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Found 10 papers, 3 papers with code
Towards Modeling Uncertainties of Self-explaining Neural Networks via Conformal Prediction
To tackle the aforementioned challenges, in this paper, we design a novel uncertainty modeling framework for self-explaining networks, which not only demonstrates strong distribution-free uncertainty modeling performance for the generated explanations in the interpretation layer but also excels in producing efficient and effective prediction sets for the final predictions based on the informative high-level basis explanations.
AdvST: Revisiting Data Augmentations for Single Domain Generalization
Then, we propose Adversarial learning with Semantics Transformations (AdvST) that augments the source domain data with semantics transformations and learns a robust model with the augmented data.
Improving Faithfulness for Vision Transformers
However, ViTs suffer from issues with explanation faithfulness, as their focal points are fragile to adversarial attacks and can be easily changed with even slight perturbations on the input image.
Automated Natural Language Explanation of Deep Visual Neurons with Large Models
Deep neural networks have exhibited remarkable performance across a wide range of real-world tasks.
MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation
Health risk prediction is one of the fundamental tasks under predictive modeling in the medical domain, which aims to forecast the potential health risks that patients may face in the future using their historical Electronic Health Records (EHR).
Inductive Graph Unlearning
To extend machine unlearning to graph data, \textit{GraphEraser} has been proposed.
Understanding and Enhancing Robustness of Concept-based Models
Subsequently, we propose a potential general adversarial training-based defense mechanism to increase robustness of these systems to the proposed malicious attacks.
SEAT: Stable and Explainable Attention
Results show that SEAT is more stable against different perturbations and randomness while also keeps the explainability of attention, which indicates it is a more faithful explanation.
Path-specific Causal Fair Prediction via Auxiliary Graph Structure Learning
To tackle these challenges, we propose a novel casual graph based fair prediction framework, which integrates graph structure learning into fair prediction to ensure that unfair pathways are excluded in the causal graph.
Representation Learning for Treatment Effect Estimation from Observational Data
Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due to the missing counterfactuals and the selection bias.
