Search Results for author: Anthimos Vardis Kandiros

Found 5 papers, 0 papers with code

Learning Hard-Constrained Models with One Sample

no code implementations6 Nov 2023 Andreas Galanis, Alkis Kalavasis, Anthimos Vardis Kandiros

For general $H$-colorings, we show that standard conditions that guarantee sampling, such as Dobrushin's condition, are insufficient for one-sample learning; on the positive side, we provide a general condition that is sufficient to guarantee linear-time learning and obtain applications for proper colorings and permissive models.

Learning and Testing Latent-Tree Ising Models Efficiently

no code implementations23 Nov 2022 Davin Choo, Yuval Dagan, Constantinos Daskalakis, Anthimos Vardis Kandiros

We provide time- and sample-efficient algorithms for learning and testing latent-tree Ising models, i. e. Ising models that may only be observed at their leaf nodes.

EM's Convergence in Gaussian Latent Tree Models

no code implementations21 Nov 2022 Yuval Dagan, Constantinos Daskalakis, Anthimos Vardis Kandiros

Our results for the landscape of the log-likelihood function in general latent tree models provide support for the extensive practical use of maximum likelihood based-methods in this setting.

Statistical Estimation from Dependent Data

no code implementations20 Jul 2021 Yuval Dagan, Constantinos Daskalakis, Nishanth Dikkala, Surbhi Goel, Anthimos Vardis Kandiros

We consider a general statistical estimation problem wherein binary labels across different observations are not independent conditioned on their feature vectors, but dependent, capturing settings where e. g. these observations are collected on a spatial domain, a temporal domain, or a social network, which induce dependencies.

regression text-classification +1

Learning Ising models from one or multiple samples

no code implementations20 Apr 2020 Yuval Dagan, Constantinos Daskalakis, Nishanth Dikkala, Anthimos Vardis Kandiros

As corollaries of our main theorem, we derive bounds when the model's interaction matrix is a (sparse) linear combination of known matrices, or it belongs to a finite set, or to a high-dimensional manifold.

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