We quantify the worst-case price of fairness for matroid allocation problems in various settings, from adversarial, to semi random, to fully random.

We introduce a generative masked autoregressive neural operator that models parametric PDE dynamics in latent space, enabling efficient trajectory generation, in-context learning, and few-shot generalization from irregular data.