woznica, Alexandros Kalousis:
Metric Learning with Multiple Kernels. 1170-1178 - Shai Shalev-Shwartz, Yonatan Wexler, Amnon Shashua:
ShareBoost: Efficient multiclass learning with feature sharing. 1179-1187 - Balázs Ujfalussy, Máté Lengyel:
Active dendrites: adaptation to spike-based communication. 1188-1196 - Jiarong Jiang, Piyush Rai, Hal Daumé III:
Message-Passing for Approximate MAP Inference with Latent Variables. 1197-1205 - Miles Lopes, Laurent Jacob, Martin J. Wainwright:
A More Powerful Two-Sample Test in High Dimensions using Random Projection. 1206-1214 - Prateek Jain, Ambuj Tewari, Inderjit S. Dhillon:
Orthogonal Matching Pursuit with Replacement. 1215-1223 - Elodie Vernet, Robert C. Williamson, Mark D. Reid:
Composite Multiclass Losses. 1224-1232 - Elad Hazan, Tomer Koren, Nati Srebro:
Beating SGD: Learning SVMs in Sublinear Time. 1233-1241 - Dong Dai, Tong Zhang:
Greedy Model Averaging. 1242-1250 - Bin Zhao, Li Fei-Fei, Eric P. Xing:
Large-Scale Category Structure Aware Image Categorization. 1251-1259 - Anatoli Iouditski, Fatma Kilinç-Karzan, Arkadi Nemirovski, Boris T. Polyak:
On the accuracy of l1-filtering of signals with block-sparse structure. 1260-1268 - Qibin Zhao, Cesar F. Caiafa, Danilo P. Mandic, Liqing Zhang, Tonio Ball, Andreas Schulze-Bonhage, Andrzej Cichocki:
Multilinear Subspace Regression: An Orthogonal Tensor Decomposition Approach. 1269-1277 - Alexandra Carpentier, Rémi Munos:
Finite Time Analysis of Stratified Sampling for Monte Carlo. 1278-1286 - Zhan Wei Lim, David Hsu, Wee Sun Lee:
Monte Carlo Value Iteration with Macro-Actions. 1287-1295 - Xinghua Lou, Fred A. Hamprecht:
Structured Learning for Cell Tracking. 1296-1304 - Cristina Savin, Peter Dayan, Máté Lengyel:
Two is better than one: distinct roles for familiarity and recollection in retrieving palimpsest memories. 1305-1313 - Rocco A. Servedio, Philip M. Long:
Algorithms and hardness results for parallel large margin learning. 1314-1322 - Fahad Shahbaz Khan, Joost van de Weijer, Andrew D. Bagdanov, María Vanrell:
Portmanteau Vocabularies for Multi-Cue Image Representation. 1323-1331 - Charles Dubout, François Fleuret:
Boosting with Maximum Adaptive Sampling. 1332-1340 - Andrew McHutchon, Carl Edward Rasmussen:
Gaussian Process Training with Input Noise. 1341-1349 - Jakob H. Macke, Lars Buesing, John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani:
Empirical models of spiking in neural populations. 1350-1358 - Angela Yao, Juergen Gall, Luc Van Gool, Raquel Urtasun:
Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities. 1359-1367 - David Adametz, Volker Roth:
Bayesian Partitioning of Large-Scale Distance Data. 1368-1376 - Skander Mensi, Richard Naud, Wulfram Gerstner:
From Stochastic Nonlinear Integrate-and-Fire to Generalized Linear Models. 1377-1385 - Guy Van den Broeck:
On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic Inference. 1386-1394 - XianXing Zhang, David B. Dunson, Lawrence Carin:
Hierarchical Topic Modeling for Analysis of Time-Evolving Personal Choices. 1395-1403 - Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier:
An Exact Algorithm for F-Measure Maximization. 1404-1412 - Abhishek Kumar, Piyush Rai, Hal Daumé III:
Co-regularized Multi-view Spectral Clustering. 1413-1421 - Johanni Brea, Walter Senn, Jean-Pascal Pfister:
Sequence learning with hidden units in spiking neural networks. 1422-1430 - Shuai Huang, Jing Li, Jieping Ye, Teresa Wu, Kewei Chen, Adam Fleisher, Eric Reiman:
Identifying Alzheimer's Disease-Related Brain Regions from Multi-Modality Neuroimaging Data using Sparse Composite Linear Discrimination Analysis. 1431-1439 - Chaitanya Ekanadham, Daniel Tranchina, Eero P. Simoncelli:
A blind sparse deconvolution method for neural spike identification. 1440-1448 - Faisal Khan, Xiaojin (Jerry) Zhu, Bilge Mutlu:
How Do Humans Teach: On Curriculum Learning and Teaching Dimension. 1449-1457 - Mark Schmidt, Nicolas Le Roux, Francis R. Bach:
Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization. 1458-1466 - Andreas Geiger, Christian Wojek, Raquel Urtasun:
Joint 3D Estimation of Objects and Scene Layout. 1467-1475 - Soumya Ghosh, Andrei B. Ungureanu, Erik B. Sudderth, David M. Blei:
Spatial distance dependent Chinese restaurant processes for image segmentation. 1476-1484 - Victor S. Lempitsky, Andrea Vedaldi, Andrew Zisserman:
Pylon Model for Semantic Segmentation. 1485-1493 - Nan Ding, S. V. N. Vishwanathan, Yuan (Alan) Qi:
t-divergence Based Approximate Inference. 1494-1502 - Vincent Delaitre, Josef Sivic, Ivan Laptev:
Learning person-object interactions for action recognition in still images. 1503-1511 - James Petterson, Tibério S. Caetano:
Submodular Multi-Label Learning. 1512-1520 - Yusuke Watanabe:
Uniqueness of Belief Propagation on Signed Graphs. 1521-1529 - Sungwoong Kim, Sebastian Nowozin, Pushmeet Kohli, Chang Dong Yoo:
Higher-Order Correlation Clustering for Image Segmentation. 1530-1538 - Mona Eberts, Ingo Steinwart:
Optimal learning rates for least squares SVMs using Gaussian kernels. 1539-1547 - Tzu-Kuo Huang, Jeff G. Schneider:
Learning Auto-regressive Models from Sequence and Non-sequence Data. 1548-1556 - Loc Bui, Ramesh Johari, Shie Mannor:
Committing Bandits. 1557-1565 - Martin B. Stemmler, Biswa Sengupta, Simon B. Laughlin, Jeremy E. Niven:
Energetically Optimal Action Potentials. 1566-1574 - Taiji Suzuki:
Unifying Framework for Fast Learning Rate of Non-Sparse Multiple Kernel Learning. 1575-1583 - Fabio Vitale, Nicolò Cesa-Bianchi, Claudio Gentile, Giovanni Zappella:
See the Tree Through the Lines: The Shazoo Algorithm. 1584-1592 - Matus Telgarsky:
The Fast Convergence of Boosting. 1593-1601 - Aleksandrs Slivkins:
Multi-armed bandits on implicit metric spaces. 1602-1610 - Ziming Zhang, Lubor Ladicky, Philip H. S. Torr, Amir Saffari:
Learning Anchor Planes for Classification. 1611-1619 - Jun Zhu, Ning Chen, Eric P. Xing:
Infinite Latent SVM for Classification and Multi-task Learning. 1620-1628 - Matthew D. Zeiler, Graham W. Taylor, Leonid Sigal, Iain A. Matthews, Rob Fergus:
Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines. 1629-1637 - Yi-Kai Liu:
Universal low-rank matrix recovery from Pauli measurements. 1638-1646 - Andrew Cotter, Ohad Shamir, Nati Srebro, Karthik Sridharan:
Better Mini-Batch Algorithms via Accelerated Gradient Methods. 1647-1655 - Tim van Erven, Peter Grunwald, Wouter M. Koolen, Steven de Rooij:
Adaptive Hedge. 1656-1664 - Yair Wiener, Ran El-Yaniv:
Agnostic Selective Classification. 1665-1673 - Armen E. Allahverdyan, Aram Galstyan:
Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs. 1674-1682 - Yevgeny Seldin, Peter Auer, François Laviolette, John Shawe-Taylor, Ronald Ortner:
PAC-Bayesian Analysis of Contextual Bandits. 1683-1691 - Il Memming Park, Jonathan W. Pillow:
Bayesian Spike-Triggered Covariance Analysis. 1692-1700 - David A. Knowles, Tom Minka:
Non-conjugate Variational Message Passing for Multinomial and Binary Regression. 1701-1709 - Zhen Li, Huazhong Ning, Liangliang Cao, Tong Zhang, Yihong Gong, Thomas S. Huang:
Learning to Search Efficiently in High Dimensions. 1710-1718 - Oliver Kroemer, Jan Peters:
A Non-Parametric Approach to Dynamic Programming. 1719-1727 - Gautam Kunapuli, Richard Maclin, Jude W. Shavlik:
Advice Refinement in Knowledge-Based SVMs. 1728-1736 - Kenji Fukumizu, Le Song, Arthur Gretton:
Kernel Bayes' Rule. 1737-1745 - Alessandro Lazaric, Marcello Restelli:
Transfer from Multiple MDPs. 1746-1754 - Cédric Archambeau, Shengbo Guo, Onno Zoeter:
Sparse Bayesian Multi-Task Learning. 1755-1763 - Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Online Learning: Stochastic, Constrained, and Smoothed Adversaries. 1764-1772 - Bharath K. Sriperumbudur, Kenji Fukumizu, Gert R. G. Lanckriet:
Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint. 1773-1781 - Alexandra Carpentier, Odalric-Ambrym Maillard, Rémi Munos:
Sparse Recovery with Brownian Sensing. 1782-1790 - Michael C. Mozer, Benjamin Link, Harold Pashler:
An Unsupervised Decontamination Procedure For Improving The Reliability Of Human Judgments. 1791-1799 - Fabian L. Wauthier, Michael I. Jordan:
Bayesian Bias Mitigation for Crowdsourcing. 1800-1808 - Vikas C. Raykar, Shipeng Yu:
Ranking annotators for crowdsourced labeling tasks. 1809-1817 - Scott Niekum, Andrew G. Barto:
Clustering via Dirichlet Process Mixture Models for Portable Skill Discovery. 1818-1826 - Adrian Ion, João Carreira, Cristian Sminchisescu:
Probabilistic Joint Image Segmentation and Labeling. 1827-1835 - Joel Veness, Marc Lanctot, Michael H. Bowling:
Variance Reduction in Monte-Carlo Tree Search. 1836-1844 - Chun-Nam Yu, Russell Greiner, Hsiu-Chin Lin, Vickie Baracos:
Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors. 1845-1853 - Pablo M. Olmos, Luis Salamanca, Juan José Murillo-Fuentes, Fernando Pérez-Cruz:
An Application of Tree-Structured Expectation Propagation for Channel Decoding. 1854-1862 - Animashree Anandkumar, Vincent Y. F. Tan, Alan S. Willsky:
High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions. 1863-1871 - Junichiro Hirayama, Aapo Hyvärinen:
Structural equations and divisive normalization for energy-dependent component analysis. 1872-1880 - Nam H. Nguyen, Nasser M. Nasrabadi, Trac D. Tran:
Robust Lasso with missing and grossly corrupted observations. 1881-1889 - Martin O. Larsson, Johan Ugander:
A concave regularization technique for sparse mixture models. 1890-1898 - Blake Shaw, Bert Huang, Tony Jebara:
Learning a Distance Metric from a Network. 1899-1907 - Pannagadatta K. Shivaswamy, Tony Jebara:
Variance Penalizing AdaBoost. 1908-1916 - João V. Messias, Matthijs T. J. Spaan, Pedro U. Lima:
Efficient Offline Communication Policies for Factored Multiagent POMDPs. 1917-1925 - Martin Slawski, Matthias Hein:
Sparse recovery by thresholded non-negative least squares. 1926-1934 - Ali Jalali, Christopher C. Johnson, Pradeep Ravikumar:
On Learning Discrete Graphical Models using Greedy Methods. 1935-1943 - Philip S. Thomas:
Policy Gradient Coagent Networks. 1944-1952 - David R. Karger, Sewoong Oh, Devavrat Shah:
Iterative Learning for Reliable Crowdsourcing Systems. 1953-1961 - Asela Gunawardana, Christopher Meek, Puyang Xu:
A Model for Temporal Dependencies in Event Streams. 1962-1970 - Andrew M. Saxe, Maneesh Bhand, Ritvik Mudur, Bipin Suresh, Andrew Y. Ng:
Unsupervised learning models of primary cortical receptive fields and receptive field plasticity. 1971-1979 - Dae Il Kim, Erik B. Sudderth:
The Doubly Correlated Nonparametric Topic Model. 1980-1988 - Jaedeug Choi, Kee-Eung Kim:
MAP Inference for Bayesian Inverse Reinforcement Learning. 1989-1997 - Purushottam Kar, Prateek Jain:
Similarity-based Learning via Data Driven Embeddings. 1998-2006 - Olana Missura, Thomas Gärtner:
Predicting Dynamic Difficulty. 2007-2015 - David P. Wipf:
Sparse Estimation with Structured Dictionaries. 2016-2024 - Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song, Tong Zhang:
Spectral Methods for Learning Multivariate Latent Tree Structure. 2025-2033 - Jakob H. Macke, Iain Murray, Peter E. Latham:
How biased are maximum entropy models? 2034-2042 - Mijung Park, Greg Horwitz, Jonathan W. Pillow:
Active learning of neural response functions with Gaussian processes. 2043-2051 - Ardavan Saeedi, Alexandre Bouchard-Côté:
Priors over Recurrent Continuous Time Processes. 2052-2060 - Ruslan Salakhutdinov, Joshua B. Tenenbaum, Antonio Torralba:
Learning to Learn with Compound HD Models. 2061-2069 - Zuoguan Wang, Gerwin Schalk, Qiang Ji:
Anatomically Constrained Decoding of Finger Flexion from Electrocorticographic Signals. 2070-2078 - Liu Yang:
Active Learning with a Drifting Distribution. 2079-2087 - Alessandro Bergamo, Lorenzo Torresani, Andrew W. Fitzgibbon:
PiCoDes: Learning a Compact Code for Novel-Category Recognition. 2088-2096 - David S. Choi, Patrick J. Wolfe, Edoardo M. Airoldi:
Confidence Sets for Network Structure. 2097-2105 - Yoshinobu Kawahara, Takashi Washio:
Prismatic Algorithm for Discrete D.C. Programming Problem. 2106-2114 - Liefeng Bo, Xiaofeng Ren, Dieter Fox:
Hierarchical Matching Pursuit for Image Classification: Architecture and Fast Algorithms. 2115-2123 - Mohammad J. Saberian, Nuno Vasconcelos:
Multiclass Boosting: Theory and Algorithms. 2124-2132 - Rina Foygel, Ruslan Salakhutdinov, Ohad Shamir, Nati Srebro:
Learning with the weighted trace-norm under arbitrary sampling distributions. 2133-2141 - Dan Feldman, Matthew Faulkner, Andreas Krause:
Scalable Training of Mixture Models via Coresets. 2142-2150 - Yusuf Kenan Yilmaz, Ali Taylan Cemgil, Umut Simsekli:
Generalised Coupled Tensor Factorisation. 2151-2159 - Inderjit S. Dhillon, Pradeep Ravikumar, Ambuj Tewari:
Nearest Neighbor based Greedy Coordinate Descent. 2160-2168 - J. Zico Kolter:
The Fixed Points of Off-Policy TD. 2169-2177 - Gilles Blanchard, Gyemin Lee, Clayton Scott:
Generalizing from Several Related Classification Tasks to a New Unlabeled Sample. 2178-2186 - Edouard Grave, Guillaume Obozinski, Francis R. Bach:
Trace Lasso: a trace norm regularization for correlated designs. 2187-2195 - Levi Boyles, Anoop Korattikara Balan, Deva Ramanan, Max Welling:
Statistical Tests for Optimization Efficiency. 2196-2204 - David A. McAllester, Joseph Keshet:
Generalization Bounds and Consistency for Latent Structural Probit and Ramp Loss. 2205-2212 - Julie Dethier, Paul Nuyujukian, Chris Eliasmith, Terrence C. Stewart, Shauki A. Elasaad, Krishna V. Shenoy, Kwabena Boahen:
A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm. 2213-2221 - Victor Gabillon, Mohammad Ghavamzadeh, Alessandro Lazaric, Sébastien Bubeck:
Multi-Bandit Best Arm Identification. 2222-2230 - Dominique Tschopp, Suhas N. Diggavi, Payam Delgosha, Soheil Mohajer:
Randomized Algorithms for Comparison-based Search. 2231-2239 - Kevin G. Jamieson, Robert D. Nowak:
Active Ranking using Pairwise Comparisons. 2240-2248 - Olivier Chapelle, Lihong Li:
An Empirical Evaluation of Thompson Sampling. 2249-2257 - Thomas J. Walsh, Daniel Hewlett, Clayton T. Morrison:
Blending Autonomous Exploration and Apprenticeship Learning. 2258-2266 - Onur Dikmen, Cédric Févotte:
Nonnegative dictionary learning in the exponential noise model for adaptive music signal representation. 2267-2275 - Alan Jern, Christopher G. Lucas, Charles Kemp:
Evaluating the inverse decision-making approach to preference learning. 2276-2284 - Christos Boutsidis, Petros Drineas, Malik Magdon-Ismail:
Sparse Features for PCA-Like Linear Regression. 2285-2293 - Salah Rifai, Yann N. Dauphin, Pascal Vincent, Yoshua Bengio, Xavier Muller:
The Manifold Tangent Classifier. 2294-2302 - Alex K. Susemihl, Ron Meir, Manfred Opper:
Analytical Results for the Error in Filtering of Gaussian Processes. 2303-2311 - Yasin Abbasi-Yadkori, Dávid Pál, Csaba Szepesvári:
Improved Algorithms for Linear Stochastic Bandits. 2312-2320 - Joshua T. Abbott, Katherine A. Heller, Zoubin Ghahramani, Thomas L. Griffiths:
Testing a Bayesian Measure of Representativeness Using a Large Image Database. 2321-2329 - Cho-Jui Hsieh, Mátyás A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar:
Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation. 2330-2338 - Michalis K. Titsias, Miguel Lázaro-Gredilla:
Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning. 2339-2347 - Alex Graves:
Practical Variational Inference for Neural Networks. 2348-2356 - David P. Reichert, Peggy Seriès, Amos J. Storkey:
Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability. 2357-2365 - Matthias Hein, Simon Setzer:
Beyond Spectral Clustering - Tight Relaxations of Balanced Graph Cuts. 2366-2374 - Michael Shindler, Alex Wong, Adam Meyerson:
Fast and Accurate k-means For Large Datasets. 2375-2383 - Michael Pacer, Thomas L. Griffiths:
A rational model of causal inference with continuous causes. 2384-2392 - Yichuan Zhang, Charles Sutton:
Quasi-Newton Methods for Markov Chain Monte Carlo. 2393-2401 - George Dimitri Konidaris, Scott Niekum, Philip S. Thomas:
TD_gamma: Re-evaluating Complex Backups in Temporal Difference Learning. 2402-2410 - Mohammad Gheshlaghi Azar, Rémi Munos, Mohammad Ghavamzadeh, Hilbert J. Kappen:
Speedy Q-Learning. 2411-2419 - Patrick O. Perry, Michael W. Mahoney:
Regularized Laplacian Estimation and Fast Eigenvector Approximation. 2420-2428 - Phillip Isola, Devi Parikh, Antonio Torralba, Aude Oliva:
Understanding the Intrinsic Memorability of Images. 2429-2437 - Marius Kloft, Gilles Blanchard:
The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning. 2438-2446 - Andreas Krause, Cheng Soon Ong:
Contextual Gaussian Process Bandit Optimization. 2447-2455 - Minmin Chen, Kilian Q. Weinberger, John Blitzer:
Co-Training for Domain Adaptation. 2456-2464 - Neville Mehta, Prasad Tadepalli, Alan Fern:
Autonomous Learning of Action Models for Planning. 2465-2473 - Vinayak A. Rao, Yee Whye Teh:
Gaussian process modulated renewal processes. 2474-2482 - Yisong Yue, Carlos Guestrin:
Linear Submodular Bandits and their Application to Diversified Retrieval. 2483-2491 - Duy Quang Vu, Arthur U. Asuncion, David R. Hunter, Padhraic Smyth:
Continuous-Time Regression Models for Longitudinal Networks. 2492-2500 - Guillaume Desjardins, Aaron C. Courville, Yoshua Bengio:
On Tracking The Partition Function. 2501-2509 - Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence:
Variational Gaussian Process Dynamical Systems. 2510-2518 - Vikas Sindhwani, Aurélie C. Lozano:
Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels. 2519-2527 - Adam Coates, Andrew Y. Ng:
Selecting Receptive Fields in Deep Networks. 2528-2536 - Daniel J. Lizotte:
Convergent Fitted Value Iteration with Linear Function Approximation. 2537-2545 - James Bergstra, Rémi Bardenet, Yoshua Bengio, Balázs Kégl:
Algorithms for Hyper-Parameter Optimization. 2546-2554 - Alyson K. Fletcher, Sundeep Rangan, Lav R. Varshney, Aniruddha Bhargava:
Neural Reconstruction with Approximate Message Passing (NeuRAMP). 2555-2563 - Michael L. Wick, Andrew McCallum:
Query-Aware MCMC. 2564-2572 - Paul Wagner:
A reinterpretation of the policy oscillation phenomenon in approximate policy iteration. 2573-2581 - Ian H. Stevenson, Konrad P. Körding:
Inferring spike-timing-dependent plasticity from spike train data. 2582-2590 - Omar Zia Khan, Pascal Poupart, John Mark Agosta:
Automated Refinement of Bayes Networks' Parameters based on Test Ordering Constraints. 2591-2599 - Jacob D. Abernethy, Rafael M. Frongillo:
A Collaborative Mechanism for Crowdsourcing Prediction Problems. 2600-2608 - Adler J. Perotte, Frank D. Wood, Noemie Elhadad, Nicholas Bartlett:
Hierarchically Supervised Latent Dirichlet Allocation. 2609-2617 - Jacquelyn A. Shelton, Jörg Bornschein, Abdul-Saboor Sheikh, Pietro Berkes, Jörg Lücke:
Select and Sample - A Model of Efficient Neural Inference and Learning. 2618-2626 - Odalric-Ambrym Maillard, Rémi Munos, Daniil Ryabko:
Selecting the State-Representation in Reinforcement Learning. 2627-2635 - Joni Pajarinen, Jaakko Peltonen:
Periodic Finite State Controllers for Efficient POMDP and DEC-POMDP Planning. 2636-2644 - Nati Srebro, Karthik Sridharan, Ambuj Tewari:
On the Universality of Online Mirror Descent. 2645-2653 - Wieland Brendel, Ranulfo Romo, Christian K. Machens:
Demixed Principal Component Analysis. 2654-2662 - Yuan (Alan) Qi, Feng Yan:
EigenNet: A Bayesian hybrid of generative and conditional models for sparse learning. 2663-2671 - Ping Li, Anshumali Shrivastava, Joshua L. Moore, Arnd Christian König:
Hashing Algorithms for Large-Scale Learning. 2672-2680 - Iasonas Kokkinos:
Rapid Deformable Object Detection using Dual-Tree Branch-and-Bound. 2681-2689 - Nico Görnitz, Christian Widmer, Georg Zeller, André Kahles, Sören Sonnenburg, Gunnar Rätsch:
Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation. 2690-2698 - Bo Chen, Vidhya Navalpakkam, Pietro Perona:
Predicting response time and error rates in visual search. 2699-2707 - Le Song, Ankur P. Parikh, Eric P. Xing:
Kernel Embeddings of Latent Tree Graphical Models. 2708-2716 - Florian Stimberg, Manfred Opper, Guido Sanguinetti, Andreas Ruttor:
Inference in continuous-time change-point models. 2717-2725 - Po-Ling Loh, Martin J. Wainwright:
High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity. 2726-2734 - Bogdan Alexe, Viviana Petrescu, Vittorio Ferrari:
Exploiting spatial overlap to efficiently compute appearance distances between image windows. 2735-2743 - Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman:
Accelerated Adaptive Markov Chain for Partition Function Computation. 2744-2752
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