{"total":28,"rows":[{"id":"2309.16314","title":"A Primer on Bayesian Neural Networks: Review and Debates","created":"2023-09-28","cited_by_count":27,"area":null},{"id":"2106.00797","title":"QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning","created":"2021-06-01","cited_by_count":26,"area":null},{"id":"2107.11820","title":"A Survey of Monte Carlo Methods for Parameter Estimation","created":"2021-07-25","cited_by_count":23,"area":null},{"id":"2112.05120","title":"On Convergence of Federated Averaging Langevin Dynamics","created":"2021-12-09","cited_by_count":14,"area":null},{"id":"2106.06300","title":"DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs","created":"2021-06-11","cited_by_count":11,"area":null},{"id":"2202.11154","title":"Parallel MCMC Without Embarrassing Failures","created":"2022-02-22","cited_by_count":11,"area":null},{"id":"2312.09817","title":"Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space","created":"2023-12-15","cited_by_count":10,"area":null},{"id":"2402.14434","title":"Parallelized Midpoint Randomization for Langevin Monte Carlo","created":"2024-02-22","cited_by_count":9,"area":null},{"id":"2208.04080","title":"SwISS: A Scalable Markov chain Monte Carlo Divide-and-Conquer Strategy","created":"2022-08-08","cited_by_count":6,"area":null},{"id":"2503.06962","title":"Capture Global Feature Statistics for One-Shot Federated Learning","created":"2025-03-10","cited_by_count":6,"area":null},{"id":"2403.07282","title":"Enhancing Transfer Learning with Flexible Nonparametric Posterior Sampling","created":"2024-03-12","cited_by_count":5,"area":null},{"id":"2302.03314","title":"Federated Variational Inference Methods for Structured Latent Variable Models","created":"2023-02-07","cited_by_count":4,"area":null},{"id":"2304.11251","title":"Machine Learning and the Future of Bayesian Computation","created":"2023-04-21","cited_by_count":4,"area":null},{"id":"2105.02579","title":"MCMC-driven importance samplers","created":"2021-05-06","cited_by_count":3,"area":null},{"id":"2110.07265","title":"Divide-and-Conquer Fusion","created":"2021-10-14","cited_by_count":3,"area":null},{"id":"2406.03288","title":"Embarrassingly Parallel GFlowNets","created":"2024-06-05","cited_by_count":3,"area":null},{"id":"2211.00100","title":"Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms","created":"2022-10-31","cited_by_count":2,"area":null},{"id":"2406.02177","title":"One-Shot Federated Learning with Bayesian Pseudocoresets","created":"2024-06-04","cited_by_count":1,"area":null},{"id":"2412.17008","title":"Data value estimation on private gradients","created":"2024-12-22","cited_by_count":1,"area":null},{"id":"2501.01148","title":"Adaptive posterior distributions for uncertainty analysis of covariance matrices in Bayesian inversion problems for multioutput signals","created":"2025-01-02","cited_by_count":1,"area":null},{"id":"2510.20225","title":"Federated Learning via Meta-Variational Dropout","created":"2025-10-23","cited_by_count":1,"area":null},{"id":"2603.27864","title":"Vertical Consensus Inference for High-Dimensional Random Partition","created":"2026-03-29","cited_by_count":1,"area":null},{"id":"2203.14088","title":"Distributed data analytics","created":"2022-03-26","cited_by_count":0,"area":null},{"id":"2206.09526","title":"Robust One Round Federated Learning with Predictive Space Bayesian Inference","created":"2022-06-20","cited_by_count":0,"area":null},{"id":"2401.15838","title":"Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of Multipliers","created":"2024-01-29","cited_by_count":0,"area":null},{"id":"2504.05007","title":"Measuring the right thing: justifying metrics in AI impact assessments","created":"2025-04-07","cited_by_count":0,"area":null},{"id":"2606.17995","title":"Differential Privacy of Gaussian Process Posterior Sampling","created":"2026-06-16","cited_by_count":0,"area":null},{"id":"2608.11275","title":"Every pooling rule has its world: matching probability combination rules to situations and stakes","created":"2026-08-11","cited_by_count":0,"area":null}],"next_cursor":null}