Tag
Introduces a framework combining flow-based generative editing with evolutionary algorithms to perform optimization in residual space, enabling controllable data editing with non-differentiable objectives. Validated on MorphoMNIST and crystal data.
Moment Matching Q-Learning (MoMa QL) uses maximum mean discrepancy to match all moment statistics for distribution-level convergence in offline RL, achieving computational efficiency and strong performance on D4RL tasks.