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