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Found 5 papers, 2 papers with code
NetGAN without GAN: From Random Walks to Low-Rank Approximations
In this paper, we investigate the implicit bias of NetGAN.
A Consistent Estimator for Confounding Strength
Regression on observational data can fail to capture a causal relationship in the presence of unobserved confounding.
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference
By reframing the problem in terms of incompatible conditional distributions we arrive at a natural solution: the Gibbs prior.
Interpolation and Regularization for Causal Learning
If the confounding strength is negative, causal learning requires weaker regularization than statistical learning, interpolators can be optimal, and the optimal regularization can even be negative.
Clustering with Tangles: Algorithmic Framework and Theoretical Guarantees
Given a collection of cuts of any dataset, tangles aggregate these cuts to point in the direction of a dense structure.
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