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ORCID25. NIPS 2012: Lake Tahoe, Nevada, USA
- Peter L. Bartlett, Fernando C. N. Pereira, Christopher J. C. Burges, Léon Bottou, Kilian Q. Weinberger:
Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States. 2012 - Andrew Ziegler, Eric M. Christiansen, David J. Kriegman, Serge J. Belongie:
Locally Uniform Comparison Image Descriptor. 1-9 - Krikamol Muandet, Kenji Fukumizu, Francesco Dinuzzo, Bernhard Schölkopf:
Learning from Distributions via Support Measure Machines. 10-18 - Ehsan Elhamifar, Guillermo Sapiro, René Vidal:
Finding Exemplars from Pairwise Dissimilarities via Simultaneous Sparse Recovery. 19-27 - Chad Scherrer, Ambuj Tewari, Mahantesh Halappanavar, David Haglin:
Feature Clustering for Accelerating Parallel Coordinate Descent. 28-36 - Chuanxin Minos Niu, Sirish K. Nandyala, Won Joon Sohn, Terence D. Sanger:
Multi-scale Hyper-time Hardware Emulation of Human Motor Nervous System Based on Spiking Neurons using FPGA. 37-45 - Michael A. Osborne, David Duvenaud, Roman Garnett, Carl E. Rasmussen, Stephen J. Roberts, Zoubin Ghahramani:
Active Learning of Model Evidence Using Bayesian Quadrature. 46-54 - Dahua Lin, John W. Fisher III:
Coupling Nonparametric Mixtures via Latent Dirichlet Processes. 55-63 - Minjie Xu, Jun Zhu, Bo Zhang:
Nonparametric Max-Margin Matrix Factorization for Collaborative Prediction. 64-72 - Feng Cao, Soumya Ray:
Bayesian Hierarchical Reinforcement Learning. 73-81 - Christoph H. Lampert:
Dynamic Pruning of Factor Graphs for Maximum Marginal Prediction. 82-90 - Joseph Wang, Venkatesh Saligrama:
Local Supervised Learning through Space Partitioning. 91-99 - S. M. Ali Eslami, Christopher K. I. Williams:
A Generative Model for Parts-based Object Segmentation. 100-107 - Jianqiu Ji, Jianmin Li, Shuicheng Yan, Bo Zhang, Qi Tian:
Super-Bit Locality-Sensitive Hashing. 108-116 - Nicholas Ruozzi
:
The Bethe Partition Function of Log-supermodular Graphical Models. 117-125 - Hossein Azari Soufiani, David C. Parkes, Lirong Xia:
Random Utility Theory for Social Choice. 126-134 - Wouter M. Koolen, Dmitry Adamskiy, Manfred K. Warmuth:
Putting Bayes to sleep. 135-143 - Suvrit Sra:
A new metric on the manifold of kernel matrices with application to matrix geometric means. 144-152 - Wei Bi, James T. Kwok:
Mandatory Leaf Node Prediction in Hierarchical Multilabel Classification. 153-161 - Tuo Zhao, Kathryn Roeder, Han Liu:
Smooth-projected Neighborhood Pursuit for High-dimensional Nonparanormal Graph Estimation. 162-170 - Fang Han, Han Liu:
Semiparametric Principal Component Analysis. 171-179 - Shinsuke Koyama:
Coding efficiency and detectability of rate fluctuations with non-Poisson neuronal firing. 180-188 - Francesco Dinuzzo, Bernhard Schölkopf:
The representer theorem for Hilbert spaces: a necessary and sufficient condition. 189-196 - Clément Calauzènes, Nicolas Usunier, Patrick Gallinari:
"On the (Non-)existence of Convex, Calibrated Surrogate Losses for Ranking". 197-205 - Manuel Lopes, Tobias Lang, Marc Toussaint, Pierre-Yves Oudeyer:
Exploration in Model-based Reinforcement Learning by Empirically Estimating Learning Progress. 206-214 - Purushottam Kar, Prateek Jain:
Supervised Learning with Similarity Functions. 215-223 - Yuxuan Wang, DeLiang Wang:
Cocktail Party Processing via Structured Prediction. 224-232 - Takayuki Osogami:
Robustness and risk-sensitivity in Markov decision processes. 233-241 - Xiaolong Wang, Liang Lin:
Dynamical And-Or Graph Learning for Object Shape Modeling and Detection. 242-250 - Alexandra Carpentier, Rémi Munos:
Adaptive Stratified Sampling for Monte-Carlo integration of Differentiable functions. 251-259 - Varun Kanade, Zhenming Liu, Bozidar Radunovic:
Distributed Non-Stochastic Experts. 260-268 - Allison Chang, Dimitris Bertsimas, Cynthia Rudin:
An Integer Optimization Approach to Associative Classification. 269-277 - Tomasz Trzcinski, C. Mario Christoudias, Vincent Lepetit, Pascal Fua:
Learning Image Descriptors with the Boosting-Trick. 278-286 - Chunxiao Zhou, Jiseong Park, Yun Fu:
Fast Resampling Weighted v-Statistics. 287-295 - Binbin Lin, Sen Yang, Chiyuan Zhang, Jieping Ye, Xiaofei He:
Multi-task Vector Field Learning. 296-304 - Aditya Khosla, Jianxiong Xiao, Antonio Torralba, Aude Oliva:
Memorability of Image Regions. 305-313 - Jaedeug Choi, Kee-Eung Kim:
Nonparametric Bayesian Inverse Reinforcement Learning for Multiple Reward Functions. 314-322 - Joonseok Lee, Mingxuan Sun, Seungyeon Kim, Guy Lebanon:
Automatic Feature Induction for Stagewise Collaborative Filtering. 323-331 - Quanquan Gu, Tong Zhang, Chris H. Q. Ding, Jiawei Han:
Selective Labeling via Error Bound Minimization. 332-340 - Koby Crammer, Tal Wagner:
Volume Regularization for Binary Classification. 341-349 - Junyuan Xie, Linli Xu, Enhong Chen:
Image Denoising and Inpainting with Deep Neural Networks. 350-358 - Du Tran, Junsong Yuan:
Max-Margin Structured Output Regression for Spatio-Temporal Action Localization. 359-367 - Fang Han, Han Liu:
Transelliptical Component Analysis. 368-376 - Hankz Hankui Zhuo, Qiang Yang, Subbarao Kambhampati:
Action-Model Based Multi-agent Plan Recognition. 377-385 - Angela Eigenstetter, Björn Ommer:
Visual Recognition using Embedded Feature Selection for Curvature Self-Similarity. 386-394 - Nikhil Bhat, Ciamac C. Moallemi, Vivek F. Farias:
Non-parametric Approximate Dynamic Programming via the Kernel Method. 395-403 - Xi Chen, Qihang Lin, Javier Peña:
Optimal Regularized Dual Averaging Methods for Stochastic Optimization. 404-412 - Emanuele Coviello, Antoni B. Chan, Gert R. G. Lanckriet:
The variational hierarchical EM algorithm for clustering hidden Markov models. 413-421 - Chong Wang, David M. Blei:
Truncation-free Online Variational Inference for Bayesian Nonparametric Models. 422-430 - Hyun Soo Park, Eakta Jain, Yaser Sheikh:
3D Social Saliency from Head-mounted Cameras. 431-439 - Peter Kontschieder, Samuel Rota Bulò, Antonio Criminisi, Pushmeet Kohli, Marcello Pelillo, Horst Bischof:
Context-Sensitive Decision Forests for Object Detection. 440-448 - Grégoire Montavon, Katja Hansen, Siamac Fazli, Matthias Rupp, Franziska Biegler, Andreas Ziehe, Alexandre Tkatchenko, O. Anatole von Lilienfeld, Klaus-Robert Müller:
Learning Invariant Representations of Molecules for Atomization Energy Prediction. 449-457 - Joan Fruitet, Alexandra Carpentier, Rémi Munos, Maureen Clerc:
Bandit Algorithms boost Brain Computer Interfaces for motor-task selection of a brain-controlled button. 458-466 - Jeremy C. Weiss, Sriraam Natarajan, David Page:
Multiplicative Forests for Continuous-Time Processes. 467-475 - Jenna Wiens, John V. Guttag, Eric Horvitz:
Patient Risk Stratification for Hospital-Associated C. diff as a Time-Series Classification Task. 476-484 - Tianbao Yang, Yufeng Li, Mehrdad Mahdavi, Rong Jin, Zhi-Hua Zhou:
Nyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison. 485-493 - Amit Daniely, Sivan Sabato, Shai Shalev-Shwartz:
Multiclass Learning Approaches: A Theoretical Comparison with Implications. 494-502 - Mehrdad Mahdavi, Tianbao Yang, Rong Jin, Shenghuo Zhu, Jinfeng Yi:
Stochastic Gradient Descent with Only One Projection. 503-511 - Dmitri B. Chklovskii, Daniel Soudry:
"Neuronal spike generation mechanism as an oversampling, noise-shaping A-to-D converter". 512-520 - Pietro Di Lena, Pierre Baldi, Ken Nagata:
Deep Spatio-Temporal Architectures and Learning for Protein Structure Prediction. 521-529 - Ognjen Arandjelovic:
Assessing Blinding in Clinical Trials. 530-538 - Suvrit Sra:
Scalable nonconvex inexact proximal splitting. 539-547 - Julian J. McAuley, Jure Leskovec:
Learning to Discover Social Circles in Ego Networks. 548-556 - Li-Ping Liu, Thomas G. Dietterich:
A Conditional Multinomial Mixture Model for Superset Label Learning. 557-565 - Tony Jebara, Anna Choromanska:
Majorization for CRFs and Latent Likelihoods. 566-574 - Kumar Sricharan, Alfred O. Hero III:
Ensemble weighted kernel estimators for multivariate entropy estimation. 575-583 - Paul Vernaza, Drew Bagnell:
Efficient high dimensional maximum entropy modeling via symmetric partition functions. 584-592 - Xiaofeng Ren, Liefeng Bo:
Discriminatively Trained Sparse Code Gradients for Contour Detection. 593-601 - Mohsen Hejrati, Deva Ramanan:
Analyzing 3D Objects in Cluttered Images. 602-610 - Zhihua Zhang, Bojun Tu:
Nonconvex Penalization Using Laplace Exponents and Concave Conjugates. 611-619 - Sanja Fidler, Sven J. Dickinson, Raquel Urtasun:
3D Object Detection and Viewpoint Estimation with a Deformable 3D Cuboid Model. 620-628 - Karthik Mohan, Michael Jae-Yoon Chung, Seungyeop Han, Daniela M. Witten, Su-In Lee, Maryam Fazel:
Structured Learning of Gaussian Graphical Models. 629-637 - Elad Hazan, Zohar Shay Karnin:
A Polylog Pivot Steps Simplex Algorithm for Classification. 638-646 - Kevin D. Tang, Vignesh Ramanathan, Li Fei-Fei, Daphne Koller:
Shifting Weights: Adapting Object Detectors from Image to Video. 647-655 - Shusen Wang, Zhihua Zhang:
A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound. 656-664 - Richard Socher, Brody Huval, Bharath Putta Bath, Christopher D. Manning, Andrew Y. Ng:
Convolutional-Recursive Deep Learning for 3D Object Classification. 665-673 - David López-Paz, José Miguel Hernández-Lobato, Bernhard Schölkopf:
Semi-Supervised Domain Adaptation with Non-Parametric Copulas. 674-682 - Firdaus Janoos, Weichang Li, Niranjan A. Subrahmanya, István Ákos Mórocz, William M. Wells III:
Identification of Recurrent Patterns in the Activation of Brain Networks. 683-691 - Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Marthinus Christoffel du Plessis, Song Liu, Ichiro Takeuchi:
Density-Difference Estimation. 692-700 - Qiang Liu, Jian Peng, Alexander Ihler:
Variational Inference for Crowdsourcing. 701-709 - Vinayak A. Rao, Yee Whye Teh:
MCMC for continuous-time discrete-state systems. 710-718 - Pieter-Jan Kindermans, Hannes Verschore, David Verstraeten, Benjamin Schrauwen:
A P300 BCI for the Masses: Prior Information Enables Instant Unsupervised Spelling. 719-727 - Elad Mezuman, Yair Weiss:
Learning about Canonical Views from Internet Image Collections. 728-736 - Assaf Glazer, Michael Lindenbaum, Shaul Markovitch:
Learning High-Density Regions for a Generalized Kolmogorov-Smirnov Test in High-Dimensional Data. 737-745 - Emily B. Fox, David B. Dunson:
Multiresolution Gaussian Processes. 746-754 - Jianxiong Xiao, Bryan C. Russell, Antonio Torralba:
Localizing 3D cuboids in single-view images. 755-763 - Peder A. Olsen, Figen Öztoprak, Jorge Nocedal, Steven J. Rennie:
Newton-Like Methods for Sparse Inverse Covariance Estimation. 764-772 - Gary B. Huang, Marwan A. Mattar, Honglak Lee, Erik G. Learned-Miller:
Learning to Align from Scratch. 773-781 - Stefan Habenschuss, Johannes Bill, Bernhard Nessler:
Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints. 782-790 - Nan Li, Longin Jan Latecki:
Clustering Aggregation as Maximum-Weight Independent Set. 791-799 - Marcelo Fiori, Pablo Musé, Guillermo Sapiro:
Topology Constraints in Graphical Models. 800-808 - Han Liu, Fang Han, Cun-Hui Zhang:
Transelliptical Graphical Models. 809-817 - Weilong Yang, Yang Wang, Arash Vahdat, Greg Mori:
Kernel Latent SVM for Visual Recognition. 818-826 - Erik Talvitie:
Learning Partially Observable Models Using Temporally Abstract Decision Trees. 827-835 - Jason D. Lee, Yuekai Sun, Michael A. Saunders:
Proximal Newton-type methods for convex optimization. 836-844 - Bo Liu, Sridhar Mahadevan, Ji Liu:
Regularized Off-Policy TD-Learning. 845-853 - Ko-Jen Hsiao, Kevin S. Xu, Jeff Calder, Alfred O. Hero III:
Multi-criteria Anomaly Detection using Pareto Depth Analysis. 854-862 - Jake V. Bouvrie, Jean-Jacques E. Slotine:
Synchronization can Control Regularization in Neural Systems via Correlated Noise Processes. 863-871 - Tingni Sun, Cun-Hui Zhang:
Calibrated Elastic Regularization in Matrix Completion. 872-880 - Uri Maoz, Shengxuan Ye, Ian B. Ross, Adam N. Mamelak, Christof Koch:
Predicting Action Content On-Line and in Real Time before Action Onset - an Intracranial Human Study. 881-889 - Bogdan Alexe, Nicolas Heess, Yee Whye Teh, Vittorio Ferrari:
Searching for objects driven by context. 890-898 - Sergey Karayev, Tobias Baumgartner, Mario Fritz, Trevor Darrell:
Timely Object Recognition. 899-907 - Gal Elidan, Cobi Cario:
Nonparanormal Belief Propagation (NPNBP). 908-916 - Ryan Kiros, Csaba Szepesvári:
Deep Representations and Codes for Image Auto-Annotation. 917-925 - Anima Anandkumar, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Yi-Kai Liu:
A Spectral Algorithm for Latent Dirichlet Allocation. 926-934 - Aharon Birnbaum, Shai Shalev-Shwartz:
Learning Halfspaces with the Zero-One Loss: Time-Accuracy Tradeoffs. 935-943 - Rina Foygel, Nathan Srebro, Ruslan Salakhutdinov:
Matrix reconstruction with the local max norm. 944-952 - Anteo Smerieri, François Duport, Yvan Paquot, Benjamin Schrauwen, Marc Haelterman, Serge Massar:
Analog readout for optical reservoir computers. 953-961 - Stephen P. Boyd, Corinna Cortes, Mehryar Mohri, Ana Radovanovic:
Accuracy at the Top. 962-970 - Andrew Delong, Olga Veksler, Anton Osokin, Yuri Boykov:
Minimizing Sparse High-Order Energies by Submodular Vertex-Cover. 971-979 - Shinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. Derin Babacan:
Perfect Dimensionality Recovery by Variational Bayesian PCA. 980-988 - Nicolò Cesa-Bianchi, Pierre Gaillard, Gábor Lugosi, Gilles Stoltz:
Mirror Descent Meets Fixed Share (and feels no regret). 989-997 - Kamalika Chaudhuri, Anand D. Sarwate, Kaushik Sinha:
Near-optimal Differentially Private Principal Components. 998-1006 - James Robert Lloyd, Peter Orbanz, Zoubin Ghahramani, Daniel M. Roy:
Random function priors for exchangeable arrays with applications to graphs and relational data. 1007-1015 - Edouard Klein, Matthieu Geist, Bilal Piot, Olivier Pietquin:
Inverse Reinforcement Learning through Structured Classification. 1016-1024 - Ashwini Shukla, Aude Billard:
Augmented-SVM: Automatic space partitioning for combining multiple non-linear dynamics. 1025-1033 - Arthur Guez, David Silver, Peter Dayan:
Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search. 1034-1042 - Chi Jin, Liwei Wang:
Dimensionality Dependent PAC-Bayes Margin Bound. 1043-1051 - Animashree Anandkumar, Ragupathyraj Valluvan:
Latent Graphical Model Selection: Efficient Methods for Locally Tree-like Graphs. 1052-1060 - Animashree Anandkumar, Daniel J. Hsu, Furong Huang, Sham M. Kakade:
Learning Mixtures of Tree Graphical Models. 1061-1069 - Mohammad Norouzi, David J. Fleet, Ruslan Salakhutdinov:
Hamming Distance Metric Learning. 1070-1078 - Romain Daniel Cazé, Mark D. Humphries, Boris S. Gutkin:
Spiking and saturating dendrites differentially expand single neuron computation capacity. 1079-1087 - Zhirong Yang, Tele Hao, Onur Dikmen, Xi Chen, Erkki Oja:
Clustering by Nonnegative Matrix Factorization Using Graph Random Walk. 1088-1096 - C. C. Alan Fung, K. Y. Michael Wong, Si Wu:
Delay Compensation with Dynamical Synapses. 1097-1105 - Juan Huo:
A dynamic excitatory-inhibitory network in a VLSI chip for spiking information reregistration. - Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton:
ImageNet Classification with Deep Convolutional Neural Networks. 1106-1114 - Weixin Li, Nuno Vasconcelos:
Recognizing Activities by Attribute Dynamics. 1115-1123 - Chen Chen, Junzhou Huang:
Compressive Sensing MRI with Wavelet Tree Sparsity. 1124-1132 - Lucas Theis, Jascha Sohl-Dickstein, Matthias Bethge:
Training sparse natural image models with a fast Gibbs sampler of an extended state space. 1133-1141 - Aaron Wilson, Alan Fern, Prasad Tadepalli:
A Bayesian Approach for Policy Learning from Trajectory Preference Queries. 1142-1150 - Jingrui He, Hanghang Tong, Qiaozhu Mei, Boleslaw K. Szymanski:
GenDeR: A Generic Diversified Ranking Algorithm. 1151-1159 - Claudio Gentile, Francesco Orabona:
On Multilabel Classification and Ranking with Partial Feedback. 1160-1168 - Vinay Jethava, Anders Martinsson, Chiranjib Bhattacharyya, Devdatt P. Dubhashi:
"The Lovasz $\theta$ function, SVMs and finding large dense subgraphs". 1169-1177 - Sergey Feldman, Maya R. Gupta, Bela A. Frigyik:
Multi-Task Averaging. 1178-1186 - Sourish Chaudhuri, Bhiksha Raj:
Unsupervised Structure Discovery for Semantic Analysis of Audio. 1187-1195 - Yali Wang, Brahim Chaib-draa:
A Marginalized Particle Gaussian Process Regression. 1196-1204 - Yunchao Gong, Sanjiv Kumar, Vishal Verma, Svetlana Lazebnik:
Angular Quantization-based Binary Codes for Fast Similarity Search. 1205-1213 - Arthur Gretton, Bharath K. Sriperumbudur, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu:
Optimal kernel choice for large-scale two-sample tests. 1214-1222 - Ben Recht, Christopher Ré, Joel A. Tropp, Victor Bittorf:
Factoring nonnegative matrices with linear programs. 1223-1231 - Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Quoc V. Le, Mark Z. Mao, Marc'Aurelio Ranzato, Andrew W. Senior, Paul A. Tucker, Ke Yang, Andrew Y. Ng:
Large Scale Distributed Deep Networks. 1232-1240 - Yanyan Lan, Jiafeng Guo, Xueqi Cheng, Tie-Yan Liu:
Statistical Consistency of Ranking Methods in A Rank-Differentiable Probability Space. 1241-1249 - Won Hwa Kim, Deepti Pachauri, Charles R. Hatt, Moo K. Chung, Sterling C. Johnson, Vikas Singh:
Wavelet based multi-scale shape features on arbitrary surfaces for cortical thickness discrimination. 1250-1258 - Aaron Defazio, Tibério S. Caetano:
A Convex Formulation for Learning Scale-Free Networks via Submodular Relaxation. 1259-1267 - Arnak S. Dalalyan, Yin Chen:
Fused sparsity and robust estimation for linear models with unknown variance. 1268-1276 - Yanping Huang, Abram L. Friesen, Timothy D. Hanks, Michael N. Shadlen, Rajesh P. N. Rao:
How Prior Probability Influences Decision Making: A Unifying Probabilistic Model. 1277-1285 - Hua Wang, Feiping Nie, Heng Huang, Jingwen Yan, Sungeun Kim, Shannon L. Risacher, Andrew J. Saykin, Li Shen:
High-Order Multi-Task Feature Learning to Identify Longitudinal Phenotypic Markers for Alzheimer's Disease Progression Prediction. 1286-1294 - Kosuke Fukumasu, Koji Eguchi, Eric P. Xing:
Symmetric Correspondence Topic Models for Multilingual Text Analysis. 1295-1303 - Michael C. Hughes, Emily B. Fox, Erik B. Sudderth:
Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data. 1304-1312 - Xue-Xin Wei, Alan A. Stocker:
Efficient coding provides a direct link between prior and likelihood in perceptual Bayesian inference. 1313-1321 - Maksims Volkovs, Richard S. Zemel:
Efficient Sampling for Bipartite Matching Problems. 1322-1330 - Marius Pachitariu, Maneesh Sahani:
Learning visual motion in recurrent neural networks. 1331-1339 - Jiarong Jiang, Adam R. Teichert, Hal Daumé III, Jason Eisner:
Learned Prioritization for Trading Off Accuracy and Speed. 1340-1348 - Amir Massoud Farahmand, Doina Precup:
Value Pursuit Iteration. 1349-1357 - Xaq Pitkow:
"Compressive neural representation of sparse, high-dimensional probabilities". 1358-1366 - Eunho Yang, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu:
Graphical Models via Generalized Linear Models. 1367-1375 - Henrik Ohlsson, Allen Y. Yang, Roy Dong, S. Shankar Sastry:
CPRL -- An Extension of Compressive Sensing to the Phase Retrieval Problem. 1376-1384 - Yi Zhen, Dit-Yan Yeung:
Co-Regularized Hashing for Multimodal Data. 1385-1393 - Xavier Bresson, Thomas Laurent, David Uminsky, James H. von Brecht:
Convergence and Energy Landscape for Cheeger Cut Clustering. 1394-1402 - Zahra Zamani, Scott Sanner, Pascal Poupart, Kristian Kersting:
Symbolic Dynamic Programming for Continuous State and Observation POMDPs. 1403-1411 - Lei Shi:
Bayesian Probabilistic Co-Subspace Addition. 1412-1420 - Thanh T. Ngo, Yousef Saad:
Scaled Gradients on Grassmann Manifolds for Matrix Completion. 1421-1429 - Chris Hinrichs, Vikas Singh, Jiming Peng, Sterling C. Johnson:
Q-MKL: Matrix-induced Regularization in Multi-Kernel Learning with Applications to Neuroimaging. 1430-1438 - John C. Duchi, Michael I. Jordan, Martin J. Wainwright:
Privacy Aware Learning. 1439-1447 - John C. Duchi, Michael I. Jordan, Martin J. Wainwright, Andre Wibisono:
Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods. 1448-1456 - Odalric-Ambrym Maillard:
Hierarchical Optimistic Region Selection driven by Curiosity. 1457-1465 - Andreas Argyriou, Rina Foygel, Nathan Srebro:
Sparse Prediction with the $k$-Support Norm. 1466-1474 - Hemant Tyagi, Volkan Cevher:
Active Learning of Multi-Index Function Models. 1475-1483 - Koby Crammer, Yishay Mansour:
Learning Multiple Tasks using Shared Hypotheses. 1484-1492 - André da Motta Salles Barreto, Doina Precup, Joelle Pineau:
On-line Reinforcement Learning Using Incremental Kernel-Based Stochastic Factorization. 1493-1501 - Kei Wakabayashi, Takao Miura:
Forward-Backward Activation Algorithm for Hierarchical Hidden Markov Models. 1502-1510 - Yuchen Zhang, John C. Duchi, Martin J. Wainwright:
Communication-Efficient Algorithms for Statistical Optimization. 1511-1519 - Daniel J. Hsu, Sham M. Kakade, Percy Liang:
Identifiability and Unmixing of Latent Parse Trees. 1520-1528 - Francois Caron, Yee Whye Teh:
Bayesian nonparametric models for ranked data. 1529-1537 - Yao-Nan Chen, Hsuan-Tien Lin:
Feature-aware Label Space Dimension Reduction for Multi-label Classification. 1538-1546 - Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright:
Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions. 1547-1555 - Tatsuya Harada, Yasuo Kuniyoshi:
Graphical Gaussian Vector for Image Categorization. 1556-1564 - XianXing Zhang, Lawrence Carin:
Joint Modeling of a Matrix with Associated Text via Latent Binary Features. 1565-1573 - Jesús Cid-Sueiro:
Proper losses for learning from partial labels. 1574-1582 - Benjamin T. Rolfs, Bala Rajaratnam, Dominique Guillot, Ian Wong, Arian Maleki:
Iterative Thresholding Algorithm for Sparse Inverse Covariance Estimation. 1583-1591 - Abhimanyu Das, Anirban Dasgupta, Ravi Kumar:
Selecting Diverse Features via Spectral Regularization. 1592-1600 - Qixia Jiang, Jun Zhu, Maosong Sun, Eric P. Xing:
Monte Carlo Methods for Maximum Margin Supervised Topic Models. 1601-1609 - Jun Wang, Alexandros Kalousis, Adam Woznica:
Parametric Local Metric Learning for Nearest Neighbor Classification. 1610-1618 - Nicolò Cesa-Bianchi, Claudio Gentile, Fabio Vitale, Giovanni Zappella:
A Linear Time Active Learning Algorithm for Link Classification. 1619-1627 - Miguel Lázaro-Gredilla:
Bayesian Warped Gaussian Processes. 1628-1636 - Rina Foygel, Michael Horrell, Mathias Drton, John D. Lafferty:
Nonparametric Reduced Rank Regression. 1637-1645 - Risi Kondor, Walter Dempsey:
Multiresolution analysis on the symmetric group. 1646-1654 - Weihao Kong, Wu-Jun Li:
Isotropic Hashing. 1655-1663 - Deepak Venugopal, Vibhav Gogate:
On Lifting the Gibbs Sampling Algorithm. 1664-1672 - Vijay Mahadevan, Nuno Vasconcelos:
On the connections between saliency and tracking. 1673-1681 - Martha White, Yaoliang Yu, Xinhua Zhang, Dale Schuurmans:
Convex Multi-view Subspace Learning. 1682-1690 - Lars Buesing, Jakob H. Macke, Maneesh Sahani:
Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. 1691-1699 - Tim van Erven, Peter D. Grünwald, Mark D. Reid, Robert C. Williamson:
Mixability in Statistical Learning. 1700-1708 - Christian Mayr, Paul Stärke, Johannes Partzsch, René Schüffny, Love Cederstroem, Yao Shuai, Nan Du, Heidemarie Schmidt:
Waveform Driven Plasticity in BiFeO3 Memristive Devices: Model and Implementation. 1709-1717 - Karol Gregor, Dmitri B. Chklovskii:
A lattice filter model of the visual pathway. 1718-1726 - Sung Ju Hwang, Kristen Grauman, Fei Sha:
Semantic Kernel Forests from Multiple Taxonomies. 1727-1735 - Zhitang Chen, Kun Zhang, Laiwan Chan:
Causal discovery with scale-mixture model for spatiotemporal variance dependencies. 1736-1744 - Daniel Zoran, Yair Weiss:
"Natural Images, Gaussian Mixtures and Dead Leaves". 1745-1753 - David P. Wipf, Yi Wu:
Dual-Space Analysis of the Sparse Linear Model. 1754-1762 - Christoph Sawade, Niels Landwehr, Tobias Scheffer:
Active Comparison of Prediction Models. 1763-1771 - Ronald Ortner, Daniil Ryabko:
Online Regret Bounds for Undiscounted Continuous Reinforcement Learning. 1772-1780 - Chao Zhang, Lei Zhang, Jieping Ye:
Generalization Bounds for Domain Adaptation. - Jinfeng Yi, Rong Jin, Anil K. Jain, Shaili Jain, Tianbao Yang:
Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning. 1781-1789 - Peter Sollich, Simon R. F. Ashton:
Learning curves for multi-task Gaussian process regression. 1790-1798 - Alexander Lorbert, Peter J. Ramadge:
Kernel Hyperalignment. 1799-1807 - Abner Guzmán-Rivera, Dhruv Batra, Pushmeet Kohli:
Multiple Choice Learning: Learning to Produce Multiple Structured Outputs. 1808-1816 - Mathieu Sinn, Bei Chen:
Mixing Properties of Conditional Markov Chains with Unbounded Feature Functions. 1817-1825 - Florian T. Pokorny, Carl Henrik Ek, Hedvig Kjellström, Danica Kragic:
Persistent Homology for Learning Densities with Bounded Support. 1826-1834 - Bruno Scherrer, Boris Lesner:
On the Use of Non-Stationary Policies for Stationary Infinite-Horizon Markov Decision Processes. 1835-1843 - Sander M. Bohté:
Efficient Spike-Coding with Multiplicative Adaptation in a Spike Response Model. 1844-1852 - David Belanger, Alexandre Passos, Sebastian Riedel, Andrew McCallum:
MAP Inference in Chains using Column Generation. 1853-1861 - Francisco J. R. Ruiz, Isabel Valera, Carlos Blanco, Fernando Pérez-Cruz:
Bayesian Nonparametric Modeling of Suicide Attempts. 1862-1870 - Antonino Freno, Mikaela Keller, Marc Tommasi:
Fiedler Random Fields: A Large-Scale Spectral Approach to Statistical Network Modeling. 1871-1879 - Jaldert O. Rombouts, Sander M. Bohté, Pieter R. Roelfsema:
Neurally Plausible Reinforcement Learning of Working Memory Tasks. 1880-1888 - Richard G. Gibson, Neil Burch, Marc Lanctot, Duane Szafron:
Efficient Monte Carlo Counterfactual Regret Minimization in Games with Many Player Actions. 1889-1897 - Francesca Petralia, Vinayak A. Rao, David B. Dunson:
Repulsive Mixtures. 1898-1906 - Jonathan W. Pillow, James G. Scott:
Fully Bayesian inference for neural models with negative-binomial spiking. 1907-1915 - Tivadar Papai, Henry A. Kautz, Daniel Stefankovic:
Slice Normalized Dynamic Markov Logic Networks. 1916-1924 - Mélanie Rey, Volker Roth:
Meta-Gaussian Information Bottleneck. 1925-1933 - Yung-Kyun Noh, Frank Chongwoo Park, Daniel D. Lee:
Diffusion Decision Making for Adaptive k-Nearest Neighbor Classification. 1934-1942 - Maayan Harel, Shie Mannor:
The Perturbed Variation. 1943-1951 - Konstantinos I. Tsianos, Sean F. Lawlor, Michael G. Rabbat:
Communication/Computation Tradeoffs in Consensus-Based Distributed Optimization. 1952-1960 - Simon M. J. Lyons, Amos J. Storkey, Simo Särkkä:
The Coloured Noise Expansion and Parameter Estimation of Diffusion Processes. 1961-1969 - Alexandra Carpentier, Odalric-Ambrym Maillard:
Online allocation and homogeneous partitioning for piecewise constant mean-approximation. 1970-1978 - Xianghang Liu, James Petterson, Tibério S. Caetano:
Learning as MAP Inference in Discrete Graphical Models. 1979-1987 - Shaul Druckmann, Tao Hu, Dmitri B. Chklovskii:
A mechanistic model of early sensory processing based on subtracting sparse representations. 1988-1996 - Pinghua Gong, Jieping Ye, Changshui Zhang:
Multi-Stage Multi-Task Feature Learning. 1997-2005 - Soumya Ghosh, Erik B. Sudderth, Matthew Loper, Michael J. Black:
From Deformations to Parts: Motion-based Segmentation of 3D Objects. 2006-2014 - Hyunsin Park, Sungrack Yun, Sanghyuk Park, Jongmin Kim, Chang D. Yoo:
Phoneme Classification using Constrained Variational Gaussian Process Dynamical System. 2015-2023 - Evan Archer, Il Memming Park, Jonathan W. Pillow:
Bayesian estimation of discrete entropy with mixtures of stick-breaking priors. 2024-2032 - Søren Hauberg, Oren Freifeld, Michael J. Black:
A Geometric take on Metric Learning. 2033-2041 - Aaron W. Dennis, Dan Ventura:
Learning the Architecture of Sum-Product Networks Using Clustering on Variables. 2042-2050 - Yair Wiener, Ran El-Yaniv:
Pointwise Tracking the Optimal Regression Function. 2051-2059 - Francois Caron:
Bayesian nonparametric models for bipartite graphs. 2060-2068 - Daniil Ryabko, Jérémie Mary:
Reducing statistical time-series problems to binary classification. 2069-2077 - Katherine Chen, Michael Bowling:
Tractable Objectives for Robust Policy Optimization. 2078-2086 - Harish G. Ramaswamy, Shivani Agarwal:
Classification Calibration Dimension for General Multiclass Losses. 2087-2095 - Po-Ling Loh, Martin J. Wainwright:
Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses. 2096-2104 - Neil Houlsby, José Miguel Hernández-Lobato, Ferenc Huszar, Zoubin Ghahramani:
Collaborative Gaussian Processes for Preference Learning. 2105-2113 - Joachim Giesen, Jens K. Müller, Sören Laue, Sascha Swiercy:
Approximating Concavely Parameterized Optimization Problems. 2114-2122 - Kenji Fukumizu, Chenlei Leng:
Gradient-based kernel method for feature extraction and variable selection. 2123-2131 - Pradeep Shenoy, Angela J. Yu:
Strategic Impatience in Go/NoGo versus Forced-Choice Decision-Making. 2132-2140 - Qirong Ho, Junming Yin, Eric P. Xing:
On Triangular versus Edge Representations --- Towards Scalable Modeling of Networks. 2141-2149 - Alexander Rakhlin, Ohad Shamir, Karthik Sridharan:
Relax and Randomize : From Value to Algorithms. 2150-2158 - Nishant A. Mehta, Dongryeol Lee, Alexander G. Gray:
Minimax Multi-Task Learning and a Generalized Loss-Compositional Paradigm for MTL. 2159-2167 - Borja Balle, Mehryar Mohri:
Spectral Learning of General Weighted Automata via Constrained Matrix Completion. 2168-2176 - Zhuo Wang, Alan A. Stocker, Daniel D. Lee:
"Optimal Neural Tuning Curves for Arbitrary Stimulus Distributions: Discrimax, Infomax and Minimum $L_p$ Loss". 2177-2185 - Abdeslam Boularias, Oliver Kroemer, Jan Peters:
Algorithms for Learning Markov Field Policies. 2186-2194 - Edward Challis, David Barber:
Affine Independent Variational Inference. 2195-2203 - Dengyong Zhou, John C. Platt, Sumit Basu, Yi Mao:
Learning from the Wisdom of Crowds by Minimax Entropy. 2204-2212 - Yudong Chen, Sujay Sanghavi, Huan Xu:
Clustering Sparse Graphs. 2213-2221 - Marc G. Bellemare, Joel Veness, Michael Bowling:
Sketch-Based Linear Value Function Approximation. 2222-2230 - Nitish Srivastava, Ruslan Salakhutdinov:
Multimodal Learning with Deep Boltzmann Machines. 2231-2239 - Zuoguan Wang, Siwei Lyu, Gerwin Schalk, Qiang Ji:
Learning with Target Prior. 2240-2248 - Nicholas J. Foti, Sinead Williamson:
Slice sampling normalized kernel-weighted completely random measure mixture models. 2249-2257 - Prem Gopalan, David M. Mimno, Sean Gerrish, Michael J. Freedman, David M. Blei:
Scalable Inference of Overlapping Communities. 2258-2266 - Shiva Prasad Kasiviswanathan, Huahua Wang, Arindam Banerjee, Prem Melville:
Online L1-Dictionary Learning with Application to Novel Document Detection. 2267-2275 - Francisco Pereira, Matthew M. Botvinick:
A systematic approach to extracting semantic information from functional MRI data. 2276-2284 - Jacquelyn A. Shelton, Philip Sterne, Jörg Bornschein, Abdul-Saboor Sheikh, Jörg Lücke:
Why MCA? Nonlinear sparse coding with spike-and-slab prior for neurally plausible image encoding. 2285-2293 - Ralph Bourdoukan, David G. T. Barrett, Christian K. Machens, Sophie Denève:
Learning optimal spike-based representations. 2294-2302 - Maksims Volkovs, Richard S. Zemel:
Collaborative Ranking With 17 Parameters. 2303-2311 - Nisheeth Srivastava, Paul R. Schrater:
Rational inference of relative preferences. 2312-2320 - Hiroki Terashima, Masato Okada:
The topographic unsupervised learning of natural sounds in the auditory cortex. 2321-2329 - Amy Greenwald, Jiacui Li, Eric Sodomka:
Approximating Equilibria in Sequential Auctions with Incomplete Information and Multi-Unit Demand. 2330-2338 - Cho-Jui Hsieh, Inderjit S. Dhillon, Pradeep Ravikumar, Arindam Banerjee:
A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation. 2339-2347 - Moritz Hardt, Katrina Ligett, Frank McSherry:
A Simple and Practical Algorithm for Differentially Private Data Release. 2348-2356 - Mijung Park, Jonathan W. Pillow:
Bayesian active learning with localized priors for fast receptive field characterization. 2357-2365 - Tsuyoshi Ueno, Kohei Hayashi, Takashi Washio, Yoshinobu Kawahara:
Weighted Likelihood Policy Search with Model Selection. 2366-2374 - Yunlong He, Yanjun Qi, Koray Kavukcuoglu, Haesun Park:
Learning the Dependency Structure of Latent Factors. 2375-2383 - Sanjeev Arora, Rong Ge, Ankur Moitra, Sushant Sachdeva:
"Provable ICA with Unknown Gaussian Noise, with Implications for Gaussian Mixtures and Autoencoders". 2384-2392 - Alexander G. Schwing, Tamir Hazan, Marc Pollefeys, Raquel Urtasun:
Globally Convergent Dual MAP LP Relaxation Solvers using Fenchel-Young Margins. 2393-2401 - Argyris Kalogeratos, Aristidis Likas:
Dip-means: an incremental clustering method for estimating the number of clusters. 2402-2410 - Matthew J. Streeter, H. Brendan McMahan:
No-Regret Algorithms for Unconstrained Online Convex Optimization. 2411-2419 - Siddharth Gopal, Yiming Yang, Bing Bai, Alexandru Niculescu-Mizil:
Bayesian models for Large-scale Hierarchical Classification. 2420-2428 - Mert Pilanci, Laurent El Ghaoui, Venkat Chandrasekaran:
Recovery of Sparse Probability Measures via Convex Programming. 2429-2437 - Hachem Kadri, Alain Rakotomamonjy, Francis R. Bach, Philippe Preux:
Multiple Operator-valued Kernel Learning. 2438-2446 - Ulugbek Kamilov, Sundeep Rangan, Alyson K. Fletcher, Michael Unser:
Approximate Message Passing with Consistent Parameter Estimation and Applications to Sparse Learning. 2447-2455 - Ruslan Salakhutdinov, Geoffrey E. Hinton:
A Better Way to Pretrain Deep Boltzmann Machines. 2456-2464 - David Balduzzi, Michel Besserve:
Towards a learning-theoretic analysis of spike-timing dependent plasticity. 2465-2473 - Guillermo D. Cañas, Tomaso A. Poggio, Lorenzo Rosasco:
Learning Manifolds with K-Means and K-Flats. 2474-2482 - Sahand Negahban, Sewoong Oh, Devavrat Shah:
Iterative ranking from pair-wise comparisons. 2483-2491 - Yaoliang Yu, Özlem Aslan, Dale Schuurmans:
A Polynomial-time Form of Robust Regression. 2492-2500 - Guillermo D. Cañas, Lorenzo Rosasco:
Learning Probability Measures with respect to Optimal Transport Metrics. 2501-2509 - Weiwei Cheng, Eyke Hüllermeier, Willem Waegeman, Volkmar Welker:
Label Ranking with Partial Abstention based on Thresholded Probabilistic Models. 2510-2518 - Amadou Ba, Mathieu Sinn, Yannig Goude, Pascal Pompey:
Adaptive Learning of Smoothing Functions: Application to Electricity Load Forecasting. 2519-2527 - Shay B. Cohen, Michael Collins:
Tensor Decomposition for Fast Parsing with Latent-Variable PCFGs. 2528-2536 - Toke Jansen Hansen, Michael W. Mahoney:
Semi-supervised Eigenvectors for Locally-biased Learning. 2537-2545 - Han Liu, John D. Lafferty, Larry A. Wasserman:
Exponential Concentration for Mutual Information Estimation with Application to Forests. 2546-2554 - Mingyuan Zhou, Lawrence Carin:
Augment-and-Conquer Negative Binomial Processes. 2555-2563 - Trung Thanh Nguyen, Tomi Silander, Tze-Yun Leong:
Transferring Expectations in Model-based Reinforcement Learning. 2564-2572 - Jason L. Pacheco, Erik B. Sudderth:
Minimization of Continuous Bethe Approximations: A Positive Variation. 2573-2581 - Dor Kedem, Stephen Tyree, Kilian Q. Weinberger, Fei Sha, Gert R. G. Lanckriet:
Non-linear Metric Learning. 2582-2590 - Michael J. Paul, Mark Dredze:
Factorial LDA: Sparse Multi-Dimensional Text Models. 2591-2599 - Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön:
Ancestor Sampling for Particle Gibbs. 2600-2608 - Charles Blundell, Katherine A. Heller, Jeffrey M. Beck:
Modelling Reciprocating Relationships with Hawkes Processes. 2609-2617 - Marc Peter Deisenroth, Shakir Mohamed:
Expectation Propagation in Gaussian Process Dynamical Systems. 2618-2626 - Stephen Becker, Mohamed-Jalal Fadili:
A quasi-Newton proximal splitting method. 2627-2635 - Demba E. Ba, Behtash Babadi, Patrick L. Purdon, Emery N. Brown:
Exact and Stable Recovery of Sequences of Signals with Sparse Increments via Differential _1-Minimization. 2636-2644 - Morteza Ibrahimi, Adel Javanmard, Benjamin Van Roy:
Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems. 2645-2653 - Ashish Kapoor, Raajay Viswanathan, Prateek Jain:
Multilabel Classification using Bayesian Compressed Sensing. 2654-2662 - Stephen H. Bach, Matthias Broecheler, Lise Getoor, Dianne P. O'Leary:
Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization. 2663-2671 - Nicolas Le Roux, Mark Schmidt, Francis R. Bach:
A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets. 2672-2680 - Kevin G. Jamieson, Robert D. Nowak, Benjamin Recht:
Query Complexity of Derivative-Free Optimization. 2681-2689 - Adam Coates, Andrej Karpathy, Andrew Y. Ng:
Emergence of Object-Selective Features in Unsupervised Feature Learning. 2690-2698 - Falk Lieder, Thomas L. Griffiths, Noah D. Goodman:
"Burn-in, bias, and the rationality of anchoring". 2699-2707 - Michael Bryant, Erik B. Sudderth:
Truly Nonparametric Online Variational Inference for Hierarchical Dirichlet Processes. 2708-2716 - Hugo Larochelle, Stanislas Lauly:
A Neural Autoregressive Topic Model. 2717-2725 - Thomas Furmston, David Barber:
A Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes. 2726-2734 - Seong-Hwan Jun, Liangliang Wang, Alexandre Bouchard-Côté:
Entangled Monte Carlo. 2735-2743 - Jennifer Gillenwater, Alex Kulesza, Ben Taskar:
Near-Optimal MAP Inference for Determinantal Point Processes. 2744-2752 - S. Derin Babacan, Shinichi Nakajima, Minh N. Do:
Probabilistic Low-Rank Subspace Clustering. 2753-2761 - Sean Gerrish, David M. Blei:
How They Vote: Issue-Adjusted Models of Legislative Behavior. 2762-2770 - Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman:
Density Propagation and Improved Bounds on the Partition Function. 2771-2779 - Alex Flint, Matthew B. Blaschko:
Perceptron Learning of SAT. 2780-2788 - Nan Du, Le Song, Alexander J. Smola, Ming Yuan:
Learning Networks of Heterogeneous Influence. 2789-2797 - Youssef Mroueh, Tomaso A. Poggio, Lorenzo Rosasco, Jean-Jacques E. Slotine:
Multiclass Learning with Simplex Coding. 2798-2806 - Amr Ahmed, Sujith Ravi, Shravan M. Narayanamurthy, Alexander J. Smola:
FastEx: Hash Clustering with Exponential Families. 2807-2815 - Peter M. Krafft, Juston Moore, Bruce A. Desmarais, Hanna M. Wallach:
Topic-Partitioned Multinetwork Embeddings. 2816-2824 - Andriy Mnih, Yee Whye Teh:
Learning Label Trees for Probabilistic Modelling of Implicit Feedback. 2825-2833 - Oriol Vinyals, Yangqing Jia, Li Deng, Trevor Darrell:
Learning with Recursive Perceptual Representations. 2834-2842 - Emile Richard, Stéphane Gaïffas, Nicolas Vayatis:
Link Prediction in Graphs with Autoregressive Features. 2843-2851 - Dan C. Ciresan, Alessandro Giusti, Luca Maria Gambardella, Jürgen Schmidhuber:
Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images. 2852-2860 - Lloyd T. Elliott, Yee Whye Teh:
Scalable imputation of genetic data with a discrete fragmentation-coagulation process. 2861-2869 - Samory Kpotufe, Abdeslam Boularias:
Gradient Weights help Nonparametric Regressors. 2870-2878 - Mark Herbster, Stephen Pasteris, Fabio Vitale:
Online Sum-Product Computation Over Trees. 2879-2887 - Ben Calderhead, Mátyás A. Sustik:
Sparse Approximate Manifolds for Differential Geometric MCMC. 2888-2896 - James Hensman, Magnus Rattray, Neil D. Lawrence:
Fast Variational Inference in the Conjugate Exponential Family. 2897-2905 - Bonnie Kirkpatrick, Alexandre Bouchard-Côté:
Bayesian Pedigree Analysis using Measure Factorization. 2906-2914 - Xinhua Zhang, Yaoliang Yu, Dale Schuurmans:
Accelerated Training for Matrix-norm Regularization: A Boosting Approach. 2915-2923 - Vasiliy Karasev, Alessandro Chiuso, Stefano Soatto:
Controlled Recognition Bounds for Visual Learning and Exploration. 2924-2932 - Sejong Yoon, Vladimir Pavlovic:
Distributed Probabilistic Learning for Camera Networks with Missing Data. 2933-2941 - Rishabh K. Iyer, Jeff A. Bilmes:
Submodular-Bregman and the Lovász-Bregman Divergences with Applications. 2942-2950 - Nilesh N. Dalvi, Aditya G. Parameswaran, Vibhor Rastogi:
Minimizing Uncertainty in Pipelines. 2951-2959 - Jasper Snoek, Hugo Larochelle, Ryan P. Adams:
Practical Bayesian Optimization of Machine Learning Algorithms. 2960-2968 - Dijun Luo, Chris H. Q. Ding, Heng Huang, Feiping Nie:
Forging The Graphs: A Low Rank and Positive Semidefinite Graph Learning Approach. 2969-2977 - Levi Boyles, Max Welling:
The Time-Marginalized Coalescent Prior for Hierarchical Clustering. 2978-2986 - Yu Zhou, Xiang Bai, Wenyu Liu, Longin Jan Latecki:
Fusion with Diffusion for Robust Visual Tracking. 2987-2995 - David A. Knowles, Konstantina Palla, Zoubin Ghahramani:
A nonparametric variable clustering model. 2996-3004 - James Y. Zou, Ryan P. Adams:
Priors for Diversity in Generative Latent Variable Models. 3005-3013 - Pedro A. Ortega, Jordi Grau-Moya, Tim Genewein, David Balduzzi, Daniel A. Braun:
A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function. 3014-3022 - Ofer Meshi, Tommi S. Jaakkola, Amir Globerson:
Convergence Rate Analysis of MAP Coordinate Minimization Algorithms. 3023-3031 - Madalina Fiterau, Artur Dubrawski:
Projection Retrieval for Classification. 3032-3040 - Yan Karklin, Chaitanya Ekanadham, Eero P. Simoncelli:
Hierarchical spike coding of sound. 3041-3049 - Joshua T. Abbott, Joseph L. Austerweil, Thomas L. Griffiths:
Human memory search as a random walk in a semantic network. 3050-3058 - Kevin Swersky, Daniel Tarlow, Ryan P. Adams, Richard S. Zemel, Brendan J. Frey:
Probabilistic n-Choose-k Models for Classification and Ranking. 3059-3067 - Jeffrey M. Beck, Katherine A. Heller, Alexandre Pouget:
Complex Inference in Neural Circuits with Probabilistic Population Codes and Topic Models. 3068-3076 - Dongho Kim, Kee-Eung Kim, Pascal Poupart:
Cost-Sensitive Exploration in Bayesian Reinforcement Learning. 3077-3085 - Xiao-Ming Wu, Zhenguo Li, Anthony Man-Cho So, John Wright, Shih-Fu Chang:
Learning with Partially Absorbing Random Walks. 3086-3094 - Azadeh Khaleghi, Daniil Ryabko:
Locating Changes in Highly Dependent Data with Unknown Number of Change Points. 3095-3103 - Jonathan Huang, Daniel C. Alexander:
Probabilistic Event Cascades for Alzheimer's disease. 3104-3112 - Brett Vintch, Andrew D. Zaharia, J. Anthony Movshon, Eero P. Simoncelli:
Efficient and direct estimation of a neural subunit model for sensory coding. 3113-3121 - Ping Li, Art B. Owen, Cun-Hui Zhang:
One Permutation Hashing. 3122-3130 - Shulin Yang, Liefeng Bo, Jue Wang, Linda G. Shapiro:
Unsupervised Template Learning for Fine-Grained Object Recognition. 3131-3139 - Teodor Mihai Moldovan, Pieter Abbeel:
Risk Aversion in Markov Decision Processes via Near Optimal Chernoff Bounds. 3140-3148 - Mohammad Emtiyaz Khan, Shakir Mohamed, Kevin P. Murphy:
Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression. 3149-3157 - He He, Hal Daumé III, Jason Eisner:
Imitation Learning by Coaching. 3158-3166 - Ke Jiang, Brian Kulis, Michael I. Jordan:
Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models. 3167-3175 - Rodolphe Jenatton, Nicolas Le Roux, Antoine Bordes, Guillaume Obozinski:
A latent factor model for highly multi-relational data. 3176-3184 - Ping Li, Cun-Hui Zhang:
Entropy Estimations Using Correlated Symmetric Stable Random Projections. 3185-3193 - Piyush Rai, Abhishek Kumar, Hal Daumé III:
Simultaneously Leveraging Output and Task Structures for Multiple-Output Regression. 3194-3202 - Yichuan Zhang, Charles Sutton, Amos J. Storkey, Zoubin Ghahramani:
Continuous Relaxations for Discrete Hamiltonian Monte Carlo. 3203-3211 - Will Y. Zou, Andrew Y. Ng, Shenghuo Zhu, Kai Yu:
Deep Learning of Invariant Features via Simulated Fixations in Video. 3212-3220 - Victor Gabillon, Mohammad Ghavamzadeh, Alessandro Lazaric:
Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence. 3221-3229 - Matthew Coudron, Gilad Lerman:
On the Sample Complexity of Robust PCA. 3230-3238 - Matthew F. Der, Lawrence K. Saul:
Latent Coincidence Analysis: A Hidden Variable Model for Distance Metric Learning. 3239-3247 - Robert Gens, Pedro M. Domingos:
Discriminative Learning of Sum-Product Networks. 3248-3256 - Felipe W. Trevizan, Manuela M. Veloso:
Trajectory-Based Short-Sighted Probabilistic Planning. 3257-3265 - Jayadev Acharya, Hirakendu Das, Alon Orlitsky:
Tight Bounds on Profile Redundancy and Distinguishability. 3266-3274 - Nicolás Della Penna, Mark D. Reid, Rafael M. Frongillo:
Interpreting prediction markets: a stochastic approach. 3275-3283 - Amir Sani, Alessandro Lazaric, Rémi Munos:
Risk-Aversion in Multi-armed Bandits. 3284-3292 - Liva Ralaivola:
Confusion-Based Online Learning and a Passive-Aggressive Scheme. 3293-3301 - Kevin Swersky, Daniel Tarlow, Ilya Sutskever, Ruslan Salakhutdinov, Richard S. Zemel, Ryan P. Adams:
Cardinality Restricted Boltzmann Machines. 3302-3310
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