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Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability

8 April 2019

Papers citing "Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability"

29 / 29 papers shown

Title
Statistical Multicriteria Benchmarking via the GSD-Front Christoph Jansen G. Schollmeyer Julian Rodemann Hannah Blocher Thomas Augustin 92 4 0 06 Jun 2024
Position: A Call to Action for a Human-Centered AutoML Paradigm Marius Lindauer Florian Karl A. Klier Julia Moosbauer Alexander Tornede Andreas Mueller Frank Hutter Matthias Feurer Bernd Bischl 92 8 0 05 Jun 2024
Statistical inference using machine learning and classical techniques based on accumulated local effects (ALE) Chitu Okoli 56 3 0 15 Oct 2023
Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models Lennart Schneider B. Bischl Janek Thomas 96 7 0 17 Jul 2023
Interpreting and generalizing deep learning in physics-based problems with functional linear models Amirhossein Arzani Lingxiao Yuan P. Newell Bei Wang AI4CE 80 8 0 10 Jul 2023
SHAP-IQ: Unified Approximation of any-order Shapley Interactions Fabian Fumagalli Maximilian Muschalik Patrick Kolpaczki Eyke Hüllermeier Barbara Hammer 129 30 0 02 Mar 2023
Improving Interpretability via Explicit Word Interaction Graph Layer Arshdeep Sekhon Hanjie Chen A. Shrivastava Zhe Wang Yangfeng Ji Yanjun Qi AI4CE MILM 70 6 0 03 Feb 2023
Mind the Gap: Measuring Generalization Performance Across Multiple Objectives Matthias Feurer Katharina Eggensperger Eddie Bergman Florian Pfisterer B. Bischl Frank Hutter 116 5 0 08 Dec 2022
Comparing Explanation Methods for Traditional Machine Learning Models Part 2: Quantifying Model Explainability Faithfulness and Improvements with Dimensionality Reduction Montgomery Flora Corey K. Potvin A. McGovern Shawn Handler FAtt 110 4 0 18 Nov 2022
Comparing Explanation Methods for Traditional Machine Learning Models Part 1: An Overview of Current Methods and Quantifying Their Disagreement Montgomery Flora Corey K. Potvin A. McGovern Shawn Handler FAtt 97 18 0 16 Nov 2022
DALE: Differential Accumulated Local Effects for efficient and accurate global explanations Vasilis Gkolemis Theodore Dalamagas Christos Diou 56 13 0 10 Oct 2022
From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML Shalaleh Rismani Renee Shelby A. Smart Edgar W. Jatho Joshua A. Kroll AJung Moon Negar Rostamzadeh 106 42 0 06 Oct 2022
Machine Learning Workflow to Explain Black-box Models for Early Alzheimer's Disease Classification Evaluated for Multiple Datasets Louise Bloch Christoph M. Friedrich 28 14 0 12 May 2022
A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning Models Lennart Schneider Florian Pfisterer Janek Thomas B. Bischl 72 3 0 28 Apr 2022
Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Models Alexander Stevens Johannes De Smedt XAI FaML 113 14 0 30 Mar 2022
Marginal Effects for Non-Linear Prediction Functions Christian A. Scholbeck Giuseppe Casalicchio Christoph Molnar Bernd Bischl C. Heumann FAtt 16 11 0 21 Jan 2022
Application of Machine Learning Methods in Inferring Surface Water Groundwater Exchanges using High Temporal Resolution Temperature Measurements Mohammad A. Moghaddam T. Ferré Xingyuan Chen Kewei Chen M. Ehsani AI4CE 48 4 0 03 Jan 2022
YAHPO Gym -- An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization Florian Pfisterer Lennart Schneider Julia Moosbauer Martin Binder B. Bischl 82 39 0 08 Sep 2021
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges B. Bischl Martin Binder Michel Lang Tobias Pielok Jakob Richter ... Theresa Ullmann Marc Becker A. Boulesteix Difan Deng Marius Lindauer 250 514 0 13 Jul 2021
Explainable Artificial Intelligence Approaches: A Survey Sheikh Rabiul Islam W. Eberle S. Ghafoor Mohiuddin Ahmed XAI 87 104 0 23 Jan 2021
CDT: Cascading Decision Trees for Explainable Reinforcement Learning Zihan Ding Pablo Hernandez-Leal G. Ding Changjian Li Ruitong Huang 65 21 0 15 Nov 2020
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges Christoph Molnar Giuseppe Casalicchio B. Bischl AI4TS AI4CE 114 405 0 19 Oct 2020
The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies A. Markus J. Kors P. Rijnbeek 91 472 0 31 Jul 2020
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models Christoph Molnar Gunnar Konig J. Herbinger Timo Freiesleben Susanne Dandl Christian A. Scholbeck Giuseppe Casalicchio Moritz Grosse-Wentrup B. Bischl FAtt AI4CE 85 140 0 08 Jul 2020
What Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model Conversations Michal Kuzba P. Biecek HAI 55 22 0 07 Feb 2020
Towards Quantification of Explainability in Explainable Artificial Intelligence Methods Sheikh Rabiul Islam W. Eberle S. Ghafoor XAI 82 43 0 22 Nov 2019
Multi-Objective Automatic Machine Learning with AutoxgboostMC Florian Pfisterer Stefan Coors Janek Thomas B. Bischl 65 17 0 28 Aug 2019
Proposed Guidelines for the Responsible Use of Explainable Machine Learning Patrick Hall Navdeep Gill N. Schmidt SILM XAI FaML 80 29 0 08 Jun 2019
Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees Summer Devlin Chandan Singh W. James Murdoch Bin Yu FAtt 62 14 0 18 May 2019