conftrace_
← Optimization & Theory
Machine Learning › Optimization & Theory ›

Bayesian Inference

5,566 papers

Papers per year

11
100
176
522
279
'05
'10
'15
'20
'25

Papers

Uncertainty-Driven Loss for Single Image Super-Resolution NIPS 2021
Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent NIPS 2021
Information Directed Sampling for Sparse Linear Bandits NIPS 2021
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels NIPS 2021
Risk-averse Heteroscedastic Bayesian Optimization NIPS 2021
Privately Publishable Per-instance Privacy NIPS 2021
De-randomizing MCMC dynamics with the diffusion Stein operator NIPS 2021
Deconditional Downscaling with Gaussian Processes NIPS 2021
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification NIPS 2021
Conformal Bayesian Computation NIPS 2021
Online Variational Filtering and Parameter Learning NIPS 2021
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence NIPS 2021
Bayesian Optimization with High-Dimensional Outputs NIPS 2021
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise NIPS 2021
Model Selection for Bayesian Autoencoders NIPS 2021
Knowledge-Adaptation Priors NIPS 2021
Time-independent Generalization Bounds for SGLD in Non-convex Settings NIPS 2021
Uncertainty Quantification and Deep Ensembles NIPS 2021
Laplace Redux - Effortless Bayesian Deep Learning NIPS 2021
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs NIPS 2021
Nested Variational Inference NIPS 2021
Local policy search with Bayesian optimization NIPS 2021
Precise characterization of the prior predictive distribution of deep ReLU networks NIPS 2021
Optimal Order Simple Regret for Gaussian Process Bandits NIPS 2021
A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs NIPS 2021