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Differentially Private Learning of Distributed Deep Models., , , and . UMAP (Adjunct Publication), page 193-200. ACM, (2020)Tradeoff Analysis of Discrepancy-Based Adaptive Thresholding Approach., , and . EBCCSP, page 1-4. IEEE, (2019)Tessellation-Filtering ReLU Neural Networks., , , , , and . IJCAI, page 3335-3341. ijcai.org, (2022)Stability of threshold-based sampling as metric problem.. EBCCSP, page 1-8. IEEE, (2015)Estimating the signal reconstruction error from threshold-based sampling without knowing the original signal.. EBCCSP, page 1-4. IEEE, (2017)The balancing principle for parameter choice in distance-regularized domain adaptation., , , , , , , and . NeurIPS, page 20798-20811. (2021)On preserving metric properties of integrate-and-fire sampling.. EBCCSP, page 1-7. IEEE, (2016)Deriving an Optimal Noise Adding Mechanism for Privacy-Preserving Machine Learning., , , and . DEXA Workshops, volume 1062 of Communications in Computer and Information Science, page 108-118. Springer, (2019)Variational Optimization of Informational Privacy., , , and . DEXA Workshops, volume 1285 of Communications in Computer and Information Science, page 32-47. Springer, (2020)Matching event sequences approach based on Weyl's discrepancy norm.. EBCCSP, page 1-5. IEEE, (2015)