Papers
Understanding and Evaluating Sparse Linear Discriminant Analysis
Yi Wu, David Wipf, Jeong-Min Yun
Unifying Local Consistency and MAX SAT Relaxations for Scalable Inference with Rounding Guarantees
Stephen Bach, Bert Huang, Lise Getoor
Variance Reduction via Antithetic Markov Chains
James Neufeld, Dale Schuurmans, Michael Bowling
WASP: Scalable Bayes via barycenters of subset posteriors
Sanvesh Srivastava, Volkan Cevher, Quoc Dinh et al.
Accelerated Stochastic Gradient Method for Composite Regularization
Wenliang Zhong, James Kwok
Accelerating ABC methods using Gaussian processes
Richard Wilkinson
Active Area Search via Bayesian Quadrature
Yifei Ma, Roman Garnett, Jeff Schneider
Active Boundary Annotation using Random MAP Perturbations
Subhransu Maji, Tamir Hazan, Tommi Jaakkola
Active Learning for Undirected Graphical Model Selection
Divyanshu Vats, Robert Nowak, Richard Baraniuk
Adaptive Variable Clustering in Gaussian Graphical Models
Siqi Sun, Yuancheng Zhu, Jinbo Xu
A Finite-Sample Generalization Bound for Semiparametric Regression: Partially Linear Models
Ruitong Huang, Csaba Szepesvari
A Gaussian Latent Variable Model for Large Margin Classification of Labeled and Unlabeled Data
Do-kyum Kim, Matthew Der, Lawrence Saul
A Geometric Algorithm for Scalable Multiple Kernel Learning
John Moeller, Parasaran Raman, Suresh Venkatasubramanian et al.
A Level-set Hit-and-run Sampler for Quasi-Concave Distributions
Shane Jensen, Dean Foster
Algebraic Reconstruction Bounds and Explicit Inversion for Phase Retrieval at the Identifiability Threshold
Franz Király, Martin Ehler
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 Deisenroth
An Analysis of Active Learning with Uniform Feature Noise
Aaditya Ramdas, Barnabas Poczos, Aarti Singh et al.
An Efficient Algorithm for Large Scale Compressive Feature Learning
Hristo Paskov, John Mitchell, Trevor Hastie
A New Approach to Probabilistic Programming Inference
Frank Wood, Jan Willem Meent, Vikash Mansinghka
A New Perspective on Learning Linear Separators with Large L_qL_p Margins
Maria-Florina Balcan, Christopher Berlind
An inclusion optimal algorithm for chain graph structure learning
Jose Peña, Dag Sonntag, Jens Nielsen
An LP for Sequential Learning Under Budgets
Joseph Wang, Kirill Trapeznikov, Venkatesh Saligrama
A Non-parametric Conditional Factor Regression Model for Multi-Dimensional Input and Response
Ava Bargi, Richard Yi Xu, Zoubin Ghahramani et al.
Approximate Slice Sampling for Bayesian Posterior Inference
Christopher DuBois, Anoop Korattikara, Max Welling et al.