Tag: overfitting

Overfitting vs. Underfitting: Making Sense of the Bias-Variance Trade-Off

Overfitting vs. Underfitting: Making Sense of the Bias-Variance Trade-Off The best models live in the sweet spot: generalizing well, learning enough, but not too much The post Overfitting vs. Underfitting: Making Sense of the Bias-Variance Trade-Off appeared first on Towards Data Science. Frida Karvouni Go to original source

November 23, 2025
MMbeddings: Parameter-Efficient, Low-Overfitting Probabilistic Embeddings Inspired by Nonlinear Mixed Models

MMbeddings: Parameter-Efficient, Low-Overfitting Probabilistic Embeddings Inspired by Nonlinear Mixed Models arXiv:2510.22198v1 Announce Type: new Abstract: We present MMbeddings, a probabilistic embedding approach that reinterprets categorical embeddings through the lens of nonlinear mixed models, effectively bridging classical statistical theory with modern deep learning. By treating embeddings as latent random effects within a variational autoencoder framework, our…

October 28, 2025
Risk Phase Transitions in Spiked Regression: Alignment Driven Benign and Catastrophic Overfitting

Risk Phase Transitions in Spiked Regression: Alignment Driven Benign and Catastrophic Overfitting arXiv:2510.01414v1 Announce Type: new Abstract: This paper analyzes the generalization error of minimum-norm interpolating solutions in linear regression using spiked covariance data models. The paper characterizes how varying spike strengths and target-spike alignments can affect risk, especially in overparameterized settings. The study presents…

October 3, 2025