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ORCID17th AISTATS 2014: Reykjavik, Iceland
- Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, AISTATS 2014, Reykjavik, Iceland, April 22-25, 2014. JMLR Workshop and Conference Proceedings 33, JMLR.org 2014
- Samuel Kaski, Jukka Corander:
Preface. i-iv
Notable Papers
- Gilles Blanchard, Clayton Scott:
Decontamination of Mutually Contaminated Models. 1-9 - Miguel Á. Carreira-Perpiñán, Weiran Wang:
Distributed optimization of deeply nested systems. 10-19 - Shinichi Nakajima, Masashi Sugiyama:
Analysis of Empirical MAP and Empirical Partially Bayes: Can They be Alternatives to Variational Bayes? 20-28
Regular Papers
- Nir Ailon:
Improved Bounds for Online Learning Over the Permutahedron and Other Ranking Polytopes. 29-37 - Cem Aksoylar, Venkatesh Saligrama:
Information-Theoretic Characterization of Sparse Recovery. 38-46 - Ricardo Andrade Pacheco, James Hensman, Max Zwiessele, Neil D. Lawrence:
Hybrid Discriminative-Generative Approach with Gaussian Processes. 47-56 - Waheed U. Bajwa, Marco F. Duarte, A. Robert Calderbank:
Average Case Analysis of High-Dimensional Block-Sparse Recovery and Regression for Arbitrary Designs. 57-67 - Maria-Florina Balcan, Christopher Berlind:
A New Perspective on Learning Linear Separators with Large \(L_qL_p\) Margins. 68-76 - Ava Bargi, Richard Yi Da Xu, Zoubin Ghahramani, Massimo Piccardi:
A Non-parametric Conditional Factor Regression Model for Multi-Dimensional Input and Response. 77-85 - Jeremias Berg, Matti Järvisalo, Brandon M. Malone:
Learning Optimal Bounded Treewidth Bayesian Networks via Maximum Satisfiability. 86-95 - Mathieu Blondel, Yotaro Kubo, Naonori Ueda:
Online Passive-Aggressive Algorithms for Non-Negative Matrix Factorization and Completion. 96-104 - Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy:
PAC-Bayesian Theory for Transductive Learning. 105-113 - Eunice Yuh-Jie Chen, Judea Pearl:
Random Bayesian networks with bounded indegree. 114-121 - Jianhui Chen, Tianbao Yang, Shenghuo Zhu:
Efficient Low-Rank Stochastic Gradient Descent Methods for Solving Semidefinite Programs. 122-130 - Robert Cohn, Satinder Singh, Edmund H. Durfee:
Characterizing EVOI-Sufficient k-Response Query Sets in Decision Problems. 131-139 - Koby Crammer:
Doubly Aggressive Selective Sampling Algorithms for Classification. 140-148 - Vinny Davies, Richard E. Reeve, William T. Harvey, Francois F. Maree, Dirk Husmeier:
Sparse Bayesian Variable Selection for the Identification of Antigenic Variability in the Foot-and-Mouth Disease Virus. 149-158 - Lee H. Dicker:
Sparsity and the truncated \(l^2\)-norm. 159-166 - Weicong Ding, Mohammad H. Rohban, Prakash Ishwar, Venkatesh Saligrama:
Efficient Distributed Topic Modeling with Provable Guarantees. 167-175 - Xinghao Ding, Yiyong Jiang, Yue Huang, John W. Paisley:
Pan-sharpening with a Bayesian nonparametric dictionary learning model. 176-184 - Christopher DuBois, Anoop Korattikara Balan, Max Welling, Padhraic Smyth:
Approximate Slice Sampling for Bayesian Posterior Inference. 185-193 - Daniele Durante, David B. Dunson:
Bayesian Logistic Gaussian Process Models for Dynamic Networks. 194-201 - David Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani:
Avoiding pathologies in very deep networks. 202-210 - Lili Dworkin, Michael J. Kearns, Lirong Xia:
Efficient Inference for Complex Queries on Complex Distributions. 211-219 - Zoran Dzunic, John W. Fisher III:
Bayesian Switching Interaction Analysis Under Uncertainty. 220-228 - Ralf Eggeling, Teemu Roos, Petri Myllymäki, Ivo Grosse:
Robust learning of inhomogeneous PMMs. 229-237 - Riki Eto, Ryohei Fujimaki, Satoshi Morinaga, Hiroshi Tamano:
Fully-Automatic Bayesian Piecewise Sparse Linear Models. 238-246 - Andreea Gane, Tamir Hazan, Tommi S. Jaakkola:
Learning with Maximum A-Posteriori Perturbation Models. 247-256 - Joachim Giesen, Sören Laue, Lars Kuehne:
Sketching the Support of a Probability Measure. 257-265 - John Goes, Teng Zhang, Raman Arora, Gilad Lerman:
Robust Stochastic Principal Component Analysis. 266-274 - Prem Gopalan, Francisco J. R. Ruiz, Rajesh Ranganath, David M. Blei:
Bayesian Nonparametric Poisson Factorization for Recommendation Systems. 275-283 - Abner Guzmán-Rivera, Pushmeet Kohli, Dhruv Batra, Rob A. Rutenbar:
Efficiently Enforcing Diversity in Multi-Output Structured Prediction. 284-292 - Nico Görnitz, Anne Porbadnigk, Alexander Binder, Claudia Sannelli, Mikio L. Braun, Klaus-Robert Müller, Marius Kloft:
Learning and Evaluation in Presence of Non-i.i.d. Label Noise. 293-302 - Nooshin HajiGhassemi, Marc Peter Deisenroth:
Analytic Long-Term Forecasting with Periodic Gaussian Processes. 303-311 - Takahiro Hayashi, Manabu Kuroki:
On Estimating Causal Effects based on Supplemental Variables. 312-319 - Peng He, Changshui Zhang:
Non-Asymptotic Analysis of Relational Learning with One Network. 320-327 - Peng He, Changshui Zhang:
Exploiting the Limits of Structure Learning via Inherent Symmetry. 328-337 - Kevin Heins, Hal S. Stern:
A Statistical Model for Event Sequence Data. 338-346 - Philipp Hennig, Søren Hauberg:
Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics. 347-355 - James Hensman, Max Zwiessele, Neil D. Lawrence:
Tilted Variational Bayes. 356-364 - Matthew W. Hoffman, Bobak Shahriari, Nando de Freitas:
On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning. 365-374 - Junya Honda, Akimichi Takemura:
Optimality of Thompson Sampling for Gaussian Bandits Depends on Priors. 375-383 - Jean Honorio
, Tommi S. Jaakkola:
Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees. 384-392 - Changwei Hu, Eunsu Ryu, David E. Carlson, Yingjian Wang, Lawrence Carin:
Latent Gaussian Models for Topic Modeling. 393-401 - Ruitong Huang, Csaba Szepesvári:
A Finite-Sample Generalization Bound for Semiparametric Regression: Partially Linear Models. 402-410 - Satoru Iwata, Yuji Nakatsukasa, Akiko Takeda:
Global Optimization Methods for Extended Fisher Discriminant Analysis. 411-419 - Rafael Izbicki, Ann B. Lee, Chad Schafer:
High-Dimensional Density Ratio Estimation with Extensions to Approximate Likelihood Computation. 420-429 - Shervin Javdani, Yuxin Chen, Amin Karbasi, Andreas Krause, Drew Bagnell, Siddhartha S. Srinivasa:
Near Optimal Bayesian Active Learning for Decision Making. 430-438 - Shane T. Jensen, Dean P. Foster:
A Level-set Hit-and-run Sampler for Quasi-Concave Distributions. 439-447 - Ata Kabán:
New Bounds on Compressive Linear Least Squares Regression. 448-456 - Motonobu Kanagawa, Kenji Fukumizu:
Recovering Distributions from Gaussian RKHS Embeddings. 457-465 - Berk Kapicioglu, David S. Rosenberg, Robert E. Schapire, Tony Jebara:
Collaborative Ranking for Local Preferences. 466-474 - Mohammad Emtiyaz Khan, Young-Jun Ko, Matthias W. Seeger:
Scalable Collaborative Bayesian Preference Learning. 475-483 - Do-kyum Kim, Matthew F. Der, Lawrence K. Saul:
A Gaussian Latent Variable Model for Large Margin Classification of Labeled and Unlabeled Data. 484-492 - Yong-Deok Kim, Seungjin Choi:
Scalable Variational Bayesian Matrix Factorization with Side Information. 493-502 - Franz J. Király, Martin Ehler:
Algebraic Reconstruction Bounds and Explicit Inversion for Phase Retrieval at the Identifiability Threshold. 503-511 - Jyri J. Kivinen, Christopher K. I. Williams, Nicolas Heess:
Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection. 512-521 - Alex Kulesza, N. Raj Rao, Satinder Singh:
Low-Rank Spectral Learning. 522-530 - Abhimanu Kumar, Alex Beutel, Qirong Ho, Eric P. Xing:
Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models on Big Data. 531-539 - Tanja Käser, Alexander G. Schwing, Tamir Hazan, Markus H. Gross:
Computational Education using Latent Structured Prediction. 540-548 - Dustin Lang, David W. Hogg, Bernhard Schölkopf:
Towards building a Crowd-Sourced Sky Map. 549-557 - Juho Lee, Seungjin Choi:
Incremental Tree-Based Inference with Dependent Normalized Random Measures. 558-566 - Leonidas Lefakis, François Fleuret:
Jointly Informative Feature Selection. 567-575 - Jie Liu, Chunming Zhang, Elizabeth S. Burnside, David Page:
Learning Heterogeneous Hidden Markov Random Fields. 576-584 - Ben London, Bert Huang, Ben Taskar, Lise Getoor:
PAC-Bayesian Collective Stability. 585-594 - Yifei Ma, Roman Garnett, Jeff G. Schneider:
Active Area Search via Bayesian Quadrature. 595-603 - Subhransu Maji, Tamir Hazan, Tommi S. Jaakkola:
Active Boundary Annotation using Random MAP Perturbations. 604-613 - Martin Renqiang Min, Xia Ning, Chao Cheng, Mark Gerstein:
Interpretable Sparse High-Order Boltzmann Machines. 614-622 - Martin Mladenov, Kristian Kersting, Amir Globerson:
Efficient Lifting of MAP LP Relaxations Using k-Locality. 623-632 - John Moeller, Parasaran Raman, Suresh Venkatasubramanian, Avishek Saha:
A Geometric Algorithm for Scalable Multiple Kernel Learning. 633-642 - Karthika Mohan, Judea Pearl:
On the Testability of Models with Missing Data. 643-650 - Edward Moroshko, Koby Crammer:
Selective Sampling with Drift. 651-659 - Willie Neiswanger, Frank D. Wood, Eric P. Xing:
The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling. 660-668 - Yung-Kyun Noh, Masashi Sugiyama, Song Liu, Marthinus Christoffel du Plessis, Frank Chongwoo Park, Daniel D. Lee:
Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence. 669-677 - Asaf Noy, Koby Crammer:
Robust Forward Algorithms via PAC-Bayes and Laplace Distributions. 678-686 - Chris J. Oates, Sach Mukherjee:
Joint Structure Learning of Multiple Non-Exchangeable Networks. 687-695 - Fritz Obermeyer, Jonathan Glidden, Eric Jonas:
Scaling Nonparametric Bayesian Inference via Subsample-Annealing. 696-705 - Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing:
Fast Distribution To Real Regression. 706-714 - Junier B. Oliva, Barnabás Póczos, Timothy D. Verstynen, Aarti Singh, Jeff G. Schneider, Fang-Cheng Yeh, Wen-Yih Isaac Tseng:
FuSSO: Functional Shrinkage and Selection Operator. 715-723 - Gaurav Pandey, Ambedkar Dukkipati:
To go deep or wide in learning? 724-732 - Yubin Park, Carlos Carvalho, Joydeep Ghosh:
LAMORE: A Stable, Scalable Approach to Latent Vector Autoregressive Modeling of Categorical Time Series. 733-742 - Jon Parker, Hans Engler:
Spoofing Large Probability Mass Functions to Improve Sampling Times and Reduce Memory Costs. 743-750 - Pekka Parviainen, Hossein Shahrabi Farahani, Jens Lagergren:
Learning Bounded Tree-width Bayesian Networks using Integer Linear Programming. 751-759 - Hristo S. Paskov, John C. Mitchell, Trevor J. Hastie:
An Efficient Algorithm for Large Scale Compressive Feature Learning. 760-768 - Tomi Peltola, Pasi Jylänki, Aki Vehtari:
Expectation Propagation for Likelihoods Depending on an Inner Product of Two Multivariate Random Variables. 769-777 - José M. Peña, Dag Sonntag, Jens Dalgaard Nielsen:
An inclusion optimal algorithm for chain graph structure learning. 778-786 - Victor Picheny:
A Stepwise uncertainty reduction approach to constrained global optimization. 787-795 - Jing Qian, Venkatesh Saligrama, Yuting Chen:
Connected Sub-graph Detection. 796-804 - Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman:
An Analysis of Active Learning with Uniform Feature Noise. 805-813 - Rajesh Ranganath, Sean Gerrish, David M. Blei:
Black Box Variational Inference. 814-822 - Nikhil Rasiwasia, Dhruv Mahajan, Vijay Mahadevan, Gaurav Aggarwal:
Cluster Canonical Correlation Analysis. 823-831 - Vikas C. Raykar, Priyanka Agrawal:
Sequential crowdsourced labeling as an epsilon-greedy exploration in a Markov Decision Process. 832-840 - Nir Rosenfeld, Ofer Meshi, Daniel Tarlow, Amir Globerson:
Learning Structured Models with the AUC Loss and Its Generalizations. 841-849 - Tyler Sanderson, Clayton Scott:
Class Proportion Estimation with Application to Multiclass Anomaly Rejection. 850-858 - Somdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav Gogate:
Lifted MAP Inference for Markov Logic Networks. 859-867 - Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvärinen:
Estimating Dependency Structures for non-Gaussian Components with Linear and Energy Correlations. 868-876 - Amar Shah, Andrew Gordon Wilson, Zoubin Ghahramani:
Student-t Processes as Alternatives to Gaussian Processes. 877-885 - Anshumali Shrivastava, Ping Li:
In Defense of Minhash over Simhash. 886-894 - David B. Smith, Vibhav Gogate:
Loopy Belief Propagation in the Presence of Determinism. 895-903 - Arno Solin, Simo Särkkä:
Explicit Link Between Periodic Covariance Functions and State Space Models. 904-912 - Vassilios Stathopoulos, Veronica Zamora-Gutierrez, Kate E. Jones, Mark A. Girolami:
Bat Call Identification with Gaussian Process Multinomial Probit Regression and a Dynamic Time Warping Kernel. 913-921 - Rebecca C. Steorts, Rob Hall, Stephen E. Fienberg:
SMERED: A Bayesian Approach to Graphical Record Linkage and De-duplication. 922-930 - Siqi Sun, Yuancheng Zhu, Jinbo Xu:
Adaptive Variable Clustering in Gaussian Graphical Models. 931-939 - Partha Pratim Talukdar, William W. Cohen:
Scaling Graph-based Semi Supervised Learning to Large Number of Labels Using Count-Min Sketch. 940-947 - Divyanshu Vats, Richard G. Baraniuk:
Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse Regression. 948-957 - Divyanshu Vats, Robert D. Nowak, Richard G. Baraniuk:
Active Learning for Undirected Graphical Model Selection. 958-967 - Max Vladymyrov, Miguel Á. Carreira-Perpiñán:
Linear-time training of nonlinear low-dimensional embeddings. 968-977 - Huahua Wang, Farideh Fazayeli, Soumyadeep Chatterjee, Arindam Banerjee:
Gaussian Copula Precision Estimation with Missing Values. 978-986 - Joseph Wang, Kirill Trapeznikov, Venkatesh Saligrama:
An LP for Sequential Learning Under Budgets. 987-995 - Shusen Wang, Zhihua Zhang:
Efficient Algorithms and Error Analysis for the Modified Nystrom Method. 996-1004 - Ziyu Wang, Babak Shakibi, Lin Jin, Nando de Freitas:
Bayesian Multi-Scale Optimistic Optimization. 1005-1014 - Richard Wilkinson:
Accelerating ABC methods using Gaussian processes. 1015-1023 - Frank D. Wood, Jan-Willem van de Meent, Vikash Mansinghka:
A New Approach to Probabilistic Programming Inference. 1024-1032 - Shan Xue, Alan Fern, Daniel Sheldon:
Dynamic Resource Allocation for Optimizing Population Diffusion. 1033-1041 - Eunho Yang, Yulia Baker, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu:
Mixed Graphical Models via Exponential Families. 1042-1050 - Juemin Yang, Fang Han, Rafael A. Irizarry, Han Liu:
Context Aware Group Nearest Shrunken Centroids in Large-Scale Genomic Studies. 1051-1059 - Justin Yang, Christina Han, Edoardo M. Airoldi:
Nonparametric estimation and testing of exchangeable graph models. 1060-1067 - Lingfeng Yang, Pat Hanrahan, Noah D. Goodman:
Generating Efficient MCMC Kernels from Probabilistic Programs. 1068-1076 - Dani Yogatama, Gideon Mann:
Efficient Transfer Learning Method for Automatic Hyperparameter Tuning. 1077-1085 - Wenliang Zhong, James Tin-Yau Kwok:
Accelerated Stochastic Gradient Method for Composite Regularization. 1086-1094 - Joey Tianyi Zhou, Ivor W. Tsang, Sinno Jialin Pan, Mingkui Tan:
Heterogeneous Domain Adaptation for Multiple Classes. 1095-1103
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