ICML 2024 "model interpretability" Papers
11 papers found
Attribution-based Explanations that Provide Recourse Cannot be Robust
Hidde Fokkema, Rianne de Heide, Tim van Erven
ICML 2024posterarXiv:2205.15834
Explaining Graph Neural Networks via Structure-aware Interaction Index
Ngoc Bui, Trung Hieu Nguyen, Viet Anh Nguyen et al.
ICML 2024poster
Explaining Probabilistic Models with Distributional Values
Luca Franceschi, Michele Donini, Cedric Archambeau et al.
ICML 2024spotlightarXiv:2402.09947
Exploring the LLM Journey from Cognition to Expression with Linear Representations
Yuzi Yan, Jialian Li, YipinZhang et al.
ICML 2024posterarXiv:2405.16964
Improving Neural Additive Models with Bayesian Principles
Kouroche Bouchiat, Alexander Immer, Hugo Yèche et al.
ICML 2024posterarXiv:2305.16905
Iterative Search Attribution for Deep Neural Networks
Zhiyu Zhu, Huaming Chen, Xinyi Wang et al.
ICML 2024poster
KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions
Fabian Fumagalli, Maximilian Muschalik, Patrick Kolpaczki et al.
ICML 2024poster
On Gradient-like Explanation under a Black-box Setting: When Black-box Explanations Become as Good as White-box
Yi Cai, Gerhard Wunder
ICML 2024posterarXiv:2308.09381
Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities
Golnoosh Farnadi, Mohammad Havaei, Negar Rostamzadeh
ICML 2024posterarXiv:2406.01757
Position: Stop Making Unscientific AGI Performance Claims
Patrick Altmeyer, Andrew Demetriou, Antony Bartlett et al.
ICML 2024poster
Provably Better Explanations with Optimized Aggregation of Feature Attributions
Thomas Decker, Ananta Bhattarai, Jindong Gu et al.
ICML 2024posterarXiv:2406.05090