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Found 7 papers, 2 papers with code
Asymptotics of Random Feature Regression Beyond the Linear Scaling Regime
On the other hand, if $p = o(n)$, the number of random features $p$ is the limiting factor and RFRR test error matches the approximation error of the random feature model class (akin to taking $n = \infty$).
Homophily modulates double descent generalization in graph convolution networks
Motivated by experimental observations of ``transductive'' double descent in key networks and datasets, we use analytical tools from statistical physics and random matrix theory to precisely characterize generalization in simple graph convolution networks on the contextual stochastic block model.
Precise Learning Curves and Higher-Order Scaling Limits for Dot Product Kernel Regression
As modern machine learning models continue to advance the computational frontier, it has become increasingly important to develop precise estimates for expected performance improvements under different model and data scaling regimes.
Sharp Asymptotics of Kernel Ridge Regression Beyond the Linear Regime
The generalization performance of kernel ridge regression (KRR) exhibits a multi-phased pattern that crucially depends on the scaling relationship between the sample size $n$ and the underlying dimension $d$.
Joint COCO and Mapillary Workshop at ICCV 2019 Keypoint Detection Challenge Track Technical Report: Distribution-Aware Coordinate Representation for Human Pose Estimation
Taking them together, we formulate a novel Distribution-Aware coordinate Representation for Keypoint (DARK) method.
SLOPE for Sparse Linear Regression:Asymptotics and Optimal Regularization
In sparse linear regression, the SLOPE estimator generalizes LASSO by penalizing different coordinates of the estimate according to their magnitudes.
Information Theory Information Theory Statistics Theory Statistics Theory
A Solvable High-Dimensional Model of GAN
We present a theoretical analysis of the training process for a single-layer GAN fed by high-dimensional input data.
