Bayesian Inference
Robust Learning of Fixed-Structure Bayesian Networks
Yu Cheng, Ilias Diakonikolas, Daniel Kane, Alistair Stewart
In this work, we study the fully observable discrete case where the structure of the network is given.
Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior
Zi Wang, Beomjoon Kim, Leslie Pack Kaelbling
Bayesian optimization usually assumes that a Bayesian prior is given.
Scaling the Poisson GLM to massive neural datasets through polynomial approximations
David Zoltowski, Jonathan W. Pillow
Such large-scale recordings pose a major challenge to existing statistical methods for neural data analysis. Here we develop highly scalable approximate inference methods for Poisson generalized linear models (GLMs) that require only a single pass over the data.
Predictive Uncertainty Estimation via Prior Networks
Estimating uncertainty in a model's predictions is important, as
Semi-crowdsourced Clustering with Deep Generative Models
The two parts share the latent variables.
Graphical model inference: Sequential Monte Carlo meets deterministic approximations
Fredrik Lindsten, Jouni Helske, Matti Vihola
The latter enjoy asymptotic consistency, but can suffer from high computational costs. In this paper we present a way of bridging the gap between deterministic and stochastic inference.
Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations
Neural Information Processing Systems http://nips.cc/
Out-of-Distribution Detection using Multiple Semantic Label Representations
Gabi Shalev, Yossi Adi, Joseph Keshet
Neural Information Processing Systems http://nips.cc/
Asymptotic optimality of adaptive importance sampling
François Portier, Bernard Delyon
AIS is asymptotically optimal, i.e., learning the sampling policy has no cost asymptotically.
BRUNO: A Deep Recurrent Model for Exchangeable Data
Iryna Korshunova, Jonas Degrave, Ferenc Huszar, Yarin Gal, Arthur Gretton, Joni Dambre
We present a novel model architecture which leverages deep learning tools to perform exact Bayesian inference on sets of high dimensional, complex observations.