3 papers
cs.LG2025
A Classical View on Benign Overfitting: The Role of Sample Size
Junhyung Park, Patrick Bloebaum, Shiva Prasad Kasiviswanathan
Benign overfitting is a phenomenon in machine learning where a model perfectly fits (interpolates) the training data, including noisy examples, yet still generalizes well to unseen…
stat.ML2025
Score matching through the roof: linear, nonlinear, and latent variables causal discovery
Francesco Montagna, Philipp M. Faller, Patrick Bloebaum +2
Causal discovery from observational data holds great promise, but existing methods rely on strong assumptions about the underlying causal structure, often requiring full observabil…
cs.LG2024
Benign Overfitting for Regression with Trained Two-Layer ReLU Networks
Junhyung Park, Patrick Bloebaum, Shiva Prasad Kasiviswanathan
We study the least-square regression problem with a two-layer fully-connected neural network, with ReLU activation function, trained by gradient flow. Our first result is a general…