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Found 9 papers, 1 papers with code
DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation
The Shapley value has recently been proposed as a principled tool to achieve this goal due to formal axiomatic justification.
Personalised Federated Learning On Heterogeneous Feature Spaces
Most personalised federated learning (FL) approaches assume that raw data of all clients are defined in a common subspace i. e. all clients store their data according to the same schema.
FedPop: A Bayesian Approach for Personalised Federated Learning
We provide non-asymptotic convergence guarantees for the proposed algorithms and illustrate their performances on various personalised federated learning tasks.
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs
Performing reliable Bayesian inference on a big data scale is becoming a keystone in the modern era of machine learning.
QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning
The objective of Federated Learning (FL) is to perform statistical inference for data which are decentralised and stored locally on networked clients.
High-dimensional Gaussian sampling: a review and a unifying approach based on a stochastic proximal point algorithm
In addition to this state of the art, this paper proposes a unifying Gaussian simulation framework by deriving a stochastic counterpart of the celebrated proximal point algorithm in optimization.
Computation
Quantitative inference of the $H_2$ column densities from 3 mm molecular emission: A case study towards Orion B
We aim to use multi-molecule line emission to infer NH2 from radio observations.
Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting
Performing exact Bayesian inference for complex models is computationally intractable.
Asymptotically exact data augmentation: models, properties and algorithms
In a broader perspective, this paper shows that AXDA models can benefit from interesting statistical properties and yield efficient inference algorithms.
