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MUDD: A New Re-Identification Dataset with Efficient Annotation for Off-Road Racers in Extreme Conditions.

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Active Learning with Partial Feedback., , , and . ICLR (Poster), OpenReview.net, (2019)Predicting Surgery Duration with Neural Heteroscedastic Regression., , , , and . MLHC, volume 68 of Proceedings of Machine Learning Research, page 100-111. PMLR, (2017)Embryo Staging with Weakly-Supervised Region Selection and Dynamically-Decoded Predictions., , , , , and . MLHC, volume 106 of Proceedings of Machine Learning Research, page 663-679. PMLR, (2019)Rebounding Bandits for Modeling Satiation Effects., , , and . CoRR, (2020)Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data., , , and . CoRR, (2020)Practical Benefits of Feature Feedback Under Distribution Shift., , , , and . BlackboxNLP@EMNLP, page 346-355. Association for Computational Linguistics, (2022)Goodhart's Law Applies to NLP's Explanation Benchmarks., , , and . EACL (Findings), page 1322-1335. Association for Computational Linguistics, (2024)Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations., , , , , and . AAAI, page 5277-5285. AAAI Press, (2022)A Field Test of Bandit Algorithms for Recommendations: Understanding the Validity of Assumptions on Human Preferences in Multi-armed Bandits., , , , and . CHI, page 504:1-504:16. ACM, (2023)Personalized Language Modeling from Personalized Human Feedback., , and . CoRR, (2024)