Quasimetric Learning

Quasimetric learning focuses on representing and learning from asymmetrical distance relationships, prevalent in diverse fields like robotics, phylogenetic analysis, and reinforcement learning. Current research emphasizes developing novel algorithms and neural architectures, such as those based on hypernetworks and interval arithmetic, that effectively capture and utilize quasimetric structures, often addressing challenges like catastrophic forgetting in continual learning and improving sample efficiency in reinforcement learning. This area holds significant promise for advancing machine learning applications by enabling more accurate modeling of real-world asymmetries and improving the performance of algorithms that rely on distance metrics.