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Found 7 papers, 5 papers with code
Momentum Particle Maximum Likelihood
Maximum likelihood estimation (MLE) of latent variable models is often recast as an optimization problem over the extended space of parameters and probability distributions.
Energy Discrepancies: A Score-Independent Loss for Energy-Based Models
Energy-based models are a simple yet powerful class of probabilistic models, but their widespread adoption has been limited by the computational burden of training them.
Particle algorithms for maximum likelihood training of latent variable models
(Neal and Hinton, 1998) recast maximum likelihood estimation of any given latent variable model as the minimization of a free energy functional $F$, and the EM algorithm as coordinate descent applied to $F$.
Faking feature importance: A cautionary tale on the use of differentially-private synthetic data
Synthetic datasets are often presented as a silver-bullet solution to the problem of privacy-preserving data publishing.
Energy-Based Models for Functional Data using Path Measure Tilting
Their ability to incorporate domain-specific choices and constraints into the structure of the model through composition make EBMs an appealing candidate for applications in physics, biology and computer vision and various other fields.
Kernel Stein Tests for Multiple Model Comparison
The first test, building on the post selection inference framework, provably controls the number of best models that are wrongly declared worse (false positive rate).
More Powerful Selective Kernel Tests for Feature Selection
An approach for addressing this is via conditioning on the selection procedure to account for how we have used the data to generate our hypotheses, and prevent information to be used again after selection.
