Saved in:
| Main Authors: | Liang, Xinzhu, Lukens, Joseph M., Lohani, Sanjaya, Kirby, Brian T., Searles, Thomas A., Qiu, Xin, Law, Kody J. H. |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.06173 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Scalable Bayesian Monte Carlo: fast uncertainty estimation beyond deep ensembles
by: Liang, Xinzhu, et al.
Published: (2025)
by: Liang, Xinzhu, et al.
Published: (2025)
Unorthodox parallelization for Bayesian quantum state estimation
by: Nguyen, Hanson H., et al.
Published: (2025)
by: Nguyen, Hanson H., et al.
Published: (2025)
Mixtures of Gaussian Process Experts with SMC$^2$
by: Härkönen, Teemu, et al.
Published: (2022)
by: Härkönen, Teemu, et al.
Published: (2022)
Bayesian Deep Learning with Multilevel Trace-class Neural Networks
by: Chada, Neil K., et al.
Published: (2022)
by: Chada, Neil K., et al.
Published: (2022)
Exponential Concentration in Stochastic Approximation
by: Law, Kody, et al.
Published: (2022)
by: Law, Kody, et al.
Published: (2022)
Fast Deep Mixtures of Gaussian Process Experts
by: Etienam, Clement, et al.
Published: (2020)
by: Etienam, Clement, et al.
Published: (2020)
Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need
by: Yang, Shangda, et al.
Published: (2024)
by: Yang, Shangda, et al.
Published: (2024)
Improving sample efficiency of high dimensional Bayesian optimization with MCMC
by: Yi, Zeji, et al.
Published: (2024)
by: Yi, Zeji, et al.
Published: (2024)
MCMC-driven learning
by: Bouchard-Côté, Alexandre, et al.
Published: (2024)
by: Bouchard-Côté, Alexandre, et al.
Published: (2024)
Probabilistic Numeric SMC Sampling for Bayesian Nonlinear System Identification in Continuous Time
by: Longbottom, Joe D., et al.
Published: (2024)
by: Longbottom, Joe D., et al.
Published: (2024)
Functional Stochastic Gradient MCMC for Bayesian Neural Networks
by: Wu, Mengjing, et al.
Published: (2024)
by: Wu, Mengjing, et al.
Published: (2024)
Function-Space MCMC for Bayesian Wide Neural Networks
by: Pezzetti, Lucia, et al.
Published: (2024)
by: Pezzetti, Lucia, et al.
Published: (2024)
Accelerated training of deep learning surrogate models for surface displacement and flow, with application to MCMC-based history matching of CO2 storage operations
by: Han, Yifu, et al.
Published: (2024)
by: Han, Yifu, et al.
Published: (2024)
Generalized Bayesian deep reinforcement learning
by: Roy, Shreya Sinha, et al.
Published: (2024)
by: Roy, Shreya Sinha, et al.
Published: (2024)
MCMC for Bayesian estimation of Differential Privacy from Membership Inference Attacks
by: Yildirim, Ceren, et al.
Published: (2025)
by: Yildirim, Ceren, et al.
Published: (2025)
Comparison of deep learning and conventional methods for disease onset prediction
by: John, Luis H., et al.
Published: (2024)
by: John, Luis H., et al.
Published: (2024)
Compact Bayesian Neural Networks via pruned MCMC sampling
by: Deo, Ratneel, et al.
Published: (2025)
by: Deo, Ratneel, et al.
Published: (2025)
Distributed MCMC inference for Bayesian Non-Parametric Latent Block Model
by: Khoufache, Reda, et al.
Published: (2024)
by: Khoufache, Reda, et al.
Published: (2024)
Multi-Fidelity Delayed Acceptance: hierarchical MCMC sampling for Bayesian inverse problems combining multiple solvers through deep neural networks
by: Zacchei, Filippo, et al.
Published: (2025)
by: Zacchei, Filippo, et al.
Published: (2025)
Derivative-informed neural operator acceleration of geometric MCMC for infinite-dimensional Bayesian inverse problems
by: Cao, Lianghao, et al.
Published: (2024)
by: Cao, Lianghao, et al.
Published: (2024)
Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data
by: Fang, Shikai, et al.
Published: (2023)
by: Fang, Shikai, et al.
Published: (2023)
Reinforcement Learning for Adaptive MCMC
by: Wang, Congye, et al.
Published: (2024)
by: Wang, Congye, et al.
Published: (2024)
Bayesian neural networks via MCMC: a Python-based tutorial
by: Chandra, Rohitash, et al.
Published: (2023)
by: Chandra, Rohitash, et al.
Published: (2023)
On the connection between least squares, regularization, and classical shadows
by: Zhu, Zhihui, et al.
Published: (2023)
by: Zhu, Zhihui, et al.
Published: (2023)
Misclassification bounds for PAC-Bayesian sparse deep learning
by: Mai, The Tien
Published: (2024)
by: Mai, The Tien
Published: (2024)
On Cyclical MCMC Sampling
by: Wang, Liwei, et al.
Published: (2024)
by: Wang, Liwei, et al.
Published: (2024)
Evaluating Bayesian deep learning for radio galaxy classification
by: Mohan, Devina, et al.
Published: (2024)
by: Mohan, Devina, et al.
Published: (2024)
Bayesian deep learning for cosmic volumes with modified gravity
by: García-Farieta, Jorge Enrique, et al.
Published: (2023)
by: García-Farieta, Jorge Enrique, et al.
Published: (2023)
Scalable Bayesian Inference for Generalized Linear Mixed Models via Stochastic Gradient MCMC
by: Berchuck, Samuel I., et al.
Published: (2024)
by: Berchuck, Samuel I., et al.
Published: (2024)
Bayesian Comparisons Between Representations
by: Schütt, Heiko H.
Published: (2024)
by: Schütt, Heiko H.
Published: (2024)
Explaining deep learning for ECG using time-localized clusters
by: Boubekki, Ahcène, et al.
Published: (2025)
by: Boubekki, Ahcène, et al.
Published: (2025)
Gaussian Process-based learning with new MCMC-based implementation of Wishart prior on correlation matrix
by: Warrior, Kane, et al.
Published: (2026)
by: Warrior, Kane, et al.
Published: (2026)
Learning with Local Search MCMC Layers
by: Vivier-Ardisson, Germain, et al.
Published: (2025)
by: Vivier-Ardisson, Germain, et al.
Published: (2025)
Course Project Report: Comparing MCMC and Variational Inference for Bayesian Probabilistic Matrix Factorization on the MovieLens Dataset
by: Xu, Ruixuan, et al.
Published: (2025)
by: Xu, Ruixuan, et al.
Published: (2025)
Harnessing the Power of Reinforcement Learning for Adaptive MCMC
by: Wang, Congye, et al.
Published: (2025)
by: Wang, Congye, et al.
Published: (2025)
Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties
by: Riccius, Leon, et al.
Published: (2024)
by: Riccius, Leon, et al.
Published: (2024)
Defending Quantum Classifiers against Adversarial Perturbations through Quantum Autoencoders
by: Andrews, Emma, et al.
Published: (2026)
by: Andrews, Emma, et al.
Published: (2026)
Adaptive Independent Sticky MCMC algorithms
by: Martino, L., et al.
Published: (2013)
by: Martino, L., et al.
Published: (2013)
MCMC with Adaptive Principal-Component Transformation: Rotation-Invariant Universal Samplers for Bayesian Structural System Identification
by: Meng, Xianghao, et al.
Published: (2026)
by: Meng, Xianghao, et al.
Published: (2026)
Towards stable training of parallel continual learning
by: Yuepan, Li, et al.
Published: (2024)
by: Yuepan, Li, et al.
Published: (2024)
Similar Items
-
Scalable Bayesian Monte Carlo: fast uncertainty estimation beyond deep ensembles
by: Liang, Xinzhu, et al.
Published: (2025) -
Unorthodox parallelization for Bayesian quantum state estimation
by: Nguyen, Hanson H., et al.
Published: (2025) -
Mixtures of Gaussian Process Experts with SMC$^2$
by: Härkönen, Teemu, et al.
Published: (2022) -
Bayesian Deep Learning with Multilevel Trace-class Neural Networks
by: Chada, Neil K., et al.
Published: (2022) -
Exponential Concentration in Stochastic Approximation
by: Law, Kody, et al.
Published: (2022)