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 ...

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 ...

This paper frames a learned solver as a Markov operator on probability laws and measures refinement in the statistical-solution topology of Lanthaler-Mishra-Parés-Pulido (arXiv:1909.06615) so ...

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

GET: A Foundation Model for Transcription, Still Between Promise and Proof Tl;dr Statistical generalization isn’t scientific understanding—don’t confuse prediction with insight. This week’s AI ∩ Bio: ...