CECF is a new causal framework for edge classification that balances high-dimensional edge features against node influences via GNN embeddings and cross-attention to achieve better performance than standard methods.
Causal machine learning: A survey and open problems
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XWP and XWP_c are novel attribution methods for FCNNs that estimate feature importance by perturbing attached weights to avoid added bias and out-of-distribution issues in occlusion approaches.
A new optimization algorithm with double machine learning for wildfire spread estimation enables better crew assignments that reduce total area burned.
TabFORGE generates high-quality synthetic tabular data by leveraging pretrained causality-aware representations in a two-stage diffusion-decoder architecture that mitigates latent distribution shifts.
citing papers explorer
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Advancing Edge Classification through High-Dimensional Causal Modeling of Node-Edge Interplay
CECF is a new causal framework for edge classification that balances high-dimensional edge features against node influences via GNN embeddings and cross-attention to achieve better performance than standard methods.
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From Weight Perturbation to Feature Attribution for Explaining Fully Connected Neural Networks
XWP and XWP_c are novel attribution methods for FCNNs that estimate feature importance by perturbing attached weights to avoid added bias and out-of-distribution issues in occlusion approaches.
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Predictive and Prescriptive AI toward Optimizing Wildfire Suppression
A new optimization algorithm with double machine learning for wildfire spread estimation enables better crew assignments that reduce total area burned.
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Tabular Foundation Model for Generative Modelling
TabFORGE generates high-quality synthetic tabular data by leveraging pretrained causality-aware representations in a two-stage diffusion-decoder architecture that mitigates latent distribution shifts.