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Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models
However, it has several unique challenges.
Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning
This has motivated numerous studies aiming to reduce the variance and improve convergence of FL on non-IID data [6, 9, 14, 17, 19, 30]. On another note, constraints on communication resources and therefore on the number of clients that may participate in training additionally complicate implementation of FL schemes.
Temporal Robustness against Data Poisoning
Data poisoning considers cases when an adversary manipulates the behavior of machine learning algorithms through malicious training data.
Conformal Meta-learners for Predictive Inference of Individual Treatment Effects
Previous work has focused primarily on developing ML-based "meta-learners" that can provide point estimates of the conditional
Appendix T able of Contents
We provide the guidelines presented to the users for the creation of the dataset. To see some examples of how the guidelines can be applied, visit the examples document. You can use it to rate each guideline and leave feedback for each task. The user should be allowed to refuse to give up any information. Ask the user to elaborate or rephrase instead.
LaSCal: Label-ShiftCalibrationwithouttargetlabels
Calibration error (CE) provides insights into the alignment between the predicted confidence scores and the classifier accuracy.
DeepPCR: Parallelizing Sequential Operations in Neural Networks
Despite this, several operations are still performed in a sequential manner.
Causal Fairness for Outcome Control
We next discuss two lines of related literature.