“Market Manipulation: An Adversarial Learning Framework for Detection and Evasion,” a new paper by University of Michigan Professor Michael Wellman and Xintong Wang, has been accepted by IJCAI-20. In the paper, they propose an adversarial learning framework to capture the evolving game between a regulator who develops tools to detect market manipulation and a manipulator who obfuscates actions to evade detection. Their experimental results demonstrate the possibility of automatically generating a diverse set of unseen manipulation strategies that can facilitate the training of more robust detection algorithms.