2 citations · 3 across the 6 of their papers we have counts for
9 papers
Learning Non-Gaussian SEMs with Conditional Risk, and Why the Direction of Recursion Matters
Ujas Shah, Panagiota Birmpa, Rebecca Morrison
Variance-based methods recover a Gaussian linear structural equation model (SEM) with equal or suitably constrained error variances, either by recursively identifying the source no…
Graph-Informed Adversarial Modeling: Infimal Subadditivity of Interpolative Divergences
Panagiota Birmpa, Eric Joseph Hall
We study adversarial learning when the target distribution factorizes according to a known Bayesian network. For interpolative divergences, including -divergences, we prove…
Space-time bounds for the stirring process with births and deaths
Panagiota Birmpa, Patrícia Gonçalves, Dimitrios Tsagkarogiannis
We consider a one-dimensional symmetric simple exclusion process on the segment , coupled to boundary dynamics that inject particles from the right reservoir and r…
Proximal optimal transport divergences
Ricardo Baptista, Panagiota Birmpa, Markos A. Katsoulakis +2
We introduce the proximal optimal transport divergence, a novel discrepancy measure that interpolates between information divergences and optimal transport distances via an infimal…
Lipschitz-regularized gradient flows and generative particle algorithms for high-dimensional scarce data
Hyemin Gu, Panagiota Birmpa, Yannis Pantazis +2
We build a new class of generative algorithms capable of efficiently learning an arbitrary target distribution from possibly scarce, high-dimensional data and subsequently generate…
Model Uncertainty and Correctability for Directed Graphical Models
Panagiota Birmpa, Jinchao Feng, Markos A. Katsoulakis +1
Probabilistic graphical models are a fundamental tool in probabilistic modeling, machine learning and artificial intelligence. They allow us to integrate in a natural way expert kn…
Uncertainty quantification for Markov Random Fields
Panagiota Birmpa, Markos A. Katsoulakis
We present an information-based uncertainty quantification method for general Markov Random Fields. Markov Random Fields (MRF) are structured, probabilistic graphical models over u…
Action minimization and macroscopic interface motion under forced displacement
P. Birmpa, D. Tsagkarogiannis
We study an one dimensional model where an interface is the stationary solution of a mesoscopic non local evolution equation which has been derived by a microscopic stochastic spin…
Large deviations for the macroscopic motion of an interface
P. Birmpa, N. Dirr, D. Tsagkarogiannis
We study the most probable way an interface moves on a macroscopic scale from an initial to a final position within a fixed time in the context of large deviations for a stochastic…