Papers
Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse Regression
Divyanshu Vats, Richard 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 et al.
Robust Stochastic Principal Component Analysis
John Goes, Teng Zhang, Raman Arora et al.
Scalable Collaborative Bayesian Preference Learning
Mohammad Emtiyaz Khan, Young Jun Ko, Matthias 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 Talukdar, William 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 Raykar, Priyanka Agrawal
Sketching the Support of a Probability Measure
Joachim Giesen, Soeren Laue, Lars Kuehne
SMERED: A Bayesian Approach to Graphical Record Linkage and De-duplication
Rebecca Steorts, Rob Hall, Stephen Fienberg
Sparse Bayesian Variable Selection for the Identification of Antigenic Variability in the Foot-and-Mouth Disease Virus
Vinny Davies, Richard Reeve, William Harvey et al.
Sparsity and the Truncated $l^2$-norm
Lee 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 Wilson, Zoubin Ghahramani
The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling
Willie Neiswanger, Frank Wood, Eric Xing
Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees
Jean Honorio, Tommi 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 Hogg, Bernhard Schölkopf
Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection
Jyri Kivinen, Chris Williams, Nicolas Heess