More than reading about theory, actual experiences are what shape our understanding. These can be efficiently provided ...

There is indeed a vast literature on the design and analysis of decision tree algorithms that aim at optimizing these parameters. This paper contributes to this important line of research: we propose ...

Abstract: The challenge of the exploration-exploitation dilemma persists in off-policy reinforcement learning (RL) algorithms, impeding the improvement of policy performance and sample efficiency. To ...

Abstract: There is increasingly interest in developing embedded machine learning hardware as it can offer better performance in terms of privacy, bandwidth efficiency, and scalability.