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Found 9 papers, 2 papers with code
A PAC-Bayesian Perspective on the Interpolating Information Criterion
Deep learning is renowned for its theory-practice gap, whereby principled theory typically fails to provide much beneficial guidance for implementation in practice.
Deep Generative Models, Synthetic Tabular Data, and Differential Privacy: An Overview and Synthesis
This article provides a comprehensive synthesis of the recent developments in synthetic data generation via deep generative models, focusing on tabular datasets.
The Interpolating Information Criterion for Overparameterized Models
The problem of model selection is considered for the setting of interpolating estimators, where the number of model parameters exceeds the size of the dataset.
Graph Neural Network-Based Anomaly Detection for River Network Systems
We further examine the strengths and weaknesses of this baseline approach, GDN, in comparison to other benchmarking methods on complex real-world river network data.
Federated Variational Inference Methods for Structured Latent Variable Models
Federated learning methods enable model training across distributed data sources without data leaving their original locations and have gained increasing interest in various fields.
Transport Reversible Jump Proposals
Reversible jump Markov chain Monte Carlo (RJMCMC) proposals that achieve reasonable acceptance rates and mixing are notoriously difficult to design in most applications.
Continuously-Tempered PDMP Samplers
We show how PDMPs, and particularly the Zig-Zag sampler, can be implemented to sample from such an extended distribution.
The reproducing Stein kernel approach for post-hoc corrected sampling
Stein importance sampling is a widely applicable technique based on kernelized Stein discrepancy, which corrects the output of approximate sampling algorithms by reweighting the empirical distribution of the samples.
Implicit Langevin Algorithms for Sampling From Log-concave Densities
Theoretical and algorithmic properties of the resulting sampling methods for $ \theta \in [0, 1] $ and a range of step sizes are established.
