Statistical Learning
Reproducibility
For each algorithm we report a range, e.g.
Clustering the Sketch: Dynamic Compression for Embedding Tables Henry Ling-Hei Tsang
However, categorical features present a unique challenge as they require embedding a typically vast vocabulary into a smaller vector space for further calculations.
Robust covariance estimation with missing values and cell-wise contamination
The aforementioned references focus solely on minimising the entrywise error for imputed entries.
e4165c96702bac5f4962b70f3cf2f136-Paper-Conference.pdf
Optimizing proper loss functions is popularly believed to yield predictors with good calibration properties; the intuition being that for such losses, the global optimum is to predict the ground-truth probabilities, which is indeed calibrated.
The s-value: evaluating stability with respect to distributional shifts
However, sampling is not the only source of uncertainty. In practice, distributions change between locations and across time.
Robust Mixture Learning when Outliers Overwhelm Small Groups
We study the problem of estimating the means of well-separated mixtures when an adversary may add arbitrary outliers.
