Giora Simchoni — accepted AI-conference papers

2025

Flexible Copula-Based Mixed Models in Deep Learning: A Scalable Approach to Arbitrary Marginals

AISTATS 2025poster

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…

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2021

Using Random Effects to Account for High-Cardinality Categorical Features and Repeated Measures in Deep Neural Networks

Giora Simchoni, Saharon Rosset

NeurIPS 2021poster

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…

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