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On Calibration of Modern Neural Networks

, , , and . roceedings of the 34th International Conference on Machine Learning, 70, page 1321-1330. PMLR, (2017)

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EIFFeL: Ensuring Integrity for Federated Learning., , , and . CoRR, (2021)Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano., , and . CoRR, (2022)Byzantine-Robust and Privacy-Preserving Framework for FedML., , , and . CoRR, (2021)Differentially Private Representation Learning via Image Captioning., , , , , , and . CoRR, (2024)Measuring data leakage in machine-learning models with Fisher information., , and . UAI, volume 161 of Proceedings of Machine Learning Research, page 760-770. AUAI Press, (2021)Privacy-aware compression for federated data analysis., , and . UAI, volume 180 of Proceedings of Machine Learning Research, page 296-306. PMLR, (2022)Private Prediction Strikes Back! Private Kernelized Nearest Neighbors with Individual Rényi Filter., , , and . UAI, volume 216 of Proceedings of Machine Learning Research, page 2586-2596. PMLR, (2023)On tight bounds for binary frameproof codes., , and . CoRR, (2014)Supervised Word Mover's Distance., , , , , and . NIPS, page 4862-4870. (2016)BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining., , , , and . NeurIPS, page 18527-18538. (2021)