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Found 6 papers, 2 papers with code
Loss Minimization through the Lens of Outcome Indistinguishability
This decomposition highlights the utility of a new multi-group fairness notion that we call calibrated multiaccuracy, which lies in between multiaccuracy and multicalibration.
KL Divergence Estimation with Multi-group Attribution
Estimating the Kullback-Leibler (KL) divergence between two distributions given samples from them is well-studied in machine learning and information theory.
Omnipredictors
We suggest a rigorous new paradigm for loss minimization in machine learning where the loss function can be ignored at the time of learning and only be taken into account when deciding an action.
Multicalibrated Partitions for Importance Weights
We significantly strengthen previous work that use the MaxEntropy approach, that define the importance weights based on a distribution $Q$ closest to $P$, that looks the same as $R$ on every set $C \in \mathcal{C}$, where $\mathcal{C}$ may be a huge collection of sets.
PIDForest: Anomaly Detection via Partial Identification
We consider the problem of detecting anomalies in a large dataset.
Efficient Anomaly Detection via Matrix Sketching
We consider the problem of finding anomalies in high-dimensional data using popular PCA based anomaly scores.
