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Editors: Samuel Kaski, Jukka Corander
Preface
; Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:i-iv
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Decontamination 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
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Improved 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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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