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Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale with MinDiff Loss.

, , , , , and . Trans. Mach. Learn. Res., (2023)

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Information Flow in Computational Systems., , and . IEEE Trans. Inf. Theory, 66 (9): 5456-5491 (2020)Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale with MinDiff Loss., , , , , and . Trans. Mach. Learn. Res., (2023)Quantifying Feature Contributions to Overall Disparity Using Information Theory., , and . CoRR, (2022)Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD., , , , and . AISTATS, volume 84 of Proceedings of Machine Learning Research, page 803-812. PMLR, (2018)On the Optimal Recovery Threshold of Coded Matrix Multiplication., , , , , and . IEEE Trans. Inf. Theory, 66 (1): 278-301 (2020)MONOPOLY: Financial Prediction from MONetary POLicY Conference Videos Using Multimodal Cues., , , , , , , and . ACM Multimedia, page 2276-2285. ACM, (2022)A Unified Coded Deep Neural Network Training Strategy Based on Generalized PolyDot Codes for Matrix Multiplication., , , , and . CoRR, (2018)"Short-Dot": Computing Large Linear Transforms Distributedly Using Coded Short Dot Products., , and . IEEE Trans. Inf. Theory, 65 (10): 6171-6193 (2019)CodeNet: Training Large Scale Neural Networks in Presence of Soft-Errors., , , and . CoRR, (2019)An Information-Theoretic Perspective on the Relationship Between Fairness and Accuracy., , , , , and . CoRR, (2019)