A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
LoD improves model safety by integrating unlabeled wild data, reducing OOD misclassification.
ES improves training efficiency by dynamically selecting data samples.
The aim of this paper is to provide new theoretical and computational understanding on two loss regularizations employed in deep learning, known as local entropy and heat regularization. For both regularized losses we introduce variational characterizations that naturally suggest a two-step scheme for their optimizatio…
Paper proposes efficient weight updates for edge nodes with minimal communication.
Introduces SoRR for aggregating losses in supervised learning.
PAIR-CI calibrates CI tests for causal discovery with incomplete data.
Decision trees and logistic regression are one of the most popular and well-known machine learning algorithms, frequently used to solve a variety of real-world problems. Stability of learning algorithms is a powerful tool to analyze their performance and sensitivity and subsequently allow researchers to draw reliable c…
The paper analyzes how repeating epochs affects data scaling in linear regression.