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GeezerVison is showing proceedings.mlr.press — Proceedings of Machine Learning Research [edit] Volume 33: Artificial Intelligence and Statistics, 22-25 April 2014, Reykjavik, Iceland[edit] Editors: Samuel Kaski, Jukka Corander PrefacePreface Samuel Kaski, Jukka Corander; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:i-iv [abs][Download PDF] Notable PapersDecontamination of Mutually Contaminated Models Gilles Blanchard, Clayton Scott; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:1-9 Distributed optimization of deeply nested systems Miguel Carreira-Perpinan, Weiran Wang; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:10-19 Analysis of Empirical MAP and Empirical Partially Bayes: Can They be Alternatives to Variational Bayes? Shinichi Nakajima, Masashi Sugiyama; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:20-28 [abs][Download PDF] Regular PapersImproved Bounds for Online Learning Over the Permutahedron and Other Ranking Polytopes Nir Ailon; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:29-37 [abs][Download PDF] Information-Theoretic Characterization of Sparse Recovery Cem Aksoylar, Venkatesh Saligrama; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:38-46 Hybrid Discriminative-Generative Approach with Gaussian Processes Ricardo Andrade Pacheco, James Hensman, Max Zwiessele, Neil D. Lawrence; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:47-56 Average Case Analysis of High-Dimensional Block-Sparse Recovery and Regression for Arbitrary Designs Waheed Bajwa, Marco Duarte, Robert Calderbank; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:57-67 [abs][Download PDF] A New Perspective on Learning Linear Separators with Large L_qL_p Margins Maria-Florina Balcan, Christopher Berlind; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:68-76 A Non-parametric Conditional Factor Regression Model for Multi-Dimensional Input and Response Ava Bargi, Richard Yi Xu, Zoubin Ghahramani, Massimo Piccardi; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:77-85 [abs][Download PDF] Learning Optimal Bounded Treewidth Bayesian Networks via Maximum Satisfiability Jeremias Berg, Matti Järvisalo, Brandon Malone; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:86-95 [abs][Download PDF] Online Passive-Aggressive Algorithms for Non-Negative Matrix Factorization and Completion Mathieu Blondel, Yotaro Kubo, Ueda Naonori; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:96-104 PAC-Bayesian Theory for Transductive Learning Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:105-113 Random Bayesian networks with bounded indegree Eunice Yuh-Jie Chen, Judea Pearl; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:114-121 [abs][Download PDF] Efficient Low-Rank Stochastic Gradient Descent Methods for Solving Semidefinite Programs Jianhui Chen, Tianbao Yang, Shenghuo Zhu; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:122-130 [abs][Download PDF] Characterizing EVOI-Sufficient k-Response Query Sets in Decision Problems Robert Cohn, Satinder Singh, Edmund Durfee; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:131-139 [abs][Download PDF] Doubly Aggressive Selective Sampling Algorithms for Classification Koby Crammer; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:140-148 Sparse Bayesian Variable Selection for the Identification of Antigenic Variability in the Foot-and-Mouth Disease Virus Vinny Davies, Richard Reeve, William Harvey, Francois Maree, Dirk Husmeier; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:149-158 [abs][Download PDF] Sparsity and the Truncated $l^2$-norm Lee Dicker; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:159-166 Efficient Distributed Topic Modeling with Provable Guarantees Weicong Ding, Mohammad Rohban, Prakash Ishwar, Venkatesh Saligrama; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:167-175 Pan-sharpening with a Bayesian nonparametric dictionary learning model Xinghao Ding, Yiyong Jiang, Yue Huang, John Paisley; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:176-184 [abs][Download PDF] Approximate Slice Sampling for Bayesian Posterior Inference Christopher DuBois, Anoop Korattikara, Max Welling, Padhraic Smyth; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:185-193 [abs][Download PDF] Bayesian Logistic Gaussian Process Models for Dynamic Networks Daniele Durante, David Dunson; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:194-201 [abs][Download PDF] Avoiding pathologies in very deep networks David Duvenaud, Oren Rippel, Ryan Adams, Zoubin Ghahramani; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:202-210 [abs][Download PDF] Efficient Inference for Complex Queries on Complex Distributions Lili Dworkin, Michael Kearns, Lirong Xia; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:211-219 Bayesian Switching Interaction Analysis Under Uncertainty Zoran Dzunic, John Fisher III; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:220-228 [abs][Download PDF] Robust learning of inhomogeneous PMMs Ralf Eggeling, Teemu Roos, Petri Myllymäki, Ivo Grosse; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:229-237 [abs][Download PDF] Fully-Automatic Bayesian Piecewise Sparse Linear Models Riki Eto, Ryohei Fujimaki, Satoshi Morinaga, Hiroshi Tamano; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:238-246 [abs][Download PDF] Learning with Maximum A-Posteriori Perturbation Models Andreea Gane, Tamir Hazan, Tommi Jaakkola; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:247-256 [abs][Download PDF] Sketching the Support of a Probability Measure Joachim Giesen, Soeren Laue, Lars Kuehne; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:257-265 [abs][Download PDF] Robust Stochastic Principal Component Analysis John Goes, Teng Zhang, Raman Arora, Gilad Lerman; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:266-274 [abs][Download PDF] Bayesian Nonparametric Poisson Factorization for Recommendation Systems Prem Gopalan, Francisco J. Ruiz, Rajesh Ranganath, David Blei; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:275-283 Efficiently Enforcing Diversity in Multi-Output Structured Prediction Abner Guzman-Rivera, Pushmeet Kohli, Dhruv Batra, Rob Rutenbar; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:284-292 [abs][Download PDF] Learning and Evaluation in Presence of Non-i.i.d. Label Noise Nico Görnitz, Anne Porbadnigk, Alexander Binder, Claudia Sannelli, Mikio Braun, Klaus-Robert Mueller, Marius Kloft; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:293-302 Analytic Long-Term Forecasting with Periodic Gaussian Processes Nooshin HajiGhassemi, Marc Deisenroth; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:303-311 [abs][Download PDF] On Estimating Causal Effects based on Supplemental Variables Takahiro Hayashi, Manabu Kuroki; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:312-319 [abs][Download PDF] Non-Asymptotic Analysis of Relational Learning with One Network Peng He, Changshui Zhang; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:320-327 [abs][Download PDF] Exploiting the Limits of Structure Learning via Inherent Symmetry Peng He, Changshui Zhang; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:328-337 [abs][Download PDF] A Statistical Model for Event Sequence Data Kevin Heins, Hal Stern; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:338-346 [abs][Download PDF] Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics Philipp Hennig, Søren Hauberg; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:347-355 Tilted Variational Bayes James Hensman, Max Zwiessele, Neil D. Lawrence; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:356-364 On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning Matthew Hoffman, Bobak Shahriari, Nando Freitas; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:365-374 Optimality of Thompson Sampling for Gaussian Bandits Depends on Priors Junya Honda, Akimichi Takemura; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:375-383 Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees Jean Honorio, Tommi Jaakkola; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:384-392 Latent Gaussian Models for Topic Modeling Changwei Hu, Eunsu Ryu, David Carlson, Yingjian Wang, Lawrence Carin; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:393-401 [abs][Download PDF] A Finite-Sample Generalization Bound for Semiparametric Regression: Partially Linear Models Ruitong Huang, Csaba Szepesvari; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:402-410 Global Optimization Methods for Extended Fisher Discriminant Analysis Satoru Iwata, Yuji Nakatsukasa, Akiko Takeda; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:411-419 High-Dimensional Density Ratio Estimation with Extensions to Approximate Likelihood Computation Rafael Izbicki, Ann Lee, Chad Schafer; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:420-429 Near Optimal Bayesian Active Learning for Decision Making Shervin Javdani, Yuxin Chen, Amin Karbasi, Andreas Krause, Drew Bagnell, Siddhartha Srinivasa; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:430-438 A Level-set Hit-and-run Sampler for Quasi-Concave Distributions Shane Jensen, Dean Foster; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:439-447 New Bounds on Compressive Linear Least Squares Regression Ata Kaban; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:448-456 [abs][Download PDF] Recovering Distributions from Gaussian RKHS Embeddings Motonobu Kanagawa, Kenji Fukumizu; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:457-465 Collaborative Ranking for Local Preferences Berk Kapicioglu, David Rosenberg, Robert Schapire, Tony Jebara; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:466-474 Scalable Collaborative Bayesian Preference Learning Mohammad Emtiyaz Khan, Young Jun Ko, Matthias Seeger; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:475-483 A Gaussian Latent Variable Model for Large Margin Classification of Labeled and Unlabeled Data Do-kyum Kim, Matthew Der, Lawrence Saul; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:484-492 Scalable Variational Bayesian Matrix Factorization with Side Information Yong-Deok Kim, Seungjin Choi; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:493-502 [abs][Download PDF] Algebraic Reconstruction Bounds and Explicit Inversion for Phase Retrieval at the Identifiability Threshold Franz Király, Martin Ehler; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:503-511 Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection Jyri Kivinen, Chris Williams, Nicolas Heess; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:512-521 Low-Rank Spectral Learning Alex Kulesza, N. Raj Rao, Satinder Singh; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:522-530 [abs][Download PDF] Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models on Big Data Abhimanu Kumar, Alex Beutel, Qirong Ho, Eric Xing; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:531-539 Computational Education using Latent Structured Prediction Tanja Käser, Alexander Schwing, Tamir Hazan, Markus Gross; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:540-548 [abs][Download PDF] Towards building a Crowd-Sourced Sky Map Dustin Lang, David Hogg, Bernhard Schölkopf; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:549-557 [abs][Download PDF] Incremental Tree-Based Inference with Dependent Normalized Random Measures Juho Lee, Seungjin Choi; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:558-566 Jointly Informative Feature Selection Leonidas Lefakis, Francois Fleuret; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:567-575 [abs][Download PDF] Learning Heterogeneous Hidden Markov Random Fields Jie Liu, Chunming Zhang, Elizabeth Burnside, David Page; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:576-584 [abs][Download PDF] PAC-Bayesian Collective Stability Ben London, Bert Huang, Ben Taskar, Lise Getoor; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:585-594 Active Area Search via Bayesian Quadrature Yifei Ma, Roman Garnett, Jeff Schneider; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:595-603 [abs][Download PDF] Active Boundary Annotation using Random MAP Perturbations Subhransu Maji, Tamir Hazan, Tommi Jaakkola; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:604-613 [abs][Download PDF] Interpretable Sparse High-Order Boltzmann Machines Martin Renqiang Min, Xia Ning, Chao Cheng, Mark Gerstein; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:614-622 Efficient Lifting of MAP LP Relaxations Using k-Locality Martin Mladenov, Kristian Kersting, Amir Globerson; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:623-632 [abs][Download PDF] A Geometric Algorithm for Scalable Multiple Kernel Learning John Moeller, Parasaran Raman, Suresh Venkatasubramanian, Avishek Saha; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:633-642 On the Testability of Models with Missing Data Karthika Mohan, Judea Pearl; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:643-650 [abs][Download PDF] Selective Sampling with Drift Edward Moroshko, Koby Crammer; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:651-659 The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling Willie Neiswanger, Frank Wood, Eric Xing; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:660-668 [abs][Download PDF] Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence Yung-Kyun Noh, Masashi Sugiyama, Song Liu, Marthinus C. Plessis, Frank Chongwoo Park, Daniel D. Lee; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:669-677 Robust Forward Algorithms via PAC-Bayes and Laplace Distributions Asaf Noy, Koby Crammer; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:678-686 Joint Structure Learning of Multiple Non-Exchangeable Networks Chris Oates, Sach Mukherjee; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:687-695 Scaling Nonparametric Bayesian Inference via Subsample-Annealing Fritz Obermeyer, Jonathan Glidden, Eric Jonas; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:696-705 Fast Distribution To Real Regression Junier Oliva, Willie Neiswanger, Barnabas Poczos, Jeff Schneider, Eric Xing; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:706-714 FuSSO: Functional Shrinkage and Selection Operator Junier Oliva, Barnabas Poczos, Timothy Verstynen, Aarti Singh, Jeff Schneider, Fang-Cheng Yeh, Wen-Yih Tseng; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:715-723 To go deep or wide in learning? Gaurav Pandey, Ambedkar Dukkipati; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:724-732 [abs][Download PDF] LAMORE: A Stable, Scalable Approach to Latent Vector Autoregressive Modeling of Categorical Time Series Yubin Park, Carlos Carvalho, Joydeep Ghosh; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:733-742 [abs][Download PDF] Spoofing Large Probability Mass Functions to Improve Sampling Times and Reduce Memory Costs Jon Parker, Hans Engler; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:743-750 [abs][Download PDF] Learning Bounded Tree-width Bayesian Networks using Integer Linear Programming Pekka Parviainen, Hossein Shahrabi Farahani, Jens Lagergren; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:751-759 [abs][Download PDF] An Efficient Algorithm for Large Scale Compressive Feature Learning Hristo Paskov, John Mitchell, Trevor Hastie; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:760-768 Expectation Propagation for Likelihoods Depending on an Inner Product of Two Multivariate Random Variables Tomi Peltola, Pasi Jylänki, Aki Vehtari; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:769-777 An inclusion optimal algorithm for chain graph structure learning Jose Peña, Dag Sonntag, Jens Nielsen; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:778-786 A Stepwise uncertainty reduction approach to constrained global optimization Victor Picheny; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:787-795 [abs][Download PDF] Connected Sub-graph Detection Jing Qian, Venkatesh Saligrama, Yuting Chen; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:796-804 An Analysis of Active Learning with Uniform Feature Noise Aaditya Ramdas, Barnabas Poczos, Aarti Singh, Larry Wasserman; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:805-813 Black Box Variational Inference Rajesh Ranganath, Sean Gerrish, David Blei; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:814-822 Cluster Canonical Correlation Analysis Nikhil Rasiwasia, Dhruv Mahajan, Vijay Mahadevan, Gaurav Aggarwal; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:823-831 [abs][Download PDF] Sequential crowdsourced labeling as an epsilon-greedy exploration in a Markov Decision Process Vikas Raykar, Priyanka Agrawal; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:832-840 Learning Structured Models with the AUC Loss and Its Generalizations Nir Rosenfeld, Ofer Meshi, Danny Tarlow, Amir Globerson; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:841-849 Class Proportion Estimation with Application to Multiclass Anomaly Rejection Tyler Sanderson, Clayton Scott; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:850-858 Lifted MAP Inference for Markov Logic Networks Somdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav Gogate; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:859-867 [abs][Download PDF] Estimating Dependency Structures for non-Gaussian Components with Linear and Energy Correlations Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvarinen; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:868-876 Student-t Processes as Alternatives to Gaussian Processes Amar Shah, Andrew Wilson, Zoubin Ghahramani; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:877-885 In Defense of Minhash over Simhash Anshumali Shrivastava, Ping Li; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:886-894 [abs][Download PDF] Loopy Belief Propagation in the Presence of Determinism David Smith, Vibhav Gogate; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:895-903 [abs][Download PDF] Explicit Link Between Periodic Covariance Functions and State Space Models Arno Solin, Simo Särkkä; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:904-912 [abs][Download PDF] Bat Call Identification with Gaussian Process Multinomial Probit Regression and a Dynamic Time Warping Kernel Vassilios Stathopoulos, Veronica Zamora-Gutierrez, Kate Jones, Mark Girolami; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:913-921 [abs][Download PDF] SMERED: A Bayesian Approach to Graphical Record Linkage and De-duplication Rebecca Steorts, Rob Hall, Stephen Fienberg; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:922-930 Adaptive Variable Clustering in Gaussian Graphical Models Siqi Sun, Yuancheng Zhu, Jinbo Xu; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:931-939 [abs][Download PDF] Scaling Graph-based Semi Supervised Learning to Large Number of Labels Using Count-Min Sketch Partha Talukdar, William Cohen; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:940-947 [abs][Download PDF] Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse Regression Divyanshu Vats, Richard Baraniuk; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:948-957 [abs][Download PDF] Active Learning for Undirected Graphical Model Selection Divyanshu Vats, Robert Nowak, Richard Baraniuk; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:958-967 Linear-time training of nonlinear low-dimensional embeddings Max Vladymyrov, Miguel Carreira-Perpinan; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:968-977 Gaussian Copula Precision Estimation with Missing Values Huahua Wang, Farideh Fazayeli, Soumyadeep Chatterjee, Arindam Banerjee; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:978-986 [abs][Download PDF] An LP for Sequential Learning Under Budgets Joseph Wang, Kirill Trapeznikov, Venkatesh Saligrama; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:987-995 Efficient Algorithms and Error Analysis for the Modified Nystrom Method Shusen Wang, Zhihua Zhang; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:996-1004 Bayesian Multi-Scale Optimistic Optimization Ziyu Wang, Babak Shakibi, Lin Jin, Nando Freitas; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:1005-1014 Accelerating ABC methods using Gaussian processes Richard Wilkinson; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:1015-1023 [abs][Download PDF] A New Approach to Probabilistic Programming Inference Frank Wood, Jan Willem Meent, Vikash Mansinghka; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:1024-1032 [abs][Download PDF] Dynamic Resource Allocation for Optimizing Population Diffusion Shan Xue, Alan Fern, Daniel Sheldon; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:1033-1041 [abs][Download PDF] Mixed Graphical Models via Exponential Families Eunho Yang, Yulia Baker, Pradeep Ravikumar, Genevera Allen, Zhandong Liu; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:1042-1050 Context Aware Group Nearest Shrunken Centroids in Large-Scale Genomic Studies Juemin Yang, Fang Han, Rafael Irizarry, Han Liu; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:1051-1059 Nonparametric estimation and testing of exchangeable graph models Justin Yang, Christina Han, Edoardo Airoldi; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:1060-1067 [abs][Download PDF] Generating Efficient MCMC Kernels from Probabilistic Programs Lingfeng Yang, Patrick Hanrahan, Noah Goodman; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:1068-1076 [abs][Download PDF] Efficient Transfer Learning Method for Automatic Hyperparameter Tuning Dani Yogatama, Gideon Mann; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:1077-1085 [abs][Download PDF] Accelerated Stochastic Gradient Method for Composite Regularization Wenliang Zhong, James Kwok; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:1086-1094 [abs][Download PDF] Heterogeneous Domain Adaptation for Multiple Classes Joey Tianyi Zhou, Ivor W.Tsang, Sinno Jialin Pan, Mingkui Tan; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:1095-1103 [abs][Download PDF] subscribe via RSS |
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