Bayesian Inference
A Simple Baseline for Bayesian Uncertainty in Deep Learning
Wesley J. Maddox, Pavel Izmailov, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson
Neural Information Processing Systems http://nips.cc/
Bayesian Extensive-Rank Matrix Factorization with Rotational Invariant Priors
These matrix ensembles are defined precisely in section 2.1, but the reader can keep in
Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon Synapse
Cornelius Schröder, Ben James, Leon Lagnado, Philipp Berens
Neural Information Processing Systems http://nips.cc/
Beyond Invariance: Test-Time Label-Shift Adaptation for Addressing " Spurious " Correlations
"label shift" assumption, where the labels now also include the nuisance factors
Most Probable Explanation Queries in Probabilistic Models
Arya et al. suggest using either supervised or self-supervised learning techniques to train the NN; the former requires access to exact inference
Deep Gamblers: Learning to Abstain with Portfolio Theory
Ziyin Liu, Zhikang Wang, Paul Pu Liang, Russ R. Salakhutdinov, Louis-Philippe Morency, Masahito Ueda
Top-10 rejected images in the MNIST testing set found by two methods.
SyntheticModelCombination: AnInstance-wise ApproachtoUnsupervisedEnsembleLearning
Alternatively,groups may release models that theyhavetrained on their privatedata.