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 ...

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 ...

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 ...

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