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Does Distributionally Robust Supervised Learning Give Robust Classifiers?

, , , and . ICML, volume 80 of Proceedings of Machine Learning Research, page 2034-2042. PMLR, (2018)

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Strategies for Pre-training Graph Neural Networks, , , , , , and . (2019)cite arxiv:1905.12265Comment: Accepted as a spotlight to ICLR 2020.Wilds: A benchmark of in-the-wild distribution shifts, , , , , , , , , and 1 other author(s). International Conference on Machine Learning, page 5637--5664. PMLR, (2021)Worst-case Redundancy of Optimal Binary AIFV Codes and their Extended Codes., , and . CoRR, (2016)Learning Discrete Representations via Information Maximizing Self-Augmented Training., , , , and . ICML, volume 70 of Proceedings of Machine Learning Research, page 1558-1567. PMLR, (2017)Pre-training Graph Neural Networks., , , , , , and . CoRR, (2019)How powerful are graph neural networks?, , , and . arXiv preprint arXiv:1810.00826, (2018)The Open Catalyst 2020 (OC20) Dataset and Community Challenges., , , , , , , , , and 7 other author(s). CoRR, (2020)GraphCast: Learning skillful medium-range global weather forecasting., , , , , , , , , and 8 other author(s). CoRR, (2022)ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations., , , , , , , and . CoRR, (2021)Dynamic monitoring of lively footbridges., , , and . exp.at, page 135-136. IEEE, (2015)