Theoretical Guarantee
Theoretical guarantees in machine learning aim to provide rigorous mathematical proof of an algorithm's performance, ensuring reliable predictions and model behavior. Current research focuses on establishing such guarantees for various models and algorithms, including generative models (like diffusion flow matching), evolutionary algorithms (e.g., NSGA-II variants), and deep learning architectures (e.g., CNNs and Bayesian neural networks), often in the context of specific challenges like robustness to noise, fairness, and domain adaptation. This pursuit of theoretical underpinnings is crucial for building trust in machine learning systems and advancing their applicability in high-stakes domains where reliable performance is paramount.
Papers
Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees
Sharan Vaswani, Amirreza Kazemi, Reza Babanezhad, Nicolas Le Roux
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference
Insung Kong, Dongyoon Yang, Jongjin Lee, Ilsang Ohn, Gyuseung Baek, Yongdai Kim