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Bayesian Inference

1724 directly classified papers

Papers per year

Papers

On Thompson Sampling for Smoother-than-Lipschitz Bandits AISTATS 2020
AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC AISTATS 2020
Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory AISTATS 2020
Validated Variational Inference via Practical Posterior Error Bounds AISTATS 2020
DYNOTEARS: Structure Learning from Time-Series Data AISTATS 2020
Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning AISTATS 2020
Simulator Calibration under Covariate Shift with Kernels AISTATS 2020
Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization AISTATS 2020
Fast Rates for General Unbounded Loss Functions: From ERM to Generalized Bayes JMLR 2020
Accelerated Bayesian Optimisation through Weight-Prior Tuning AISTATS 2020
Bayesian Optimisation for Premise Selection in Automated Theorem Proving (Student Abstract) AAAI 2020
Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions AISTATS 2020
Uncertainty in Neural Networks: Approximately Bayesian Ensembling AISTATS 2020
Estimating Model Uncertainty of Neural Networks in Sparse Information Form ICML 2020
Bayesian Model Selection with Graph Structured Sparsity JMLR 2020
A Nonparametric Off-Policy Policy Gradient AISTATS 2020
Fast Bayesian Inference of Sparse Networks with Automatic Sparsity Determination JMLR 2020
Multiplicative Gaussian Particle Filter AISTATS 2020
Knowing The What But Not The Where in Bayesian Optimization ICML 2020
Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring NIPS 2020
Continuous-Time Birth-Death MCMC for Bayesian Regression Tree Models JMLR 2020
Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond NIPS 2020
Being Bayesian about Categorical Probability ICML 2020
Bayesian Adversarial Attack on Graph Neural Networks (Student Abstract) AAAI 2020
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support ICML 2020