default search action
14. AISTATS 2011: Fort Lauderdale, USA
- Geoffrey J. Gordon, David B. Dunson, Miroslav Dudík:
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2011, Fort Lauderdale, USA, April 11-13, 2011. JMLR Proceedings 15, JMLR.org 2011 - Geoffrey J. Gordon, David B. Dunson:
Preface. 1-2 - Robert E. Tillman, Peter Spirtes:
Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables. 3-15 - Jiji Zhang, Ricardo Bezerra de Andrade e Silva:
Discussion of "Learning Equivalence Classes of Acyclic Models with Latent and Selection Variables from Multiple Datasets with Overlapping Variables". 16-18 - Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, Robert E. Schapire:
Contextual Bandit Algorithms with Supervised Learning Guarantees. 19-26 - H. Brendan McMahan:
Discussion of "Contextual Bandit Algorithms with Supervised Learning Guarantees". 27-28 - Hugo Larochelle, Iain Murray:
The Neural Autoregressive Distribution Estimator. 29-37 - Yoshua Bengio:
Discussion of "The Neural Autoregressive Distribution Estimator". 38-39 - Qiang Liu, Alexander Ihler:
Learning Scale Free Networks by Reweighted L1 regularization. 40-48 - Deepak Agarwal:
Discussion of "Learning Scale Free Networks by Reweighted L1 regularization". 49-50 - Neil D. Lawrence:
Spectral Dimensionality Reduction via Maximum Entropy. 51-59 - Laurens van der Maaten:
Discussion of "Spectral Dimensionality Reduction via Maximum Entropy". 60-62 - Frederik Eaton:
A conditional game for comparing approximations. 63-71 - Vincent Conitzer:
Discussion of "A conditional game for comparing approximations". 72-73 - John W. Paisley, Chong Wang, David M. Blei:
The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling. 74-82 - Frank D. Wood:
Discussion of "The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling". 83-84 - Arvind Agarwal, Hal Daumé III:
Generative Kernels for Exponential Families. 85-92 - Deepak Agarwal, Lihong Li, Alexander J. Smola:
Linear-Time Estimators for Propensity Scores. 93-100 - Amr Ahmed, Qirong Ho, Choon Hui Teo, Jacob Eisenstein, Alexander J. Smola, Eric P. Xing:
Online Inference for the Infinite Topic-Cluster Model: Storylines from Streaming Text. 101-109 - Edoardo M. Airoldi, Bertrand Haas:
Polytope samplers for inference in ill-posed inverse problems. 110-118 - Mohammad Gheshlaghi Azar, Vicenç Gómez, Bert Kappen:
Dynamic Policy Programming with Function Approximation. 119-127 - Anoop Korattikara Balan, Levi Boyles, Max Welling, Jingu Kim, Haesun Park:
Statistical Optimization of Non-Negative Matrix Factorization. 128-136 - Krishnakumar Balasubramanian, Pinar Donmez, Guy Lebanon:
Unsupervised Supervised Learning II: Margin-Based Classification without Labels. 137-145 - Dhruv Batra, Sebastian Nowozin, Pushmeet Kohli:
Tighter Relaxations for MAP-MRF Inference: A Local Primal-Dual Gap based Separation Algorithm. 146-154 - Gowtham Bellala, Suresh K. Bhavnani, Clayton Scott:
Active Diagnosis under Persistent Noise with Unknown Noise Distribution: A Rank-Based Approach. 155-163 - Yoshua Bengio, Frédéric Bastien, Arnaud Bergeron, Nicolas Boulanger-Lewandowski, Thomas M. Breuel, Youssouf Chherawala, Moustapha Cissé, Myriam Côté, Dumitru Erhan, Jeremy Eustache, Xavier Glorot, Xavier Muller, Sylvain Pannetier Lebeuf, Razvan Pascanu, Salah Rifai, François Savard, Guillaume Sicard:
Deep Learners Benefit More from Out-of-Distribution Examples. 164-172 - John Blitzer, Sham M. Kakade, Dean P. Foster:
Domain Adaptation with Coupled Subspaces. 173-181 - Abdeslam Boularias, Jens Kober, Jan Peters:
Relative Entropy Inverse Reinforcement Learning. 182-189 - Chris Bracegirdle, David Barber:
Switch-Reset Models : Exact and Approximate Inference. 190-198 - Edward Challis, David Barber:
Concave Gaussian Variational Approximations for Inference in Large-Scale Bayesian Linear Models. 199-207 - Wei Chu, Lihong Li, Lev Reyzin, Robert E. Schapire:
Contextual Bandits with Linear Payoff Functions. 208-214 - Adam Coates, Andrew Y. Ng, Honglak Lee:
An Analysis of Single-Layer Networks in Unsupervised Feature Learning. 215-223 - Ronan Collobert:
Deep Learning for Efficient Discriminative Parsing. 224-232 - Aaron C. Courville, James Bergstra, Yoshua Bengio:
A Spike and Slab Restricted Boltzmann Machine. 233-241 - Christopher R. Dance, Onno Zoeter:
Optimal and Robust Price Experimentation: Learning by Lottery. 242-250 - Gal Elidan:
Bagged Structure Learning of Bayesian Network. 251-259 - Brian Eriksson, Gautam Dasarathy, Aarti Singh, Robert D. Nowak:
Active Clustering: Robust and Efficient Hierarchical Clustering using Adaptively Selected Similarities. 260-268 - Ahmed K. Farahat, Ali Ghodsi, Mohamed S. Kamel:
A novel greedy algorithm for Nyström approximation. 269-277 - James R. Foulds, Nicholas Navaroli, Padhraic Smyth, Alexander Ihler:
Revisiting MAP Estimation, Message Passing and Perfect Graphs. 278-286 - James R. Foulds, Christopher DuBois, Arthur U. Asuncion, Carter T. Butts, Padhraic Smyth:
A Dynamic Relational Infinite Feature Model for Longitudinal Social Networks. 287-295 - Rahul Garg, Rohit Khandekar:
Block-sparse Solutions using Kernel Block RIP and its Application to Group Lasso. 296-304 - Pierre Geurts:
Learning from positive and unlabeled examples by enforcing statistical significance. 305-314 - Xavier Glorot
, Antoine Bordes, Yoshua Bengio:
Deep Sparse Rectifier Neural Networks. 315-323 - Joseph Gonzalez, Yucheng Low, Arthur Gretton, Carlos Guestrin:
Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees. 324-332 - Qirong Ho, Ankur P. Parikh, Le Song, Eric P. Xing:
Multiscale Community Blockmodel for Network Exploration. 333-341 - Qirong Ho, Le Song, Eric P. Xing:
Evolving Cluster Mixed-Membership Blockmodel for Time-Evolving Networks. 342-350 - Sue Ann Hong, Geoffrey J. Gordon:
Optimal Distributed Market-Based Planning for Multi-Agent Systems with Shared Resources. 351-360 - Bert Huang, Tony Jebara:
Fast b-matching via Sufficient Selection Belief Propagation. 361-369 - Zakria Hussain, John Shawe-Taylor:
Improved Loss Bounds For Multiple Kernel Learning. 370-377 - Ali Jalali, Pradeep Ravikumar, Vishvas Vasuki, Sujay Sanghavi:
On Learning Discrete Graphical Models using Group-Sparse Regularization. 378-387 - Jeremy Jancsary, Gerald Matz:
Convergent Decomposition Solvers for Tree-reweighted Free Energies. 388-398 - Vladimir Jojic, Suchi Saria, Daphne Koller:
Convex envelopes of complexity controlling penalties: the case against premature envelopment. 399-406 - Mladen Kolar, Eric P. Xing:
On Time Varying Undirected Graphs. 407-415 - Simon Lacoste-Julien, Ferenc Huszar, Zoubin Ghahramani:
Approximate inference for the loss-calibrated Bayesian. 416-424 - Balaji Lakshminarayanan, Guillaume Bouchard, Cédric Archambeau:
Robust Bayesian Matrix Factorisation. 425-433 - Bin Li, Steven C. H. Hoi, Peilin Zhao, Vivekanand Gopalkrishnan:
Confidence Weighted Mean Reversion Strategy for On-Line Portfolio Selection. 434-442 - Dazhuo Li, Patrick Shafto:
Bayesian Hierarchical Cross-Clustering. 443-451 - Aurélie C. Lozano, Grzegorz Swirszcz, Naoki Abe:
Group Orthogonal Matching Pursuit for Logistic Regression. 452-460 - Hongtao Lu, Xianzhong Long, Jingyuan Lv:
A Fast Algorithm for Recovery of Jointly Sparse Vectors based on the Alternating Direction Methods. 461-469 - Heng Luo, Ruimin Shen, Changyong Niu, Carsten Ullrich:
Learning Class-relevant Features and Class-irrelevant Features via a Hybrid third-order RBM. 470-478 - Laurens van der Maaten, Max Welling, Lawrence K. Saul:
Hidden-Unit Conditional Random Fields. 479-488 - Satyaki Mahalanabis:
Learning mixtures of Gaussians with maximum-a-posteriori oracle. 489-497 - Ravi Sastry Ganti Mahapatruni, Alexander G. Gray:
CAKE: Convex Adaptive Kernel Density Estimation. 498-506 - André Filipe Torres Martins, Noah A. Smith, Eric P. Xing, Pedro M. Q. Aguiar, Mário A. T. Figueiredo:
Online Learning of Structured Predictors with Multiple Kernels. 507-515 - Daniel J. McDonald, Cosma Rohilla Shalizi, Mark J. Schervish:
Estimating beta-mixing coefficients. 516-524 - H. Brendan McMahan:
Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and L1 Regularization. 525-533 - Mehryar Mohri, Ameet Talwalkar:
Can matrix coherence be efficiently and accurately estimated? 534-542 - Ramesh Nallapati, Daniel A. McFarland, Christopher D. Manning:
TopicFlow Model: Unsupervised Learning of Topic-specific Influences of Hyperlinked Documents. 543-551 - Donglin Niu, Jennifer G. Dy, Michael I. Jordan:
Dimensionality Reduction for Spectral Clustering. 552-560 - Gang Niu, Bo Dai, Lin Shang, Masashi Sugiyama:
Maximum Volume Clustering. 561-569 - Odalric-Ambrym Maillard, Rémi Munos:
Adaptive Bandits: Towards the best history-dependent strategy. 570-578 - Jaakko Peltonen, Samuel Kaski:
Generative Modeling for Maximizing Precision and Recall in Information Visualization. 579-587 - José M. Peña:
Faithfulness in Chain Graphs: The Gaussian Case. 588-599 - Sergey M. Plis, Stephen McCracken, Terran Lane, Vince D. Calhoun:
Directional Statistics on Permutations. 600-608 - Barnabás Póczos, Jeff G. Schneider:
On the Estimation of alpha-Divergences. 609-617 - Pradeep Ravikumar, Ambuj Tewari, Eunho Yang:
On NDCG Consistency of Listwise Ranking Methods. 618-626 - Stéphane Ross, Geoffrey J. Gordon, Drew Bagnell:
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning. 627-635 - Ankan Saha, Ambuj Tewari:
Improved Regret Guarantees for Online Smooth Convex Optimization with Bandit Feedback. 636-642 - Avishek Saha, Piyush Rai, Hal Daumé III, Suresh Venkatasubramanian:
Online Learning of Multiple Tasks and Their Relationships. 643-651 - Matthias W. Seeger, Hannes Nickisch:
Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference. 652-660 - Ohad Shamir, Naftali Tishby:
Spectral Clustering on a Budget. 661-669 - Ricardo Bezerra de Andrade e Silva, Charles Blundell, Yee Whye Teh:
Mixed Cumulative Distribution Networks. 670-678 - Mathieu Sinn, Pascal Poupart:
Asymptotic Theory for Linear-Chain Conditional Random Fields. 679-687 - Ning Situ, Xiaojing Yuan, George Zouridakis:
Assisting Main Task Learning by Heterogeneous Auxiliary Tasks with Applications to Skin Cancer Screening. 688-697 - Richard Socher, Andrew L. Maas, Christopher D. Manning:
Spectral Chinese Restaurant Processes: Nonparametric Clustering Based on Similarities. 698-706 - Le Song, Arthur Gretton, Danny Bickson, Yucheng Low, Carlos Guestrin:
Kernel Belief Propagation. 707-715 - Amos J. Storkey:
Machine Learning Markets. 716-724 - Veselin Stoyanov, Alexander Ropson, Jason Eisner:
Empirical Risk Minimization of Graphical Model Parameters Given Approximate Inference, Decoding, and Model Structure. 725-733 - Mingxuan Sun, Guy Lebanon, Paul Kidwell:
Estimating Probabilities in Recommendation Systems. 734-742 - Kirill Trapeznikov, Venkatesh Saligrama, David A. Castañón:
Active Boosted Learning (ActBoost). 743-751 - Chong Wang, John W. Paisley, David M. Blei:
Online Variational Inference for the Hierarchical Dirichlet Process. 752-760 - Meihong Wang, Fei Sha:
Information Theoretical Clustering via Semidefinite Programming. 761-769 - David Wingate, Andreas Stuhlmüller, Noah D. Goodman:
Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation. 770-778 - Rongjing Xiang, Jennifer Neville:
Relational Learning with One Network: An Asymptotic Analysis. 779-788 - Liang Xiong, Barnabás Póczos, Jeff G. Schneider, Andrew J. Connolly, Jake VanderPlas:
Hierarchical Probabilistic Models for Group Anomaly Detection. 789-797 - Tianbing Xu, Alexander Ihler:
Multicore Gibbs Sampling in Dense, Unstructured Graphs. 798-806 - Makoto Yamada, Masashi Sugiyama:
Cross-Domain Object Matching with Model Selection. 807-815 - Liu Yang, Steve Hanneke, Jaime G. Carbonell:
The Sample Complexity of Self-Verifying Bayesian Active Learning. 816-822 - Shuang-Hong Yang, Steven P. Crain, Hongyuan Zha:
Bridging the Language Gap: Topic Adaptation for Documents with Different Technicality. 823-831 - Gui-Bo Ye, Yifei Chen, Xiaohui Xie:
Efficient variable selection in support vector machines via the alternating direction method of multipliers. 832-840 - Xiaotong Yuan, Shuicheng Yan:
A Finite Newton Algorithm for Non-degenerate Piecewise Linear Systems. 841-854 - Erik Zawadzki, Geoffrey J. Gordon, André Platzer:
An Instantiation-Based Theorem Prover for First-Order Programming. 855-863 - Chao Zhang, Dacheng Tao:
Generalization Bound for Infinitely Divisible Empirical Process. 864-872 - Yi Zhang, Jeff G. Schneider:
Multi-Label Output Codes using Canonical Correlation Analysis. 873-882 - Mingyuan Zhou, Hongxia Yang, Guillermo Sapiro, David B. Dunson, Lawrence Carin:
Dependent Hierarchical Beta Process for Image Interpolation and Denoising. 883-891 - Xueyuan Zhou, Mikhail Belkin:
Semi-supervised Learning by Higher Order Regularization. 892-900 - Xueyuan Zhou, Nathan Srebro:
Error Analysis of Laplacian Eigenmaps for Semi-supervised Learning. 901-908 - Jinfeng Zhuang, Ivor W. Tsang
Two-Layer Multiple Kernel Learning. 909-917
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2024-11-26 20:34 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: