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Found 13 papers, 6 papers with code
Federated Learning for Non-factorizable Models using Deep Generative Prior Approximations
We demonstrate the SIGMA prior's effectiveness on synthetic data and showcase its utility in a real-world example of FL for spatial data, using a conditional autoregressive prior to model spatial dependence across Australia.
Beyond the noise: intrinsic dimension estimation with optimal neighbourhood identification
The Intrinsic Dimension (ID) is a key concept in unsupervised learning and feature selection, as it is a lower bound to the number of variables which are necessary to describe a system.
Scalable Vertical Federated Learning via Data Augmentation and Amortized Inference
Vertical federated learning (VFL) has emerged as a paradigm for collaborative model estimation across multiple clients, each holding a distinct set of covariates.
On the intrinsic dimensionality of Covid-19 data: a global perspective
This paper aims to develop a global perspective of the complexity of the relationship between the standardised per-capita growth rate of Covid-19 cases, deaths, and the OxCGRT Covid-19 Stringency Index, a measure describing a country's stringency of lockdown policies.
Learning Summary Statistics for Bayesian Inference with Autoencoders
For stochastic models with intractable likelihood functions, approximate Bayesian computation offers a way of approximating the true posterior through repeated comparisons of observations with simulated model outputs in terms of a small set of summary statistics.
Revealing three-dimensional quantum criticality by Sr-substitution in Han Purple
Classical and quantum phase transitions (QPTs), with their accompanying concepts of criticality and universality, are a cornerstone of statistical thermodynamics.
Strongly Correlated Electrons
Personalized pathology test for Cardio-vascular disease: Approximate Bayesian computation with discriminative summary statistics learning
Cardio/cerebrovascular diseases (CVD) have become one of the major health issue in our societies.
A Common Atom Model for the Bayesian Nonparametric Analysis of Nested Data
The use of high-dimensional data for targeted therapeutic interventions requires new ways to characterize the heterogeneity observed across subgroups of a specific population.
Methodology Applications
Data segmentation based on the local intrinsic dimension
Here we develop a robust approach to discriminate regions with different local IDs and segment the points accordingly.
Regularized Zero-Variance Control Variates
Zero-variance control variates (ZV-CV) are a post-processing method to reduce the variance of Monte Carlo estimators of expectations using the derivatives of the log target.
Computation Methodology
Fast Maximum Likelihood estimation via Equilibrium Expectation for Large Network Data
We demonstrate the performance of the EE algorithm in the context of exponential random graphmodels (ERGMs) a family of statistical models commonly used in empirical research based on network data observed at a single period in time.
ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation
Further, ABCpy enables ABC experts to easily develop new inference schemes and evaluate them in a standardized environment and to extend the library with new algorithms.
Computation
Bayesian Inference of Spreading Processes on Networks
Although the underlying processes of transmission are different, the network approach can be used to study the spread of pathogens in a contact network or the spread of rumors in an online social network.
