Statistical mechanics is one of the pillars of modern physics. Ludwig Boltzmann (1844–1906) and Josiah Willard Gibbs (1839–1903) were its primary formulators. They both worked to establish a bridge ...

Statistical Learning Theory provides a mathematical framework for understanding how algorithms infer predictive rules from data. At its core lies the notion of risk: the expected loss of a model on ...

With recent advances in sequencing, genotyping arrays, and imputation, GWAS now aim to identify associations with rare and uncommon genetic variants. Here, we describe and evaluate a class of ...

However, Boltzmann-Gibbs statistical mechanics has limitations. For example, its predictions can fail when a system is in certain regimes, such as phase transitions or critical phenomena. For instance ...

We develop an EPIC-based lifelong reinforcement learning framework that enables adaptive policy updates and efficient knowledge transfer across tasks while ensuring statistical generalization ...