arXiv:2607. 02203v1 Announce Type: new Abstract: Operator learning has emerged as a powerful tool for modeling complex physical systems in functional spaces.
By Mojgan Alishiri, Amirhossein Arzani
arXiv:2607. 14970v1 Announce Type: new Abstract: Automated optimisation is increasingly adopted in industrial processes, yet a trust gap persists between engineers who design these algorithms and operators who must act on their recommendations.
By Paul Darm, Cem Alpturk, Kenneth Ulrich, William Duncan, Ali Anwar, Annalisa Riccardi
arXiv:2608. 11508v1 Announce Type: new Abstract: Machine learning pipelines commonly flatten relational data into single-table representations, discarding structural constraints.
By Seungeun Lee, Joao Fonseca, Julia Stoyanovich
The paper introduces Adaptive Derivative-Ordered Random Explanation (ADORE), a unified framework that uses first- and second-order derivatives to capture nonlinear feature interactions and feature-sample dynamics. ADORE combines global feature importance with local sample contributions, quantifying both magnitude and direction of feature impact while identifying critical samples. It achieves computational efficiency via randomized SVD and dynamic sparsity detection, outperforming LIME and SHAP across tabular, text, and image data, and is released as an open-source Python package on GitHub.
By Lemen Chao, Ming Lei, Anran Fanga
arXiv:2607. 09235v1 Announce Type: cross Abstract: The current state of the art in AI/ML rests on deep neural architectures, which, in general, suffer from a lack of interpretability.
By Jayadeva, Madhur Aswani
DynSHAP is a SHAP-based framework designed for dynamic survival analysis, extending marginal SHAP estimators to handle time–feature pairs as players in the Shapley game. It introduces Temporal DynSHAP, which models linear dependencies across time and uses conditional sampling to improve explanations. Experiments on synthetic data and two real-world clinical datasets show that Temporal DynSHAP more accurately recovers temporally dependent features and provides faithful attributions for two DSA architectures, enabling medical experts to identify which patient information influenced predictions and when.
By Nastasya Anokhina, Jonas J\"ur{\ss}, Pietro Li\`o