Statistical Learning
High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality
Zhaoran Wang, Quanquan Gu, Yang Ning, Han Liu
Neural Information Processing Systems http://nips.cc/
Local Expectation Gradients for Black Box Variational Inference
Michalis Titsias RC AUEB, Miguel Lázaro-Gredilla
Neural Information Processing Systems http://nips.cc/
Efficient and Robust Automated Machine Learning
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, Frank Hutter
Recent work has started to tackle this automated machine learning (AutoML) problem with the help of efficient Bayesian optimization methods.
Universal Boosting Variational Inference
Neural Information Processing Systems http://nips.cc/
Differentially Private Markov Chain Monte Carlo
Mikko Heikkilä, Joonas Jälkö, Onur Dikmen, Antti Honkela
Neural Information Processing Systems http://nips.cc/
Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis
Jimei Yang, Scott E. Reed, Ming-Hsuan Yang, Honglak Lee
Neural Information Processing Systems http://nips.cc/
Bayesian Deep Ensembles via the Neural Tangent Kernel
Previous work has shown that even in the infinite width limit, when NNs become GPs, there is no GP posterior interpretation to a deep ensemble trained with squared error loss.
Meta-Learning with Implicit Gradients
Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, Sergey Levine
Neural Information Processing Systems http://nips.cc/
Reliable training and estimation of variance networks
Nicki Skafte, Martin Jørgensen, Søren Hauberg
We propose and investigate new complementary methodologies for estimating predictive variance networks in regression neural networks.