"interpretable machine learning" Papers
18 papers found
Conference
Bayesian Concept Bottleneck Models with LLM Priors
Jean Feng, Avni Kothari, Lucas Zier et al.
Causally Reliable Concept Bottleneck Models
Giovanni De Felice, Arianna Casanova Flores, Francesco De Santis et al.
CHiQPM: Calibrated Hierarchical Interpretable Image Classification
Thomas Norrenbrock, Timo Kaiser, Sovan Biswas et al.
Differentiable Rule Induction from Raw Sequence Inputs
Kun Gao, Katsumi Inoue, Yongzhi Cao et al.
Empowering Decision Trees via Shape Function Branching
Nakul Upadhya, Eldan Cohen
From GNNs to Trees: Multi-Granular Interpretability for Graph Neural Networks
Jie Yang, Yuwen Wang, Kaixuan Chen et al.
MAGE: Model-Level Graph Neural Networks Explanations via Motif-based Graph Generation
Zhaoning Yu, Hongyang Gao
MIX: A Multi-view Time-Frequency Interactive Explanation Framework for Time Series Classification
Viet-Hung Tran, Ngoc Phu Doan, Zichi Zhang et al.
V2C-CBM: Building Concept Bottlenecks with Vision-to-Concept Tokenizer
Hangzhou He, Lei Zhu, Xinliang Zhang et al.
Compositional Few-Shot Class-Incremental Learning
Yixiong Zou, Shanghang Zhang, haichen zhou et al.
Gaussian Process Neural Additive Models
Wei Zhang, Brian Barr, John Paisley
Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions
Harrie Oosterhuis, Lijun Lyu, Avishek Anand
Post-hoc Part-Prototype Networks
Andong Tan, Fengtao ZHOU, Hao Chen
Prospector Heads: Generalized Feature Attribution for Large Models & Data
Gautam Machiraju, Alexander Derry, Arjun Desai et al.
Removing Spurious Concepts from Neural Network Representations via Joint Subspace Estimation
Floris Holstege, Bram Wouters, Noud van Giersbergen et al.
Rethinking Robustness of Model Attributions
Sandesh Kamath, Sankalp Mittal, Amit Deshpande et al.
This Probably Looks Exactly Like That: An Invertible Prototypical Network
Zachariah Carmichael, Timothy Redgrave, Daniel Gonzalez Cedre et al.
Towards Modeling Uncertainties of Self-explaining Neural Networks via Conformal Prediction
Wei Qian, Chenxu Zhao, Yangyi Li et al.