Papers
Statistical Optimization of Non-Negative Matrix Factorization
Anoop Korattikara Balan, Levi Boyles, Max Welling et al.
Switch-Reset Models : Exact and Approximate Inference
Chris Bracegirdle, David Barber
The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling
John Paisley, Chong Wang, David Blei
The Neural Autoregressive Distribution Estimator
Hugo Larochelle, Iain Murray
The Sample Complexity of Self-Verifying Bayesian Active Learning
Liu Yang, Steve Hanneke, Jaime Carbonell
Tighter Relaxations for MAP-MRF Inference: A Local Primal-Dual Gap based Separation Algorithm
Dhruv Batra, Sebastian Nowozin, Pushmeet Kohli
TopicFlow Model: Unsupervised Learning of Topic-specific Influences of Hyperlinked Documents
Ramesh Nallapati, Daniel McFarland, Christopher Manning
Two-Layer Multiple Kernel Learning
Jinfeng Zhuang, Ivor W. Tsang, Steven C.H. Hoi
Unsupervised Supervised Learning II: Margin-Based Classification without Labels
Krishnakumar Balasubramanian, Pinar Donmez, Guy Lebanon
Active Sequential Learning with Tactile Feedback
Hannes Saal, Jo–Anne Ting, Sethu Vijayakumar
A generalization of the Multiple-try Metropolis algorithm for Bayesian estimation and model selection
Silvia Pandolfi, Francesco Bartolucci, Nial Friel
A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra
Ji Won Yoon, Simon Wilson, K. Hun Mok
A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping
Peter Torma, András György, Csaba Szepesvári
An Alternative Prior Process for Nonparametric Bayesian Clustering
Hanna Wallach, Shane Jensen, Lee Dicker et al.
A Potential-based Framework for Online Multi-class Learning with Partial Feedback
Shijun Wang, Rong Jin, Hamed Valizadegan
Approximate parameter inference in a stochastic reaction-diffusion model
Andreas Ruttor, Manfred Opper
Approximation of hidden Markov models by mixtures of experts with application to particle filtering
Jimmy Olsson, Jonas Ströjby
A Regularization Approach to Nonlinear Variable Selection
Lorenzo Rosasco, Matteo Santoro, Sofia Mosci et al.
A Weighted Multi-Sequence Markov Model For Brain Lesion Segmentation
Florence Forbes, Senan Doyle, Daniel Garcia–Lorenzo et al.
Bayesian Gaussian Process Latent Variable Model
Michalis Titsias, Neil D. Lawrence
Bayesian Generalized Kernel Models
Zhihua Zhang, Guang Dai, Donghui Wang et al.
Bayesian Online Learning for Multi-label and Multi-variate Performance Measures
Xinhua Zhang, Thore Graepel, Ralf Herbrich
Bayesian structure discovery in Bayesian networks with less space
Pekka Parviainen, Mikko Koivisto
Bayesian variable order Markov models
Christos Dimitrakakis
Boosted Optimization for Network Classification
Timothy Hancock, Hiroshi Mamitsuka