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Found 4 papers, 1 papers with code
Uncertainty Estimation based on Geometric Separation
In machine learning, accurately predicting the probability that a specific input is correct is crucial for risk management.
A Geometric Method for Improved Uncertainty Estimation in Real-time
Predicting the probability of a specific input to be correct is called uncertainty (or confidence) estimation and is crucial for risk management.
QUIC-FL: Quick Unbiased Compression for Federated Learning
Distributed Mean Estimation (DME), in which $n$ clients communicate vectors to a parameter server that estimates their average, is a fundamental building block in communication-efficient federated learning.
Verifying Robustness of Gradient Boosted Models
Robustness to small perturbations of the input is an important quality measure for machine learning models, but the literature lacks a method to prove the robustness of gradient boosted models.
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