Abstract is missing.
- PrefaceSamuel Kaski, Jukka Corander. [doi]
- Decontamination of Mutually Contaminated ModelsGilles Blanchard, Clayton Scott. 1-9 [doi]
- Distributed optimization of deeply nested systemsMiguel Á Carreira-Perpiñán, Weiran Wang. 10-19 [doi]
- Analysis of Empirical MAP and Empirical Partially Bayes: Can They be Alternatives to Variational Bayes?Shinichi Nakajima, Masashi Sugiyama. 20-28 [doi]
- Improved Bounds for Online Learning Over the Permutahedron and Other Ranking PolytopesNir Ailon. 29-37 [doi]
- Information-Theoretic Characterization of Sparse RecoveryCem Aksoylar, Venkatesh Saligrama. 38-46 [doi]
- Hybrid Discriminative-Generative Approach with Gaussian ProcessesRicardo Andrade Pacheco, James Hensman, Max Zwiessele, Neil D. Lawrence. 47-56 [doi]
- Average Case Analysis of High-Dimensional Block-Sparse Recovery and Regression for Arbitrary DesignsWaheed U. Bajwa, Marco F. Duarte, A. Robert Calderbank. 57-67 [doi]
- A New Perspective on Learning Linear Separators with Large \(L_qL_p\) MarginsMaria-Florina Balcan, Christopher Berlind. 68-76 [doi]
- A Non-parametric Conditional Factor Regression Model for Multi-Dimensional Input and ResponseAva Bargi, Richard Yi Da Xu, Zoubin Ghahramani, Massimo Piccardi. 77-85 [doi]
- Learning Optimal Bounded Treewidth Bayesian Networks via Maximum SatisfiabilityJeremias Berg, Matti Järvisalo, Brandon Malone. 86-95 [doi]
- Online Passive-Aggressive Algorithms for Non-Negative Matrix Factorization and CompletionMathieu Blondel, Yotaro Kubo, Naonori Ueda. 96-104 [doi]
- PAC-Bayesian Theory for Transductive LearningLuc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy. 105-113 [doi]
- Random Bayesian networks with bounded indegreeEunice Yuh-Jie Chen, Judea Pearl. 114-121 [doi]
- Efficient Low-Rank Stochastic Gradient Descent Methods for Solving Semidefinite ProgramsJianhui Chen, Tianbao Yang, Shenghuo Zhu. 122-130 [doi]
- Characterizing EVOI-Sufficient k-Response Query Sets in Decision ProblemsRobert Cohn, Satinder P. Singh, Edmund H. Durfee. 131-139 [doi]
- Doubly Aggressive Selective Sampling Algorithms for ClassificationKoby Crammer. 140-148 [doi]
- Sparse Bayesian Variable Selection for the Identification of Antigenic Variability in the Foot-and-Mouth Disease VirusVinny Davies, Richard Reeve, William Harvey, Francois F. Maree, Dirk Husmeier. 149-158 [doi]
- Sparsity and the truncated \(l^2\)-normLee H. Dicker. 159-166 [doi]
- Efficient Distributed Topic Modeling with Provable GuaranteesWeicong Ding, Mohammad H. Rohban, Prakash Ishwar, Venkatesh Saligrama. 167-175 [doi]
- Pan-sharpening with a Bayesian nonparametric dictionary learning modelXinghao Ding, Yiyong Jiang, Yue Huang, John Paisley. 176-184 [doi]
- Approximate Slice Sampling for Bayesian Posterior InferenceChristopher DuBois, Anoop Korattikara Balan, Max Welling, Padhraic Smyth. 185-193 [doi]
- Bayesian Logistic Gaussian Process Models for Dynamic NetworksDaniele Durante, David B. Dunson. 194-201 [doi]
- Avoiding pathologies in very deep networksDavid K. Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani. 202-210 [doi]
- Efficient Inference for Complex Queries on Complex DistributionsLili Dworkin, Michael Kearns, Lirong Xia. 211-219 [doi]
- Bayesian Switching Interaction Analysis Under UncertaintyZoran Dzunic, John Fisher III. 220-228 [doi]
- Robust learning of inhomogeneous PMMsRalf Eggeling, Teemu Roos, Petri Myllymäki, Ivo Grosse. 229-237 [doi]
- Fully-Automatic Bayesian Piecewise Sparse Linear ModelsRiki Eto, Ryohei Fujimaki, Satoshi Morinaga, Hiroshi Tamano. 238-246 [doi]
- Learning with Maximum A-Posteriori Perturbation ModelsAndreea Gane, Tamir Hazan, Tommi Jaakkola. 247-256 [doi]
- Sketching the Support of a Probability MeasureJoachim Giesen, Sören Laue, Lars Kuehne. 257-265 [doi]
- Robust Stochastic Principal Component AnalysisJohn Goes, Teng Zhang, Raman Arora, Gilad Lerman. 266-274 [doi]
- Bayesian Nonparametric Poisson Factorization for Recommendation SystemsPrem Gopalan, Francisco J. Ruiz, Rajesh Ranganath, David M. Blei. 275-283 [doi]
- Efficiently Enforcing Diversity in Multi-Output Structured PredictionAbner Guzmán-Rivera, Pushmeet Kohli, Dhruv Batra, Rob A. Rutenbar. 284-292 [doi]
- Learning and Evaluation in Presence of Non-i.i.d. Label NoiseNico Görnitz, Anne Porbadnigk, Alexander Binder, Claudia Sannelli, Mikio L. Braun, Klaus-Robert Müller, Marius Kloft. 293-302 [doi]
- Analytic Long-Term Forecasting with Periodic Gaussian ProcessesNooshin HajiGhassemi, Marc Peter Deisenroth. 303-311 [doi]
- On Estimating Causal Effects based on Supplemental VariablesTakahiro Hayashi, Manabu Kuroki. 312-319 [doi]
- Non-Asymptotic Analysis of Relational Learning with One NetworkPeng He, Changshui Zhang. 320-327 [doi]
- Exploiting the Limits of Structure Learning via Inherent SymmetryPeng He, Changshui Zhang. 328-337 [doi]
- A Statistical Model for Event Sequence DataKevin Heins, Hal Stern. 338-346 [doi]
- Probabilistic Solutions to Differential Equations and their Application to Riemannian StatisticsPhilipp Hennig, Søren Hauberg. 347-355 [doi]
- Tilted Variational BayesJames Hensman, Max Zwiessele, Neil Lawrence. 356-364 [doi]
- On correlation and budget constraints in model-based bandit optimization with application to automatic machine learningMatthew D. Hoffman, Bobak Shahriari, Nando de Freitas. 365-374 [doi]
- Optimality of Thompson Sampling for Gaussian Bandits Depends on PriorsJunya Honda, Akimichi Takemura. 375-383 [doi]
- Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample GuaranteesJean Honorio, Tommi Jaakkola. 384-392 [doi]
- Latent Gaussian Models for Topic ModelingChangwei Hu, Eunsu Ryu, David Carlson, Yingjian Wang, Lawrence Carin. 393-401 [doi]
- A Finite-Sample Generalization Bound for Semiparametric Regression: Partially Linear ModelsRuitong Huang, Csaba Szepesvári. 402-410 [doi]
- Global Optimization Methods for Extended Fisher Discriminant AnalysisSatoru Iwata, Yuji Nakatsukasa, Akiko Takeda. 411-419 [doi]
- High-Dimensional Density Ratio Estimation with Extensions to Approximate Likelihood ComputationRafael Izbicki, Ann Lee, Chad Schafer. 420-429 [doi]
- Near Optimal Bayesian Active Learning for Decision MakingShervin Javdani, Yuxin Chen, Amin Karbasi, Andreas Krause, Drew Bagnell, Siddhartha S. Srinivasa. 430-438 [doi]
- A Level-set Hit-and-run Sampler for Quasi-Concave DistributionsShane Jensen, Dean Foster. 439-447 [doi]
- New Bounds on Compressive Linear Least Squares RegressionAta Kaban. 448-456 [doi]
- Recovering Distributions from Gaussian RKHS EmbeddingsMotonobu Kanagawa, Kenji Fukumizu. 457-465 [doi]
- Collaborative Ranking for Local PreferencesBerk Kapicioglu, David Rosenberg, Robert E. Schapire, Tony Jebara. 466-474 [doi]
- Scalable Collaborative Bayesian Preference LearningMohammad Emtiyaz Khan, Young Jun Ko, Matthias Seeger. 475-483 [doi]
- A Gaussian Latent Variable Model for Large Margin Classification of Labeled and Unlabeled DataDo-kyum Kim, Matthew Der, Lawrence K. Saul. 484-492 [doi]
- Scalable Variational Bayesian Matrix Factorization with Side InformationYong-Deok Kim, Seungjin Choi. 493-502 [doi]
- Algebraic Reconstruction Bounds and Explicit Inversion for Phase Retrieval at the Identifiability ThresholdFranz J. Király, Martin Ehler. 503-511 [doi]
- Visual Boundary Prediction: A Deep Neural Prediction Network and Quality DissectionJyri J. Kivinen, Christopher K. I. Williams, Nicolas Heess. 512-521 [doi]
- Low-Rank Spectral LearningAlex Kulesza, N. Raj Rao, Satinder Singh. 522-530 [doi]
- Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models on Big DataAbhimanu Kumar, Alex Beutel, Qirong Ho, Eric P. Xing. 531-539 [doi]
- Computational Education using Latent Structured PredictionTanja Käser, Alexander G. Schwing, Tamir Hazan, Markus H. Gross. 540-548 [doi]
- Towards building a Crowd-Sourced Sky MapDustin Lang, David W. Hogg, Bernhard Schölkopf. 549-557 [doi]
- Incremental Tree-Based Inference with Dependent Normalized Random MeasuresJuho Lee, Seungjin Choi. 558-566 [doi]
- Jointly Informative Feature SelectionLeonidas Lefakis, François Fleuret. 567-575 [doi]
- Learning Heterogeneous Hidden Markov Random FieldsJie Liu, Chunming Zhang, Elizabeth S. Burnside, David Page. 576-584 [doi]
- PAC-Bayesian Collective StabilityBen London, Bert Huang, Ben Taskar, Lise Getoor. 585-594 [doi]
- Active Area Search via Bayesian QuadratureYifei Ma, Roman Garnett, Jeff G. Schneider. 595-603 [doi]
- Active Boundary Annotation using Random MAP PerturbationsSubhransu Maji, Tamir Hazan, Tommi Jaakkola. 604-613 [doi]
- Interpretable Sparse High-Order Boltzmann MachinesMartin Renqiang Min, Xia Ning, Chao Cheng, Mark Gerstein. 614-622 [doi]
- Efficient Lifting of MAP LP Relaxations Using k-LocalityMartin Mladenov, Kristian Kersting, Amir Globerson. 623-632 [doi]
- A Geometric Algorithm for Scalable Multiple Kernel LearningJohn Moeller, Parasaran Raman, Suresh Venkatasubramanian, Avishek Saha. 633-642 [doi]
- On the Testability of Models with Missing DataKarthika Mohan, Judea Pearl. 643-650 [doi]
- Selective Sampling with DriftEdward Moroshko, Koby Crammer. 651-659 [doi]
- The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object ModelingWillie Neiswanger, Frank Wood, Eric P. Xing. 660-668 [doi]
- Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler DivergenceYung-Kyun Noh, Masashi Sugiyama, Song Liu, Marthinus Christoffel du Plessis, Frank Chongwoo Park, Daniel D. Lee. 669-677 [doi]
- Robust Forward Algorithms via PAC-Bayes and Laplace DistributionsAsaf Noy, Koby Crammer. 678-686 [doi]
- Joint Structure Learning of Multiple Non-Exchangeable NetworksChris J. Oates, Sach Mukherjee. 687-695 [doi]
- Scaling Nonparametric Bayesian Inference via Subsample-AnnealingFritz Obermeyer, Jonathan Glidden, Eric Jonas. 696-705 [doi]
- Fast Distribution To Real RegressionJunier B. Oliva, Willie Neiswanger, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing. 706-714 [doi]
- FuSSO: Functional Shrinkage and Selection OperatorJunier B. Oliva, Barnabás Póczos, Timothy Verstynen, Aarti Singh, Jeff G. Schneider, Fang-Cheng Yeh, Wen-Yih Isaac Tseng. 715-723 [doi]
- To go deep or wide in learning?Gaurav Pandey, Ambedkar Dukkipati. 724-732 [doi]
- LAMORE: A Stable, Scalable Approach to Latent Vector Autoregressive Modeling of Categorical Time SeriesYubin Park, Carlos Carvalho, Joydeep Ghosh. 733-742 [doi]
- Spoofing Large Probability Mass Functions to Improve Sampling Times and Reduce Memory CostsJon Parker, Hans Engler. 743-750 [doi]
- Learning Bounded Tree-width Bayesian Networks using Integer Linear ProgrammingPekka Parviainen, Hossein Shahrabi Farahani, Jens Lagergren. 751-759 [doi]
- An Efficient Algorithm for Large Scale Compressive Feature LearningHristo S. Paskov, John C. Mitchell, Trevor J. Hastie. 760-768 [doi]
- Expectation Propagation for Likelihoods Depending on an Inner Product of Two Multivariate Random VariablesTomi Peltola, Pasi Jylänki, Aki Vehtari. 769-777 [doi]
- An inclusion optimal algorithm for chain graph structure learningJose M. Peña, Dag Sonntag, Jens Nielsen. 778-786 [doi]
- A Stepwise uncertainty reduction approach to constrained global optimizationVictor Picheny. 787-795 [doi]
- Connected Sub-graph DetectionJing Qian, Venkatesh Saligrama, Yuting Chen. 796-804 [doi]
- An Analysis of Active Learning with Uniform Feature NoiseAaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman. 805-813 [doi]
- Black Box Variational InferenceRajesh Ranganath, Sean Gerrish, David M. Blei. 814-822 [doi]
- Cluster Canonical Correlation AnalysisNikhil Rasiwasia, Dhruv Mahajan, Vijay Mahadevan, Gaurav Aggarwal. 823-831 [doi]
- Sequential crowdsourced labeling as an epsilon-greedy exploration in a Markov Decision ProcessVikas Raykar, Priyanka Agrawal. 832-840 [doi]
- Learning Structured Models with the AUC Loss and Its GeneralizationsNir Rosenfeld, Ofer Meshi, Daniel Tarlow, Amir Globerson. 841-849 [doi]
- Class Proportion Estimation with Application to Multiclass Anomaly RejectionTyler Sanderson, Clayton Scott. 850-858 [doi]
- Lifted MAP Inference for Markov Logic NetworksSomdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav Gogate. 859-867 [doi]
- Estimating Dependency Structures for non-Gaussian Components with Linear and Energy CorrelationsHiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvärinen. 868-876 [doi]
- Student-t Processes as Alternatives to Gaussian ProcessesAmar Shah, Andrew Gordon Wilson, Zoubin Ghahramani. 877-885 [doi]
- In Defense of Minhash over SimhashAnshumali Shrivastava, Ping Li 0001. 886-894 [doi]
- Loopy Belief Propagation in the Presence of DeterminismDavid Brodie Smith, Vibhav Gogate. 895-903 [doi]
- Explicit Link Between Periodic Covariance Functions and State Space ModelsArno Solin, Simo Särkkä. 904-912 [doi]
- Bat Call Identification with Gaussian Process Multinomial Probit Regression and a Dynamic Time Warping KernelVassilios Stathopoulos, Veronica Zamora-Gutierrez, Kate Jones, Mark Girolami. 913-921 [doi]
- SMERED: A Bayesian Approach to Graphical Record Linkage and De-duplicationRebecca Steorts, Rob Hall, Stephen E. Fienberg. 922-930 [doi]
- Adaptive Variable Clustering in Gaussian Graphical ModelsSiqi Sun, Yuancheng Zhu, Jinbo Xu. 931-939 [doi]
- Scaling Graph-based Semi Supervised Learning to Large Number of Labels Using Count-Min SketchPartha Talukdar, William Cohen. 940-947 [doi]
- Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse RegressionDivyanshu Vats, Richard G. Baraniuk. 948-957 [doi]
- Active Learning for Undirected Graphical Model SelectionDivyanshu Vats, Robert Nowak, Richard G. Baraniuk. 958-967 [doi]
- Linear-time training of nonlinear low-dimensional embeddingsMax Vladymyrov, Miguel Á. Carreira-Perpinan. 968-977 [doi]
- Gaussian Copula Precision Estimation with Missing ValuesHuahua Wang, Farideh Fazayeli, Soumyadeep Chatterjee, Arindam Banerjee. 978-986 [doi]
- An LP for Sequential Learning Under BudgetsJoseph Wang, Kirill Trapeznikov, Venkatesh Saligrama. 987-995 [doi]
- Efficient Algorithms and Error Analysis for the Modified Nystrom MethodShusen Wang, Zhihua Zhang. 996-1004 [doi]
- Bayesian Multi-Scale Optimistic OptimizationZiyu Wang, Babak Shakibi, Lin Jin, Nando de Freitas. 1005-1014 [doi]
- Accelerating ABC methods using Gaussian processesRichard Wilkinson. 1015-1023 [doi]
- A New Approach to Probabilistic Programming InferenceFrank Wood, Jan-Willem van de Meent, Vikash Mansinghka. 1024-1032 [doi]
- Dynamic Resource Allocation for Optimizing Population DiffusionShan Xue, Alan Fern, Daniel Sheldon. 1033-1041 [doi]
- Mixed Graphical Models via Exponential FamiliesEunho Yang, Yulia Baker, Pradeep D. Ravikumar, Genevera I. Allen, Zhandong Liu. 1042-1050 [doi]
- Context Aware Group Nearest Shrunken Centroids in Large-Scale Genomic StudiesJuemin Yang, Fang Han, Rafael A. Irizarry, Han Liu. 1051-1059 [doi]
- Nonparametric estimation and testing of exchangeable graph modelsJustin Yang, Christina Han, Edoardo Airoldi. 1060-1067 [doi]
- Generating Efficient MCMC Kernels from Probabilistic ProgramsLingfeng Yang, Pat Hanrahan, Noah D. Goodman. 1068-1076 [doi]
- Efficient Transfer Learning Method for Automatic Hyperparameter TuningDani Yogatama, Gideon Mann. 1077-1085 [doi]
- Accelerated Stochastic Gradient Method for Composite RegularizationWenliang Zhong, James Tin-Yau Kwok. 1086-1094 [doi]
- Heterogeneous Domain Adaptation for Multiple ClassesJoey Tianyi Zhou, Ivor W. Tsang, Sinno Jialin Pan, Mingkui Tan. 1095-1103 [doi]