Media Summary: Abstract: Graph Neural Networks (GNNs) have become a popular tool for learning algorithmic tasks, related to combinatorial ... Abstract: In this talk, we introduce methods that remove the barrier for applying neural networks in real-life power systems and ... Parametric Optimization Beyond Discretization Abstract: Many applications require solving a family of optimization problems, ...
Ai4opt Seminar Series Using Machine - Detailed Analysis & Overview
Abstract: Graph Neural Networks (GNNs) have become a popular tool for learning algorithmic tasks, related to combinatorial ... Abstract: In this talk, we introduce methods that remove the barrier for applying neural networks in real-life power systems and ... Parametric Optimization Beyond Discretization Abstract: Many applications require solving a family of optimization problems, ... Title: On the Foundations of Interactive Decision Making and Reinforcement Learning Abstract: In this talk, we will present a new ... Abstract: Neural network driven applications suffer from hallucination and calibration issues where they confidently provide ... Abstract: One surprising trait of neural networks is the extent to which their connections can be pruned
Abstract: In many applications of reinforcement learning (RL) and control, policies need to satisfy constraints to ensure feasibility, ... Abstract: Markov decision processes (MDPs) constitute one of the predominant modeling and solution paradigms for dynamic ... Abstract: Data-driven learning and decision-making in complex systems are often subject to a variety of operational constraints ...
