surrogate-models

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topic2 events

papersTODAY 04:00 UTC

SAILS: New Method Reveals Functional Form of Feature Interactions in ML Models

A new arXiv paper introduces SAILS, a surrogate-based approach that uses local effect smooths to characterize how features interact inside machine learning models. Unlike prior explanation techniques that only flag or score interactions, or that handle just a narrow set of interaction shapes, SAILS aims to expose the actual functional form of those interactions. The work is posted as a cross-listing on arXiv's cs.AI and cs.LG categories.

interpretability/explainable-ai/research/arxiv

papersSEP 10 04:00 UTC

Paper introduces constraint-aware discrete black-box optimization using tensor decomposition

A new arXiv preprint tackles optimization of costly-to-evaluate functions over discrete domains under constraints. The proposed method incorporates tensor decomposition into sequential model-based optimization, using surrogate models to improve sample efficiency.

optimization/machine-learning/surrogate-models/research