4 citations · 8 across the 5 of their papers we have counts for
Showing stat.MLShow all
3 papers · 1 filter
stat.ML2025
On Reconstructing Training Data From Bayesian Posteriors and Trained Models
George Wynne
Publicly releasing the specification of a model with its trained parameters means an adversary can attempt to reconstruct information about the training data via training data reco…
stat.ML2022★ 4 cited
Grassmann Stein Variational Gradient Descent
Xing Liu, Harrison Zhu, Jean-François Ton +2
Stein variational gradient descent (SVGD) is a deterministic particle inference algorithm that provides an efficient alternative to Markov chain Monte Carlo. However, SVGD has been…
stat.ML2021★ 1 cited
Variational Gaussian Processes: A Functional Analysis View
Veit Wild, George Wynne
Variational Gaussian process (GP) approximations have become a standard tool in fast GP inference. This technique requires a user to select variational features to increase efficie…