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Robust distillation for worst-class performance: on the interplay between teacher and student objectives.

, , , , , and . UAI, volume 216 of Proceedings of Machine Learning Research, page 2237-2247. PMLR, (2023)

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Textual Features for Programming by Example, , , , and . CoRR, (2012)Complementary-Label Learning for Arbitrary Losses and Models., , , and . CoRR, (2018)ResMem: Learn what you can and memorize the rest., , , , , , , and . CoRR, (2023)Predicting Short-Term Public Transport Demand via Inhomogeneous Poisson Processes., and . CIKM, page 2207-2210. ACM, (2017)AutoRec: Autoencoders Meet Collaborative Filtering., , , and . WWW (Companion Volume), page 111-112. ACM, (2015)Latent feature models for dyadic prediction /.. University of California, San Diego, USA, (2013)base-search.net (ftcdlib:qt4xw874p5).SupMMD: A Sentence Importance Model for Extractive Summarisation using Maximum Mean Discrepancy., , , and . EMNLP (Findings), volume EMNLP 2020 of Findings of ACL, page 4108-4122. Association for Computational Linguistics, (2020)A scaled Bregman theorem with applications., , and . NIPS, page 19-27. (2016)Federated Learning with Only Positive Labels., , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 10946-10956. PMLR, (2020)In defense of dual-encoders for neural ranking., , , , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 15376-15400. PMLR, (2022)