Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines

14 October 2020

Papers citing "Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines"

20 / 20 papers shown

Title
Principles and Practice of Explainable Machine Learning Vaishak Belle I. Papantonis FaML 50 446 0 18 Sep 2020
Looking Deeper into Tabular LIME Damien Garreau U. V. Luxburg FAtt LMTD 155 30 0 25 Aug 2020
Closed-Form Expressions for Global and Local Interpretation of Tsetlin Machines with Applications to Explaining High-Dimensional Data Christopher D. Blakely Ole-Christoffer Granmo 37 16 0 27 Jul 2020
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey Arun Das P. Rad XAI 152 602 0 16 Jun 2020
How Interpretable and Trustworthy are GAMs? C. Chang S. Tan Benjamin J. Lengerich Anna Goldenberg R. Caruana FAtt 103 79 0 11 Jun 2020
Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps Tobias Huber Katharina Weitz Elisabeth André Ofra Amir FAtt 51 67 0 18 May 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 95 377 0 30 Apr 2020
Neural Additive Models: Interpretable Machine Learning with Neural Nets Rishabh Agarwal Levi Melnick Nicholas Frosst Xuezhou Zhang Ben Lengerich R. Caruana Geoffrey E. Hinton 79 417 0 29 Apr 2020
SurvLIME: A method for explaining machine learning survival models M. Kovalev Lev V. Utkin E. Kasimov 243 90 0 18 Mar 2020
GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions Zebin Yang Aijun Zhang Agus Sudjianto FAtt 141 128 0 16 Mar 2020
GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks Q. Huang M. Yamada Yuan Tian Dinesh Singh Dawei Yin Yi-Ju Chang FAtt 75 354 0 17 Jan 2020
Explaining the Explainer: A First Theoretical Analysis of LIME Damien Garreau U. V. Luxburg FAtt 45 178 0 10 Jan 2020
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 116 6,254 0 22 Oct 2019
ALIME: Autoencoder Based Approach for Local Interpretability Sharath M. Shankaranarayana D. Runje FAtt 46 103 0 04 Sep 2019
RISE: Randomized Input Sampling for Explanation of Black-box Models Vitali Petsiuk Abir Das Kate Saenko FAtt 181 1,170 0 19 Jun 2018
Multi-Layered Gradient Boosting Decision Trees Ji Feng Yang Yu Zhi Zhou AI4CE 176 120 0 31 May 2018
Global Model Interpretation via Recursive Partitioning Chengliang Yang Anand Rangarajan Sanjay Ranka FAtt 40 80 0 11 Feb 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 124 3,957 0 06 Feb 2018
Interpretable Explanations of Black Boxes by Meaningful Perturbation Ruth C. Fong Andrea Vedaldi FAtt AAML 74 1,519 0 11 Apr 2017
A Random Forest Guided Tour Gérard Biau Erwan Scornet AI4TS 221 2,807 0 18 Nov 2015