Search Results for author: James Schmidt

Found 3 papers, 1 papers with code

Taylor Learning

no code implementations24 May 2023 James Schmidt

Empirical risk minimization stands behind most optimization in supervised machine learning.

Testing for Overfitting

1 code implementation9 May 2023 James Schmidt

High complexity models are notorious in machine learning for overfitting, a phenomenon in which models well represent data but fail to generalize an underlying data generating process.

Holdout Set valid

Latent Properties of Lifelong Learning Systems

no code implementations28 Jul 2022 Corban Rivera, Chace Ashcraft, Alexander New, James Schmidt, Gautam Vallabha

Creating artificial intelligence (AI) systems capable of demonstrating lifelong learning is a fundamental challenge, and many approaches and metrics have been proposed to analyze algorithmic properties.

reinforcement-learning Reinforcement Learning (RL)

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