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ORCID15. AISTATS 2012: La Palma, Canary Islands
- Neil D. Lawrence, Mark A. Girolami:
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2012, La Palma, Canary Islands, Spain, April 21-23, 2012. JMLR Proceedings 22, JMLR.org 2012 - Neil D. Lawrence, Mark A. Girolami:
Preface. - Yasin Abbasi-Yadkori, Dávid Pál, Csaba Szepesvári:
Online-to-Confidence-Set Conversions and Application to Sparse Stochastic Bandits. 1-9 - Felix V. Agakov, Peter Orchard
, Amos J. Storkey:
Discriminative Mixtures of Sparse Latent Fields for Risk Management. 10-18 - Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, Robert E. Schapire:
Contextual Bandit Learning with Predictable Rewards. 19-26 - Genevera I. Allen:
Sparse Higher-Order Principal Components Analysis. 27-36 - Saeed Amizadeh, Hamed Valizadegan, Milos Hauskrecht:
Factorized Diffusion Map Approximation. 37-46 - Galen Andrew, Jeff A. Bilmes:
Memory-efficient inference in dynamic graphical models using multiple cores. 47-53 - Hossein Azari Soufiani, Edoardo M. Airoldi:
Graphlet decomposition of a weighted network. 54-63 - Sivaraman Balakrishnan, Alessandro Rinaldo, Don Sheehy, Aarti Singh, Larry A. Wasserman:
Minimax rates for homology inference. 64-72 - Guha Balakrishnan, Zeeshan Syed:
Scalable Personalization of Long-Term Physiological Monitoring: Active Learning Methodologies for Epileptic Seizure Onset Detection. 73-81 - Luca Baldassarre, Jean Morales, Andreas Argyriou, Massimiliano Pontil:
A General Framework for Structured Sparsity via Proximal Optimization. 82-90 - Rémi Bardenet, Olivier Cappé, Gersende Fort, Balázs Kégl:
Adaptive Metropolis with Online Relabeling. 91-99 - Elias Bareinboim, Judea Pearl:
Controlling Selection Bias in Causal Inference. 100-108 - Wei Bian, Bo Xie, Dacheng Tao:
CorrLog: Correlated Logistic Models for Joint Prediction of Multiple Labels. 109-117 - Jasmina Bogojeska, Daniel Stöckel, Maurizio Zazzi, Rolf Kaiser, Francesca Incardona, Michal Rosen-Zvi, Thomas Lengauer:
History-alignment models for bias-aware prediction of virological response to HIV combination therapy. 118-126 - Antoine Bordes, Xavier Glorot, Jason Weston, Yoshua Bengio:
Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing. 127-135 - Joseph K. Bradley, Carlos Guestrin:
Sample Complexity of Composite Likelihood. 136-160 - Marcus A. Brubaker, Mathieu Salzmann, Raquel Urtasun:
A Family of MCMC Methods on Implicitly Defined Manifolds. 161-172 - David Buchman, Mark Schmidt, Shakir Mohamed, David Poole, Nando de Freitas:
On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models. 173-181 - Lucian Busoniu, Rémi Munos:
Optimistic planning for Markov decision processes. 182-189 - Alexandra Carpentier, Rémi Munos:
Bandit Theory meets Compressed Sensing for high dimensional Stochastic Linear Bandit. 190-198 - Xi Chen, Han Liu, Jaime G. Carbonell:
Structured Sparse Canonical Correlation Analysis. 199-207 - Xiaohui Chen, Xinghua Shi, Xing Xu, Zhiyong Wang, Ryan Mills, Charles Lee, Jinbo Xu:
A Two-Graph Guided Multi-task Lasso Approach for eQTL Mapping. 208-217 - Minmin Chen, Zhixiang Eddie Xu, Kilian Q. Weinberger, Olivier Chapelle, Dor Kedem:
Classifier Cascade for Minimizing Feature Evaluation Cost. 218-226 - Anna Choromanska, Claire Monteleoni:
Online Clustering with Experts. 227-235 - Olivier Collier:
Minimax hypothesis testing for curve registration. 236-245 - Bryan R. Conroy, Paul Sajda:
Fast, Exact Model Selection and Permutation Testing for l2-Regularized Logistic Regression. 246-254 - John P. Cunningham, Zoubin Ghahramani, Carl Edward Rasmussen:
Gaussian Processes for time-marked time-series data. 255-263 - Arnak S. Dalalyan, Olivier Collier:
Wilks' phenomenon and penalized likelihood-ratio test for nonparametric curve registration. 264-272 - Christian Daniel, Gerhard Neumann, Jan Peters:
Hierarchical Relative Entropy Policy Search. 273-281 - Hal Daumé III, Jeff M. Phillips, Avishek Saha, Suresh Venkatasubramanian:
Protocols for Learning Classifiers on Distributed Data. 282-290 - Ofer Dekel, Ohad Shamir:
There's a Hole in My Data Space: Piecewise Predictors for Heterogeneous Learning Problems. 291-298 - Paramveer S. Dhillon, S. Sathiya Keerthi, Kedar Bellare, Olivier Chapelle, Sundararajan Sellamanickam:
Deterministic Annealing for Semi-Supervised Structured Output Learning. 299-307 - Huyen Do, Alexandros Kalousis, Jun Wang, Adam Woznica:
A metric learning perspective of SVM: on the relation of LMNN and SVM. 308-317 - Justin Domke:
Generic Methods for Optimization-Based Modeling. 318-326 - Miroslav Dudík, Zaïd Harchaoui, Jérôme Malick:
Lifted coordinate descent for learning with trace-norm regularization. 327-336 - Robert J. Durrant, Ata Kabán:
Error bounds for Kernel Fisher Linear Discriminant in Gaussian Hilbert space. 337-345 - Gal Elidan:
Copula Network Classifiers (CNCs). 346-354 - Gal Elidan:
Lightning-speed Structure Learning of Nonlinear Continuous Networks. 355-363 - Doris Entner, Patrik O. Hoyer, Peter Spirtes:
Statistical test for consistent estimation of causal effects in linear non-Gaussian models. 364-372 - Brian Eriksson, Laura Balzano, Robert D. Nowak:
High-Rank Matrix Completion. 373-381 - Dean P. Foster, Alexander Rakhlin:
No Internal Regret via Neighborhood Watch. 382-390 - Antonino Freno:
Semiparametric Pseudo-Likelihood Estimation in Markov Random Fields. 391-399 - Ryohei Fujimaki, Satoshi Morinaga:
Factorized Asymptotic Bayesian Inference for Mixture Modeling. 400-408 - Alona Fyshe, Emily B. Fox, David B. Dunson, Tom M. Mitchell:
Hierarchical Latent Dictionaries for Models of Brain Activation. 409-421 - Ravi Ganti, Alexander G. Gray:
UPAL: Unbiased Pool Based Active Learning. 422-431 - Joachim Giesen, Martin Jaggi, Sören Laue:
Regularization Paths with Guarantees for Convex Semidefinite Optimization. 432-439 - Dilan Görür, Levi Boyles, Max Welling:
Scalable Inference on Kingman's Coalescent using Pair Similarity . 440-448 - Yuri Grinberg, Doina Precup:
On Average Reward Policy Evaluation in Infinite-State Partially Observable Systems. 449-457 - Alexander Grubb, Drew Bagnell:
SpeedBoost: Anytime Prediction with Uniform Near-Optimality. 458-466 - Marco Grzegorczyk, Dirk Husmeier:
Bayesian regularization of non-homogeneous dynamic Bayesian networks by globally coupling interaction parameters. 467-476 - Quanquan Gu, Marina Danilevsky, Zhenhui Li, Jiawei Han:
Locality Preserving Feature Learning. 477-485 - Michael Habeck:
Evaluation of marginal likelihoods via the density of states. 486-494 - Morteza Haghir Chehreghani, Alberto Giovanni Busetto, Joachim M. Buhmann:
Information Theoretic Model Validation for Spectral Clustering. 495-503 - Fares Hedayati, Peter L. Bartlett:
Exchangeability Characterizes Optimality of Sequential Normalized Maximum Likelihood and Bayesian Prediction with Jeffreys Prior. 504-510 - Philipp Hennig, David H. Stern, Ralf Herbrich, Thore Graepel:
Kernel Topic Models. 511-519 - Minh Hoai Nguyen, Fernando De la Torre:
Maximum Margin Temporal Clustering. 520-528 - Junya Honda, Akimichi Takemura:
Stochastic Bandit Based on Empirical Moments. 529-537 - Jean Honorio, Dimitris Samaras, Irina Rish, Guillermo A. Cecchi:
Variable Selection for Gaussian Graphical Models. 538-546 - Katsuhiko Ishiguro, Naonori Ueda, Hiroshi Sawada:
Subset Infinite Relational Models. 547-555 - Ming Ji, Jiawei Han:
A Variance Minimization Criterion to Active Learning on Graphs. 556-564 - Xiaoye Jiang, Yuan Yao, Han Liu, Leonidas J. Guibas:
Detecting Network Cliques with Radon Basis Pursuit. 565-573 - Christopher C. Johnson, Ali Jalali, Pradeep Ravikumar:
High-dimensional Sparse Inverse Covariance Estimation using Greedy Methods. 574-582 - Purushottam Kar, Harish Karnick:
Random Feature Maps for Dot Product Kernels. 583-591 - Emilie Kaufmann, Olivier Cappé, Aurélien Garivier:
On Bayesian Upper Confidence Bounds for Bandit Problems. 592-600 - Azadeh Khaleghi, Daniil Ryabko, Jérémie Mary, Philippe Preux:
Online Clustering of Processes. 601-609 - Mohammad Emtiyaz Khan, Shakir Mohamed, Benjamin M. Marlin, Kevin P. Murphy:
A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models. 610-618 - Hyun-Chul Kim, Zoubin Ghahramani:
Bayesian Classifier Combination. 619-627 - Franz J. Király, Paul von Bünau, Jan Saputra Müller, Duncan A. J. Blythe, Frank C. Meinecke, Klaus-Robert Müller:
Regression for sets of polynomial equations. 628-637 - Jyri J. Kivinen, Christopher K. I. Williams:
Multiple Texture Boltzmann Machines. 638-646 - Mladen Kolar, Han Liu:
Marginal Regression For Multitask Learning. 647-655 - Akshat Kumar, Shlomo Zilberstein, Marc Toussaint:
Message-Passing Algorithms for MAP Estimation Using DC Programming. 656-664 - Alexandre Lacoste, François Laviolette, Mario Marchand:
Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets. 665-675 - Marius Leordeanu, Cristian Sminchisescu:
Efficient Hypergraph Clustering. 676-684 - Guy Lever, Tom Diethe, John Shawe-Taylor:
Data dependent kernels in nearly-linear time. 685-693 - Dahua Lin, John W. Fisher III:
Efficient Sampling from Combinatorial Space via Bridging. 694-702 - Guangcan Liu, Huan Xu, Shuicheng Yan:
Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation. 703-711 - Jie Liu, Chunming Zhang, Catherine A. McCarty, Peggy L. Peissig, Elizabeth S. Burnside, David Page:
High-Dimensional Structured Feature Screening Using Binary Markov Random Fields. 712-721 - Roi Livni, Koby Crammer, Amir Globerson:
A Simple Geometric Interpretation of SVM using Stochastic Adversaries. 722-730 - Jörg Lücke, Marc Henniges:
Closed-Form Entropy Limits - A Tool to Monitor Likelihood Optimization of Probabilistic Generative Models. 731-740 - Jaakko Luttinen, Alexander Ilin:
Efficient Gaussian Process Inference for Short-Scale Spatio-Temporal Modeling. 741-750 - Nimalan Mahendran, Ziyu Wang, Firas Hamze, Nando de Freitas:
Adaptive MCMC with Bayesian Optimization. 751-760 - Franziska Meier, Evangelos A. Theodorou, Stefan Schaal:
Movement Segmentation and Recognition for Imitation Learning. 761-769 - Elad Mezuman, Yair Weiss:
Globally Optimizing Graph Partitioning Problems Using Message Passing. 770-778 - Kevin Miller, M. Pawan Kumar, Benjamin Packer, Danny Goodman, Daphne Koller:
Max-Margin Min-Entropy Models. 779-787 - Martin Mladenov, Babak Ahmadi, Kristian Kersting:
Lifted Linear Programming. 788-797 - Grégoire Montavon, Mikio L. Braun, Klaus-Robert Müller:
Deep Boltzmann Machines as Feed-Forward Hierarchies. 798-804 - Gergely Neu, András György, Csaba Szepesvári:
The adversarial stochastic shortest path problem with unknown transition probabilities. 805-813 - Donglin Niu, Jennifer G. Dy, Zoubin Ghahramani:
A Nonparametric Bayesian Model for Multiple Clustering with Overlapping Feature Views. 814-822 - Francesco Orabona, Nicolò Cesa-Bianchi, Claudio Gentile:
Beyond Logarithmic Bounds in Online Learning. 823-831 - Michael A. Osborne, Roman Garnett, Stephen J. Roberts, Christopher Hart, Suzanne Aigrain, Neale Gibson:
Bayesian Quadrature for Ratios. 832-840 - Zhijian Ou, Yang Zhang:
Probabilistic acoustic tube: a probabilistic generative model of speech for speech analysis/synthesis. 841-849 - John W. Paisley, David M. Blei, Michael I. Jordan:
Stick-Breaking Beta Processes and the Poisson Process. 850-858 - Alexander Paprotny, Jochen Garcke:
On a Connection between Maximum Variance Unfolding, Shortest Path Problems and IsoMap. 859-867 - Jian Peng, Tamir Hazan, Nathan Srebro, Jinbo Xu:
Approximate Inference by Intersecting Semidefinite Bound and Local Polytope. 868-876 - Patrick Pletscher, Cheng Soon Ong:
Part & Clamp: Efficient Structured Output Learning. 877-885 - Patrick Pletscher, Pushmeet Kohli:
Learning Low-order Models for Enforcing High-order Statistics. 886-894 - Eftychios A. Pnevmatikakis, Liam Paninski:
Fast interior-point inference in high-dimensional sparse, penalized state-space models. 895-904 - Adam Craig Pocock, Mikel Luján, Gavin Brown:
Informative Priors for Markov Blanket Discovery. 905-913 - Barnabás Póczos, Jeff G. Schneider:
Nonparametric Estimation of Conditional Information and Divergences. 914-923 - Tapani Raiko, Harri Valpola, Yann LeCun:
Deep Learning Made Easier by Linear Transformations in Perceptrons. 924-932 - Arun Rajkumar, Shivani Agarwal:
A Differentially Private Stochastic Gradient Descent Algorithm for Multiparty Classification. 933-941 - Nikhil Rao, Ben Recht, Robert D. Nowak:
Universal Measurement Bounds for Structured Sparse Signal Recovery. 942-950 - Bernardino Romera-Paredes, Andreas Argyriou, Nadia Berthouze, Massimiliano Pontil:
Exploiting Unrelated Tasks in Multi-Task Learning. 951-959 - Ruslan Salakhutdinov, Sham M. Kakade, Dean P. Foster:
Domain Adaptation: A Small Sample Statistical Approach. 960-968 - Venkatesh Saligrama, Manqi Zhao:
Local Anomaly Detection. 969-983 - Avneesh Singh Saluja, Priya Krishnan Sundararajan, Ole J. Mengshoel:
Age-Layered Expectation Maximization for Parameter Learning in Bayesian Networks. 984-992 - Simo Särkkä, Jouni Hartikainen:
Infinite-Dimensional Kalman Filtering Approach to Spatio-Temporal Gaussian Process Regression. 993-1001 - Martin Schiegg, Marion Neumann, Kristian Kersting:
Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data. 1002-1011 - Matthias W. Seeger, Guillaume Bouchard:
Fast Variational Bayesian Inference for Non-Conjugate Matrix Factorization Models. 1012-1018 - Shai Shalev-Shwartz, Ohad Shamir, Eran Tromer:
Using More Data to Speed-up Training Time. 1019-1027 - James Sharpnack, Aarti Singh, Alessandro Rinaldo:
Sparsistency of the Edge Lasso over Graphs. 1028-1036 - Jinwoo Shin:
Complexity of Bethe Approximation. 1037-1045 - Pannagadatta K. Shivaswamy, Thorsten Joachims:
Multi-armed Bandit Problems with History. 1046-1054 - Ajit P. Singh, Andrew Guillory, Jeff A. Bilmes:
On Bisubmodular Maximization. 1055-1063 - Carl Smith, Frank D. Wood, Liam Paninski:
Low rank continuous-space graphical models. 1064-1072 - Jasper Snoek, Ryan Prescott Adams, Hugo Larochelle:
On Nonparametric Guidance for Learning Autoencoder Representations. 1073-1080 - Kyung-Ah Sohn, Seyoung Kim:
Joint Estimation of Structured Sparsity and Output Structure in Multiple-Output Regression via Inverse-Covariance Regularization. 1081-1089 - Bharath K. Sriperumbudur, Ingo Steinwart:
Consistency and Rates for Clustering with DBSCAN. 1090-1098 - Radhendushka Srivastava, Ping Li, Debasis Sengupta:
Testing for Membership to the IFRA and the NBU Classes of Distributions. 1099-1107 - Jacob Steinhardt, Zoubin Ghahramani:
Flexible Martingale Priors for Deep Hierarchies. 1108-1116 - Florian Stimberg, Andreas Ruttor, Manfred Opper:
Bayesian Inference for Change Points in Dynamical Systems with Reusable States - a Chinese Restaurant Process Approach. 1117-1124 - Peter Stobbe, Andreas Krause:
Learning Fourier Sparse Set Functions. 1125-1133 - Min Sun, Murali Telaprolu, Honglak Lee, Silvio Savarese:
Efficient and Exact MAP-MRF Inference using Branch and Bound. 1134-1142 - Syama Sundar Rangapuram, Matthias Hein:
Constrained 1-Spectral Clustering. 1143-1151 - Taiji Suzuki, Masashi Sugiyama:
Fast Learning Rate of Multiple Kernel Learning: Trade-Off between Sparsity and Smoothness. 1152-1183 - Matt Taddy:
On Estimation and Selection for Topic Models. 1184-1193 - Nima Taghipour, Daan Fierens, Jesse Davis, Hendrik Blockeel:
Lifted Variable Elimination with Arbitrary Constraints. 1194-1202 - Yichuan Tang, Abdel-rahman Mohamed:
Multiresolution Deep Belief Networks. 1203-1211 - Daniel Tarlow, Richard S. Zemel:
Structured Output Learning with High Order Loss Functions. 1212-1220 - Daniel Tarlow, Ryan Prescott Adams, Richard S. Zemel:
Randomized Optimum Models for Structured Prediction. 1221-1229 - Bo Thiesson, Jingu Kim:
Fast Variational Mode-Seeking. 1230-1242 - Ryota Tomioka, Morten Mørup:
A Bayesian Analysis of the Radioactive Releases of Fukushima. 1243-1251 - Ruth Urner, Shai Ben-David, Ohad Shamir:
Learning from Weak Teachers. 1252-1260 - Oriol Vinyals, Daniel Povey:
Krylov Subspace Descent for Deep Learning. 1261-1268 - Seppo Virtanen, Arto Klami, Suleiman A. Khan, Samuel Kaski:
Bayesian Group Factor Analysis. 1269-1277 - Vincent Q. Vu, Jing Lei:
Minimax Rates of Estimation for Sparse PCA in High Dimensions. 1278-1286 - Li Wan, Leo Zhu, Rob Fergus:
A Hybrid Neural Network-Latent Topic Model. 1287-1294 - Weiran Wang, Miguel Á. Carreira-Perpiñán:
Nonlinear low-dimensional regression using auxiliary coordinates. 1295-1304 - Martha White, Dale Schuurmans:
Generalized Optimal Reverse Prediction. 1305-1313 - John M. Winn:
Causality with Gates. 1314-1322 - Yu Xin, Tommi S. Jaakkola:
Primal-Dual methods for sparse constrained matrix completion. 1323-1331 - Huan Xu, Constantine Caramanis, Shie Mannor:
Statistical Optimization in High Dimensions. 1332-1340 - Huan Xu, Chenlei Leng:
Robust Multi-task Regression with Grossly Corrupted Observations. 1341-1349 - Yan Yan, Rómer Rosales, Glenn Fung, Faisal Farooq, Bharat Rao, Jennifer G. Dy:
Active Learning from Multiple Knowledge Sources. 1350-1357 - Eunho Yang, Ambuj Tewari, Pradeep Ravikumar:
Perturbation based Large Margin Approach for Ranking. 1358-1366 - Chun-Nam Yu:
Transductive Learning of Structural SVMs via Prior Knowledge Constraints. 1367-1376 - Xiaotong Yuan, Shuicheng Yan:
Forward Basis Selection for Sparse Approximation over Dictionary. 1377-1388 - Hyokun Yun, S. V. N. Vishwanathan:
Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative Attribute Graphs. 1389-1397 - Caoxie Zhang, Honglak Lee, Kang G. Shin:
Efficient Distributed Linear Classification Algorithms via the Alternating Direction Method of Multipliers. 1398-1406 - Yi Zhang, Jeff G. Schneider:
A Composite Likelihood View for Multi-Label Classification. 1407-1415 - Zhihua Zhang, Dakan Wang, Edward Y. Chang:
An Autoregressive Approach to Nonparametric Hierarchical Dependent Modeling. 1416-1424 - Kai Zhang, Liang Lan, Zhuang Wang, Fabian Moerchen:
Scaling up Kernel SVM on Limited Resources: A Low-rank Linearization Approach. 1425-1434 - Tuo Zhao, Han Liu:
Sparse Additive Machine. 1435-1443 - Tianyi Zhou, Dacheng Tao:
Multi-label Subspace Ensemble. 1444-1452 - Guanyu Zhou, Kihyuk Sohn, Honglak Lee:
Online Incremental Feature Learning with Denoising Autoencoders. 1453-1461 - Mingyuan Zhou, Lauren Hannah, David B. Dunson, Lawrence Carin:
Beta-Negative Binomial Process and Poisson Factor Analysis. 1462-1471 - J. Zico Kolter, Tommi S. Jaakkola:
Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation. 1472-1482
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