Autonomous agents operating in real-world scenarios frequently encounter uncertainty and make decisions based on incomplete information.

Learning to simulate complex physics with graph networks.

SEEDS outperform or are competitive with previous SDE solvers.

Finally, PDE-Refiner's connection to diffusion models enables an accurate and efficient assessment of the model's predictive uncertainty, allowing us to estimate when the surrogate becomes inaccurate.

In contrast, we directly evaluate the matrix equations which arise from applying variable elimination on the Lagrange multiplier terms in the (differential) KKT system.

We develop a deeper understanding of its relationship with more conventional analyses of extragradient methods [Nem04, Nes07]. We also give improved solvers for the subproblems required by variants of the [She17] algorithm, designed through the lens of relative smoothness [BBT17, LFN18].

Work completed at the University of Washington. Work completed at Microsoft Research.

Two salient limitations have long hindered the relevance of optimal transport methods to machine learning.