Interpretable Model

Interpretable models aim to create machine learning systems whose decision-making processes are transparent and understandable to humans, addressing the "black box" problem of many high-performing models. Current research focuses on developing inherently interpretable architectures like generalized additive models (GAMs), decision trees, rule lists, and symbolic regression, as well as post-hoc explanation methods for existing models, such as SHAP and LIME. This emphasis on interpretability is driven by the need for trust, accountability, and the ability to gain insights from complex data in fields ranging from healthcare and finance to scientific discovery, where understanding model decisions is crucial for effective application and responsible use. The development of more accurate and efficient methods for creating and evaluating interpretable models is a major focus of ongoing research.

Papers

October 5, 2023

A Quantitatively Interpretable Model for Alzheimer's Disease Prediction Using Deep Counterfactuals
Kwanseok Oh, Da-Woon Heo, Ahmad Wisnu Mulyadi, Wonsik Jung, Eunsong Kang, Kun Ho Lee, Heung-Il Suk
Deep Learning Alzheimer'S Disease Interpretable Model Counterfactual Reasoning Disease Prediction Gray Matter

October 4, 2023

Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation
Julia Herbinger, Susanne Dandl, Fiona K. Ewald, Sofia Loibl, Giuseppe Casalicchio
Interpretable Model Model Distillation Interpretable Surrogate Model Model Tree

September 29, 2023

Efficient Interpretable Nonlinear Modeling for Multiple Time Series
Kevin Roy, Luis Miguel Lopez-Ramos, Baltasar Beferull-Lozano
Interpretable Model Nonlinear Model Time Series Prediction Vector Autoregressive

September 14, 2023

Doubly High-Dimensional Contextual Bandits: An Interpretable Model for Joint Assortment-Pricing
Junhui Cai, Ran Chen, Martin J. Wainwright, Linda Zhao
Contextual Bandit Interpretable Model Assortment Optimization Bandit Learning

August 29, 2023

Probabilistic Dataset Reconstruction from Interpretable Models
Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie-José Huguet, Mohamed Siala
Inherent Interpretability Interpretable Model Data Leakage Interpretable Regression

August 17, 2023

Interpretable Graph Neural Networks for Tabular Data
Amr Alkhatib, Sofiane Ennadir, Henrik Boström, Michalis Vazirgiannis
Graph Neural Network Representation Learning Tabular Data Interpretable Model Interpretable Graph Neural Network

August 9, 2023

Towards true discovery of the differential equations
Alexander Hvatov, Roman Titov
Scientific Discovery Interpretable Model Differential Equation Equation Discovery Differential Equation Discovery

August 2, 2023

LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs
Benjamin J. Lengerich, Sebastian Bordt, Harsha Nori, Mark E. Nunnally, Yin Aphinyanaphongs, Manolis Kellis, Rich Caruana
Large Language Model Interpretable Model Open Source LLM Generalized Additive Model Third Position Repair Model Generated Summary

July 28, 2023

Toward Transparent Sequence Models with Model-Based Tree Markov Model
Chan Hsu, Wei-Chun Huang, Jun-Ting Wu, Chih-Yuan Li, Yihuang Kang
Interpretable Model Long Sequence Sequence Model Tree Based Model Sequence Data Semi Markov Hidden Semi Markov Model

July 27, 2023

Discriminative Feature Attributions: Bridging Post Hoc Explainability and Inherent Interpretability
Usha Bhalla, Suraj Srinivas, Himabindu Lakkaraju
Inherent Interpretability Interpretable Model Black Box Model Post Hoc Distractor Generation

July 26, 2023

The Co-12 Recipe for Evaluating Interpretable Part-Prototype Image Classifiers
Meike Nauta, Christin Seifert
Interpretable Model Computer Vision Model Explanation Quality Prototype Based Simple Recipe

July 17, 2023

Cross Feature Selection to Eliminate Spurious Interactions and Single Feature Dominance Explainable Boosting Machines
Shree Charran R, Sandipan Das Mahapatra
Inherent Interpretability Interpretable Model Concept Based Better Interpretability Explainable Boosting Machine

July 16, 2023

It's All Relative: Interpretable Models for Scoring Bias in Documents
Aswin Suresh, Chi-Hsuan Wu, Matthias Grossglauser
Inherent Interpretability Absolute Stance Bias Interpretable Model Document Relevance Bias Score

July 7, 2023

Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat
Shantanu Ghosh, Ke Yu, Forough Arabshahi, Kayhan Batmanghelich
Mixture Component Black Box Interpretable Model Interpretable by Design Expert Annotated

July 2, 2023

Conditionally Invariant Representation Learning for Disentangling Cellular Heterogeneity
Hananeh Aliee, Ferdinand Kapl, Soroor Hediyeh-Zadeh, Fabian J. Theis
Interpretable Model Single Cell Latent Feature Cell Population Invariant Predictor Conditional Invariant

July 1, 2023

The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations
Vinitra Swamy, Jibril Frej, Tanja Käser
Explainable Artificial Intelligence Future Reasoning Interpretable Model xAI Community Post Hoc Explanation Interpretable Neural Network Post Hoc Explainability Human Centered XAI Human Centered Explainable AI

June 23, 2023

Co-creating a globally interpretable model with human input
Rahul Nair
Interpretable Model Human Ai Collaboration Natural Language Input Decision Logic Boolean Rule Joint Decision Making

June 20, 2023

Learning Locally Interpretable Rule Ensemble
Kentaro Kanamori
Interpretable Model Interpretable Rule Local Interpretability Rule Ensemble

June 14, 2023

SaDI: A Self-adaptive Decomposed Interpretable Framework for Electric Load Forecasting under Extreme Events
Hengbo Liu, Ziqing Ma, Linxiao Yang, Tian Zhou, Rui Xia, Yi Wang, Qingsong Wen, Liang Sun
Interpretable Model Load Forecasting Extreme Event Generalized Additive Model Novel Forecasting Self Interpretable

June 12, 2023

FIRE: An Optimization Approach for Fast Interpretable Rule Extraction
Brian Liu, Rahul Mazumder
Inherent Interpretability Interpretable Model Fire Occurrence Ensemble Algorithm Rule Extraction Like Rule