Papers
Student-t Processes as Alternatives to Gaussian Processes
Amar Shah, Andrew Wilson, Zoubin Ghahramani
Estimating Dependency Structures for non-Gaussian Components with Linear and Energy Correlations
Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno et al.
Lifted MAP Inference for Markov Logic Networks
Somdeb Sarkhel, Deepak Venugopal, Parag Singla et al.
Class Proportion Estimation with Application to Multiclass Anomaly Rejection
Tyler Sanderson, Clayton Scott
Learning Structured Models with the AUC Loss and Its Generalizations
Nir Rosenfeld, Ofer Meshi, Danny Tarlow et al.
Sequential crowdsourced labeling as an epsilon-greedy exploration in a Markov Decision Process
Vikas Raykar, Priyanka Agrawal
Cluster Canonical Correlation Analysis
Nikhil Rasiwasia, Dhruv Mahajan, Vijay Mahadevan et al.
Black Box Variational Inference
Rajesh Ranganath, Sean Gerrish, David Blei
An Analysis of Active Learning with Uniform Feature Noise
Aaditya Ramdas, Barnabas Poczos, Aarti Singh et al.
Connected Sub-graph Detection
Jing Qian, Venkatesh Saligrama, Yuting Chen
An inclusion optimal algorithm for chain graph structure learning
Jose Peña, Dag Sonntag, Jens Nielsen
Expectation Propagation for Likelihoods Depending on an Inner Product of Two Multivariate Random Variables
Tomi Peltola, Pasi Jylänki, Aki Vehtari
An Efficient Algorithm for Large Scale Compressive Feature Learning
Hristo Paskov, John Mitchell, Trevor Hastie
Learning Bounded Tree-width Bayesian Networks using Integer Linear Programming
Pekka Parviainen, Hossein Shahrabi Farahani, Jens Lagergren
Spoofing Large Probability Mass Functions to Improve Sampling Times and Reduce Memory Costs
Jon Parker, Hans Engler
LAMORE: A Stable, Scalable Approach to Latent Vector Autoregressive Modeling of Categorical Time Series
Yubin Park, Carlos Carvalho, Joydeep Ghosh
To go deep or wide in learning?
Gaurav Pandey, Ambedkar Dukkipati
FuSSO: Functional Shrinkage and Selection Operator
Junier Oliva, Barnabas Poczos, Timothy Verstynen et al.
Fast Distribution To Real Regression
Junier Oliva, Willie Neiswanger, Barnabas Poczos et al.
Scaling Nonparametric Bayesian Inference via Subsample-Annealing
Fritz Obermeyer, Jonathan Glidden, Eric Jonas
Joint Structure Learning of Multiple Non-Exchangeable Networks
Chris Oates, Sach Mukherjee
Robust Forward Algorithms via PAC-Bayes and Laplace Distributions
Asaf Noy, Koby Crammer
Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence
Yung-Kyun Noh, Masashi Sugiyama, Song Liu et al.
The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling
Willie Neiswanger, Frank Wood, Eric Xing