Synopsis of recent research by authors named "Howard Heaton"
- Howard Heaton's recent research focuses on enhancing explainability in artificial intelligence (AI) and optimization algorithms, addressing critical challenges in the interpretability of machine learning models and efficient algorithms.
- His paper titled "Explainable AI via learning to optimize" proposes tools for creating transparent algorithms that allow for the inclusion of prior knowledge and identification of unreliable predictions in AI systems.
- Heaton also explores optimization techniques with his article "A Hamilton-Jacobi-based proximal operator," addressing limitations in existing proximal operators and their effectiveness in first-order optimization algorithms, demonstrating a need for more comprehensive methods in the field.
