Regression
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S Supplementary Materials 1 We confirm that the dataset will be available to the community under an MIT licence and that we
For what purpose was the dataset created?
Technology: Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Regression (0.33)
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LEACE: Perfect linear concept erasure in closed form
Concept erasure aims to remove specified features from a representation. It can improve fairness (e.g.
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FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression Y ouguang Chen George Biros Oden Institute for Computational Engineering and Sciences The University of Texas at Austin
Based on our theoretical analysis, we propose an active learning algorithm that employs regret minimization to minimize the FIR.
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Derandomized novelty detection with FDR control via conformal e-values
Instead, we apply powerful machine learning models for one-class (Moya et al., 1993) or
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Sub-optimality of the Naive Mean Field approximation for proportional high-dimensional Linear Regression
Despite its popularity in practice, theoretical guarantees for high-dimensional problems are only available under strong structural assumptions (e.g., sparsity).
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Optimal Unbiased Randomizers for Regression with Label Differential Privacy
We propose a new family of label randomizers for training regression models under the constraint of label differential privacy (DP).
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