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bayesian posterior

22 papers

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Co-occurring keywords

bayesian inference (1907) markov chain monte carlo (725) variational inference (2069) predictive distribution (39) graph neural network (3962) posterior sampling (152) uncertainty quantification (1393) label propagation (185) semi-supervised learning (2341) stochastic gradient (296)

Papers

Nonparametric Regression on Random Geometric Graphs Sampled from Submanifolds JMLR 2025
Few-Shot Open-Set Classification via Reasoning-Aware Decomposition EMNLP 2025
How good is your Laplace approximation of the Bayesian posterior? Finite-sample computable error bounds for a variety of useful divergences JMLR 2025
Posterior Uncertainty Quantification in Neural Networks using Data Augmentation AISTATS 2024
Bias Detection via Signaling NIPS 2024
On the Computational Complexity of Metropolis-Adjusted Langevin Algorithms for Bayesian Posterior Sampling JMLR 2024
Bayesian Design Principles for Frequentist Sequential Learning ICML 2023
Bayesian Posterior Approximation With Stochastic Ensembles CVPR 2023
PACm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime AISTATS 2022
Forward-Backward Latent State Inference for Hidden Continuous-Time semi-Markov Chains NIPS 2022
Is SGD a Bayesian sampler? Well, almost JMLR 2021
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery NIPS 2021
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification NIPS 2021
Asymptotic Consistency of $\alpha$-{R}ényi-Approximate Posteriors JMLR 2020
Aggregated Gradient Langevin Dynamics AAAI 2020
Neural Clustering Processes ICML 2020
Old Techniques in Differentially Private Linear Regression ALT 2019
A Flexible Generative Framework for Graph-based Semi-supervised Learning NIPS 2019
On Connecting Stochastic Gradient MCMC and Differential Privacy AISTATS 2019
Kernel Bayesian Inference with Posterior Regularization NIPS 2016
Stochastic Gradient Geodesic MCMC Methods NIPS 2016
Streaming Variational Bayes NIPS 2013