5 accepted papers
We present a methodology for model evaluation and selection in binary classification models with the presence of correlations in the data, where the sampling mechanism violates the i.i.d. assumption. Our methodology involves a formulation of the bias term between the standard Cross-Validation (CV) e…
We introduce copula-based neural networks (COPNN), a novel framework that extends beyond the limitations of Gaussian marginals for random effects in mixed models. COPNN integrates the flexibility of Gaussian copulas in capturing rich dependence structures with arbitrary marginal distributions, with…
Accurate and reliable probability predictions are essential for multi-class supervised learning tasks, where well-calibrated models enable rational decision-making. While isotonic regression has proven effective for binary calibration, its extension to multi-class problems via one-vs-rest calibratio…
High-cardinality categorical features are a major challenge for machine learning methods in general and for deep learning in particular. Existing solutions such as one-hot encoding and entity embeddings can be hard to scale when the cardinality is very high, require much space, are hard to interpret…
The problem of handling adaptivity in data analysis, intentional or not, permeates a variety of fields, including test-set overfitting in ML challenges and the accumulation of invalid scientific discoveries. We propose a mechanism for answering an arbitrarily long sequence of potentially a…