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Found 6 papers, 2 papers with code
Querying Easily Flip-flopped Samples for Deep Active Learning
Active learning is a machine learning paradigm that aims to improve the performance of a model by strategically selecting and querying unlabeled data.
ESD: Expected Squared Difference as a Tuning-Free Trainable Calibration Measure
Studies have shown that modern neural networks tend to be poorly calibrated due to over-confident predictions.
Deep Neural Network Based Accelerated Failure Time Models using Rank Loss
An accelerated failure time (AFT) model assumes a log-linear relationship between failure times and a set of covariates.
Active Learning: Sampling in the Least Probable Disagreement Region
This strategy is valid only when the sample's "closeness" to the decision boundary can be estimated.
Fast and Efficient MMD-based Fair PCA via Optimization over Stiefel Manifold
This paper defines fair principal component analysis (PCA) as minimizing the maximum mean discrepancy (MMD) between dimensionality-reduced conditional distributions of different protected classes.
Least Probable Disagreement Region for Active Learning
Active learning strategy to query unlabeled samples nearer the estimated decision boundary at each step has been known to be effective when the distance from the sample data to the decision boundary can be explicitly evaluated; however, in numerous cases in machine learning, especially when it involves deep learning, conventional distance such as the $\ell_p$ from sample to decision boundary is not readily measurable.
