← Optimization & Theory
Machine Learning › Optimization & Theory ›

Bayesian Inference

4821 directly classified papers

Papers per year

Papers

Small-Variance Asymptotics for Nonparametric Bayesian Overlapping Stochastic Blockmodels IJCAI 2018
Differentially Private Bayesian Inference for Exponential Families NIPS 2018
Random Feature Stein Discrepancies NIPS 2018
A Stein variational Newton method NIPS 2018
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning NIPS 2018
Bayesian Trees for Automated Cytometry Data Analysis MLHC 2018
Variational Sequential Labelers for Semi-Supervised Learning EMNLP 2018
Neural Architecture Search with Bayesian Optimisation and Optimal Transport NIPS 2018
Using Large Ensembles of Control Variates for Variational Inference NIPS 2018
Clustering Induced Kernel Learning ACML 2018
Distinguishing Question Subjectivity from Difficulty for Improved Crowdsourcing ACML 2018
Stochastic Variational Inference With Gradient Linearization CVPR 2018
Bandit Learning with Implicit Feedback NIPS 2018
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient NIPS 2018
Orthogonally Decoupled Variational Gaussian Processes NIPS 2018
Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo NIPS 2018
Nonparametric learning from Bayesian models with randomized objective functions NIPS 2018
Reparameterization Gradient for Non-differentiable Models NIPS 2018
Information Constraints on Auto-Encoding Variational Bayes NIPS 2018
Implicit Reparameterization Gradients NIPS 2018
Discretely Relaxing Continuous Variables for tractable Variational Inference NIPS 2018
Boosting Black Box Variational Inference NIPS 2018
Coupled Variational Bayes via Optimization Embedding NIPS 2018
Improving Explorability in Variational Inference with Annealed Variational Objectives NIPS 2018
Posterior Concentration for Sparse Deep Learning NIPS 2018