Seven papers at NIPS 2014

Published on December 1, 2014

The MLRG has the following seven papers (one oral, two spotlights and four posters) appearing at NIPS 2014:

[1] Automated Variational Inference for Gaussian Process Models (V.T. Nguyen and E. Bonilla)
[2] Extended and Unscented Gaussian Processes (D. Steinberg and E. Bonilla)
[3] Convex Deep Learning via Normalized Kernels (O Aslan, X. Zhang and D. Schuurmans)
[4] Robust Bayesian Max-Margin Clustering (C. Chen, J. Zhu and X. Zhang)
[5] From Stochastic Mixability to Fast Rates (N. Mehta and R.C. Williamson)
[6] (Almost) No Label No Cry (G. Patrini, R. Nock, P Rivera and T. Caetano)
[7] Projecting General Markov Random Fields for Fast Mixing (X. Liu and J. Domke)