2024 "interpretable machine learning" Papers
8 papers found
Compositional Few-Shot Class-Incremental Learning
Yixiong Zou, Shanghang Zhang, haichen zhou et al.
ICML 2024poster
Gaussian Process Neural Additive Models
Wei Zhang, Brian Barr, John Paisley
AAAI 2024paperarXiv:2402.12518
11
citations
Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions
Harrie Oosterhuis, Lijun Lyu, Avishek Anand
ICML 2024poster
Post-hoc Part-Prototype Networks
Andong Tan, Fengtao ZHOU, Hao Chen
ICML 2024poster
Prospector Heads: Generalized Feature Attribution for Large Models & Data
Gautam Machiraju, Alexander Derry, Arjun Desai et al.
ICML 2024poster
Removing Spurious Concepts from Neural Network Representations via Joint Subspace Estimation
Floris Holstege, Bram Wouters, Noud van Giersbergen et al.
ICML 2024poster
Rethinking Robustness of Model Attributions
Sandesh Kamath, Sankalp Mittal, Amit Deshpande et al.
AAAI 2024paperarXiv:2312.10534
2
citations
Towards Modeling Uncertainties of Self-explaining Neural Networks via Conformal Prediction
Wei Qian, Chenxu Zhao, Yangyi Li et al.
AAAI 2024paperarXiv:2401.01549
10
citations