AISTATS 2014

121 papers

A Finite-Sample Generalization Bound for Semiparametric Regression: Partially Linear Models Ruitong Huang, Csaba Szepesvári

A Gaussian Latent Variable Model for Large Margin Classification of Labeled and Unlabeled Data Do-kyum Kim, Matthew F. Der, Lawrence K. Saul

A Geometric Algorithm for Scalable Multiple Kernel Learning John Moeller, Parasaran Raman, Suresh Venkatasubramanian, Avishek Saha

A Level-Set Hit-and-Run Sampler for Quasi-Concave Distributions Shane T. Jensen, Dean P. Foster

A New Approach to Probabilistic Programming Inference Frank D. Wood, Jan-Willem van de Meent, Vikash Mansinghka

A New Perspective on Learning Linear Separators with Large \(L_qL_p\) Margins Maria-Florina Balcan, Christopher Berlind

A Non-Parametric Conditional Factor Regression Model for Multi-Dimensional Input and Response Ava Bargi, Richard Yi Da Xu, Zoubin Ghahramani, Massimo Piccardi

A Statistical Model for Event Sequence Data Kevin Heins, Hal S. Stern

A Stepwise Uncertainty Reduction Approach to Constrained Global Optimization Victor Picheny

Accelerated Stochastic Gradient Method for Composite Regularization Wenliang Zhong, James Tin-Yau Kwok

Accelerating ABC Methods Using Gaussian Processes Richard Wilkinson

Active Area Search via Bayesian Quadrature Yifei Ma, Roman Garnett, Jeff G. Schneider

Active Boundary Annotation Using Random MAP Perturbations Subhransu Maji, Tamir Hazan, Tommi S. Jaakkola

Active Learning for Undirected Graphical Model Selection Divyanshu Vats, Robert D. Nowak, Richard G. Baraniuk

Adaptive Variable Clustering in Gaussian Graphical Models Siqi Sun, Yuancheng Zhu, Jinbo Xu

Algebraic Reconstruction Bounds and Explicit Inversion for Phase Retrieval at the Identifiability Threshold Franz J. Király, Martin Ehler

An Analysis of Active Learning with Uniform Feature Noise Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman

An Efficient Algorithm for Large Scale Compressive Feature Learning Hristo S. Paskov, John C. Mitchell, Trevor J. Hastie

An Inclusion Optimal Algorithm for Chain Graph Structure Learning José M. Peña, Dag Sonntag, Jens Dalgaard Nielsen

An LP for Sequential Learning Under Budgets Joseph Wang, Kirill Trapeznikov, Venkatesh Saligrama

Analysis of Empirical MAP and Empirical Partially Bayes: Can They Be Alternatives to Variational Bayes? Shinichi Nakajima, Masashi Sugiyama

Analytic Long-Term Forecasting with Periodic Gaussian Processes Nooshin HajiGhassemi, Marc Peter Deisenroth

Approximate Slice Sampling for Bayesian Posterior Inference Christopher DuBois, Anoop Korattikara Balan, Max Welling, Padhraic Smyth

Average Case Analysis of High-Dimensional Block-Sparse Recovery and Regression for Arbitrary Designs Waheed U. Bajwa, Marco F. Duarte, A. Robert Calderbank

Avoiding Pathologies in Very Deep Networks David Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani

Bat Call Identification with Gaussian Process Multinomial Probit Regression and a Dynamic Time Warping Kernel Vassilios Stathopoulos, Veronica Zamora-Gutierrez, Kate E. Jones, Mark A. Girolami

Bayesian Logistic Gaussian Process Models for Dynamic Networks Daniele Durante, David B. Dunson

Bayesian Multi-Scale Optimistic Optimization Ziyu Wang, Babak Shakibi, Lin Jin, Nando de Freitas

Bayesian Nonparametric Poisson Factorization for Recommendation Systems Prem Gopalan, Francisco J. R. Ruiz, Rajesh Ranganath, David M. Blei

Bayesian Switching Interaction Analysis Under Uncertainty Zoran Dzunic, John W. Fisher Iii

Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence Yung-Kyun Noh, Masashi Sugiyama, Song Liu, Marthinus Christoffel du Plessis, Frank Chongwoo Park, Daniel D. Lee

Black Box Variational Inference Rajesh Ranganath, Sean Gerrish, David M. Blei

Characterizing EVOI-Sufficient K-Response Query Sets in Decision Problems Robert Cohn, Satinder Singh, Edmund H. Durfee

Class Proportion Estimation with Application to Multiclass Anomaly Rejection Tyler Sanderson, Clayton Scott

Cluster Canonical Correlation Analysis Nikhil Rasiwasia, Dhruv Mahajan, Vijay Mahadevan, Gaurav Aggarwal

Collaborative Ranking for Local Preferences Berk Kapicioglu, David S. Rosenberg, Robert E. Schapire, Tony Jebara

Computational Education Using Latent Structured Prediction Tanja Käser, Alexander G. Schwing, Tamir Hazan, Markus H. Gross

Connected Sub-Graph Detection Jing Qian, Venkatesh Saligrama, Yuting Chen

Context Aware Group Nearest Shrunken Centroids in Large-Scale Genomic Studies Juemin Yang, Fang Han, Rafael A. Irizarry, Han Liu

Decontamination of Mutually Contaminated Models Gilles Blanchard, Clayton Scott

Distributed Optimization of Deeply Nested Systems Miguel Á. Carreira-Perpiñán, Weiran Wang

Doubly Aggressive Selective Sampling Algorithms for Classification Koby Crammer

Dynamic Resource Allocation for Optimizing Population Diffusion Shan Xue, Alan Fern, Daniel Sheldon

Efficient Algorithms and Error Analysis for the Modified Nystrom Method Shusen Wang, Zhihua Zhang

Efficient Distributed Topic Modeling with Provable Guarantees Weicong Ding, Mohammad H. Rohban, Prakash Ishwar, Venkatesh Saligrama

Efficient Inference for Complex Queries on Complex Distributions Lili Dworkin, Michael J. Kearns, Lirong Xia

Efficient Lifting of MAP LP Relaxations Using K-Locality Martin Mladenov, Kristian Kersting, Amir Globerson

Efficient Low-Rank Stochastic Gradient Descent Methods for Solving Semidefinite Programs Jianhui Chen, Tianbao Yang, Shenghuo Zhu

Efficient Transfer Learning Method for Automatic Hyperparameter Tuning Dani Yogatama, Gideon Mann

Efficiently Enforcing Diversity in Multi-Output Structured Prediction Abner Guzmán-Rivera, Pushmeet Kohli, Dhruv Batra, Rob A. Rutenbar

Estimating Dependency Structures for Non-Gaussian Components with Linear and Energy Correlations Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvärinen

Expectation Propagation for Likelihoods Depending on an Inner Product of Two Multivariate Random Variables Tomi Peltola, Pasi Jylänki, Aki Vehtari

Explicit Link Between Periodic Covariance Functions and State Space Models Arno Solin, Simo Särkkä

Exploiting the Limits of Structure Learning via Inherent Symmetry Peng He, Changshui Zhang

Fast Distribution to Real Regression Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing

Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models on Big Data Abhimanu Kumar, Alex Beutel, Qirong Ho, Eric P. Xing

Fully-Automatic Bayesian Piecewise Sparse Linear Models Riki Eto, Ryohei Fujimaki, Satoshi Morinaga, Hiroshi Tamano

FuSSO: Functional Shrinkage and Selection Operator Junier B. Oliva, Barnabás Póczos, Timothy D. Verstynen, Aarti Singh, Jeff G. Schneider, Fang-Cheng Yeh, Wen-Yih Isaac Tseng

Gaussian Copula Precision Estimation with Missing Values Huahua Wang, Farideh Fazayeli, Soumyadeep Chatterjee, Arindam Banerjee

Generating Efficient MCMC Kernels from Probabilistic Programs Lingfeng Yang, Pat Hanrahan, Noah D. Goodman

Global Optimization Methods for Extended Fisher Discriminant Analysis Satoru Iwata, Yuji Nakatsukasa, Akiko Takeda

Heterogeneous Domain Adaptation for Multiple Classes Joey Tianyi Zhou, Ivor W. Tsang, Sinno Jialin Pan, Mingkui Tan

High-Dimensional Density Ratio Estimation with Extensions to Approximate Likelihood Computation Rafael Izbicki, Ann B. Lee, Chad Schafer

Hybrid Discriminative-Generative Approach with Gaussian Processes Ricardo Andrade Pacheco, James Hensman, Max Zwiessele, Neil D. Lawrence

Improved Bounds for Online Learning over the Permutahedron and Other Ranking Polytopes Nir Ailon

In Defense of Minhash over Simhash Anshumali Shrivastava, Ping Li

Incremental Tree-Based Inference with Dependent Normalized Random Measures Juho Lee, Seungjin Choi

Information-Theoretic Characterization of Sparse Recovery Cem Aksoylar, Venkatesh Saligrama

Interpretable Sparse High-Order Boltzmann Machines Martin Renqiang Min, Xia Ning, Chao Cheng, Mark Gerstein

Joint Structure Learning of Multiple Non-Exchangeable Networks Chris J. Oates, Sach Mukherjee

Jointly Informative Feature Selection Leonidas Lefakis, François Fleuret

LAMORE: A Stable, Scalable Approach to Latent Vector Autoregressive Modeling of Categorical Time Series Yubin Park, Carlos Carvalho, Joydeep Ghosh

Latent Gaussian Models for Topic Modeling Changwei Hu, Eunsu Ryu, David E. Carlson, Yingjian Wang, Lawrence Carin

Learning and Evaluation in Presence of Non-I.i.d. Label Noise Nico Görnitz, Anne Porbadnigk, Alexander Binder, Claudia Sannelli, Mikio L. Braun, Klaus-Robert Müller, Marius Kloft

Learning Bounded Tree-Width Bayesian Networks Using Integer Linear Programming Pekka Parviainen, Hossein Shahrabi Farahani, Jens Lagergren

Learning Heterogeneous Hidden Markov Random Fields Jie Liu, Chunming Zhang, Elizabeth S. Burnside, David Page

Learning Optimal Bounded Treewidth Bayesian Networks via Maximum Satisfiability Jeremias Berg, Matti Järvisalo, Brandon M. Malone

Learning Structured Models with the AUC Loss and Its Generalizations Nir Rosenfeld, Ofer Meshi, Daniel Tarlow, Amir Globerson

Learning with Maximum A-Posteriori Perturbation Models Andreea Gane, Tamir Hazan, Tommi S. Jaakkola

Lifted MAP Inference for Markov Logic Networks Somdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav Gogate

Linear-Time Training of Nonlinear Low-Dimensional Embeddings Max Vladymyrov, Miguel Á. Carreira-Perpiñán

Loopy Belief Propagation in the Presence of Determinism David B. Smith, Vibhav Gogate

Low-Rank Spectral Learning Alex Kulesza, N. Raj Rao, Satinder Singh

Mixed Graphical Models via Exponential Families Eunho Yang, Yulia Baker, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu

Near Optimal Bayesian Active Learning for Decision Making Shervin Javdani, Yuxin Chen, Amin Karbasi, Andreas Krause, Drew Bagnell, Siddhartha S. Srinivasa

New Bounds on Compressive Linear Least Squares Regression Ata Kabán

Non-Asymptotic Analysis of Relational Learning with One Network Peng He, Changshui Zhang

Nonparametric Estimation and Testing of Exchangeable Graph Models Justin Yang, Christina Han, Edoardo M. Airoldi

On Correlation and Budget Constraints in Model-Based Bandit Optimization with Application to Automatic Machine Learning Matthew W. Hoffman, Bobak Shahriari, Nando de Freitas

On Estimating Causal Effects Based on Supplemental Variables Takahiro Hayashi, Manabu Kuroki

On the Testability of Models with Missing Data Karthika Mohan, Judea Pearl

Online Passive-Aggressive Algorithms for Non-Negative Matrix Factorization and Completion Mathieu Blondel, Yotaro Kubo, Naonori Ueda

Optimality of Thompson Sampling for Gaussian Bandits Depends on Priors Junya Honda, Akimichi Takemura

PAC-Bayesian Collective Stability Ben London, Bert Huang, Ben Taskar, Lise Getoor

PAC-Bayesian Theory for Transductive Learning Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy

Pan-Sharpening with a Bayesian Nonparametric Dictionary Learning Model Xinghao Ding, Yiyong Jiang, Yue Huang, John W. Paisley

Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse Regression Divyanshu Vats, Richard G. Baraniuk

Probabilistic Solutions to Differential Equations and Their Application to Riemannian Statistics Philipp Hennig, Søren Hauberg

Random Bayesian Networks with Bounded Indegree Eunice Yuh-Jie Chen, Judea Pearl

Recovering Distributions from Gaussian RKHS Embeddings Motonobu Kanagawa, Kenji Fukumizu

Robust Forward Algorithms via PAC-Bayes and Laplace Distributions Asaf Noy, Koby Crammer

Robust Learning of Inhomogeneous PMMs Ralf Eggeling, Teemu Roos, Petri Myllymäki, Ivo Grosse

Robust Stochastic Principal Component Analysis John Goes, Teng Zhang, Raman Arora, Gilad Lerman

Scalable Collaborative Bayesian Preference Learning Mohammad Emtiyaz Khan, Young-Jun Ko, Matthias W. Seeger

Scalable Variational Bayesian Matrix Factorization with Side Information Yong-Deok Kim, Seungjin Choi

Scaling Graph-Based Semi Supervised Learning to Large Number of Labels Using Count-Min Sketch Partha Pratim Talukdar, William W. Cohen

Scaling Nonparametric Bayesian Inference via Subsample-Annealing Fritz Obermeyer, Jonathan Glidden, Eric Jonas

Selective Sampling with Drift Edward Moroshko, Koby Crammer

Sequential Crowdsourced Labeling as an Epsilon-Greedy Exploration in a Markov Decision Process Vikas C. Raykar, Priyanka Agrawal

Sketching the Support of a Probability Measure Joachim Giesen, Sören Laue, Lars Kuehne

SMERED: A Bayesian Approach to Graphical Record Linkage and De-Duplication Rebecca C. Steorts, Rob Hall, Stephen E. Fienberg

Sparse Bayesian Variable Selection for the Identification of Antigenic Variability in the Foot-and-Mouth Disease Virus Vinny Davies, Richard E. Reeve, William T. Harvey, Francois F. Maree, Dirk Husmeier

Sparsity and the Truncated \(l2\)-Norm Lee H. Dicker

Spoofing Large Probability Mass Functions to Improve Sampling Times and Reduce Memory Costs Jon Parker, Hans Engler

Student-T Processes as Alternatives to Gaussian Processes Amar Shah, Andrew Gordon Wilson, Zoubin Ghahramani

The Dependent Dirichlet Process Mixture of Objects for Detection-Free Tracking and Object Modeling Willie Neiswanger, Frank D. Wood, Eric P. Xing

Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees Jean Honorio, Tommi S. Jaakkola

Tilted Variational Bayes James Hensman, Max Zwiessele, Neil D. Lawrence

To Go Deep or Wide in Learning? Gaurav Pandey, Ambedkar Dukkipati

Towards Building a Crowd-Sourced Sky mAP Dustin Lang, David W. Hogg, Bernhard Schölkopf

Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection Jyri J. Kivinen, Christopher K. I. Williams, Nicolas Heess