Tag: structure
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The Impact of Anisotropic Covariance Structure on the Training Dynamics and Generalization Error of Linear Networks
The Impact of Anisotropic Covariance Structure on the Training Dynamics and Generalization Error of Linear Networks arXiv:2601.06961v1 Announce Type: new Abstract: The success of deep neural networks largely depends on the statistical structure of the training data. While learning dynamics and generalization on isotropic data are well-established, the impact of pronounced anisotropy on these crucial…
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Sharp Structure-Agnostic Lower Bounds for General Functional Estimation
Sharp Structure-Agnostic Lower Bounds for General Functional Estimation arXiv:2512.17341v1 Announce Type: new Abstract: The design of efficient nonparametric estimators has long been a central problem in statistics, machine learning, and decision making. Classical optimal procedures often rely on strong structural assumptions, which can be misspecified in practice and complicate deployment. This limitation has sparked growing…
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How to Spin Up a Project Structure with Cookiecutter
How to Spin Up a Project Structure with Cookiecutter If you’re anything like me, “procrastination” might as well be your middle name. There’s always that nagging hesitation before starting a new project. Just thinking about setting up the project structure, creating documentation, or writing a decent README is enough to trigger yawns. It feels like…
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Testing for correlation between network structure and high-dimensional node covariates
Testing for correlation between network structure and high-dimensional node covariates arXiv:2509.03772v1 Announce Type: new Abstract: In many application domains, networks are observed with node-level features. In such settings, a common problem is to assess whether or not nodal covariates are correlated with the network structure itself. Here, we present four novel methods for addressing this…
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It’s Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation
It’s Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation arXiv:2507.02275v1 Announce Type: new Abstract: Structure-agnostic causal inference studies how well one can estimate a treatment effect given black-box machine learning estimates of nuisance functions (like the impact of confounders on treatment and outcomes). Here, we find that the answer depends in a…
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Dynamic Causal Structure Discovery and Causal Effect Estimation
Dynamic Causal Structure Discovery and Causal Effect Estimation arXiv:2501.06534v1 Announce Type: new Abstract: To represent the causal relationships between variables, a directed acyclic graph (DAG) is widely utilized in many areas, such as social sciences, epidemics, and genetics. Many causal structure learning approaches are developed to learn the hidden causal structure utilizing deep-learning approaches. However,…