Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models

17 July 2023

Papers citing "Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models"

22 / 22 papers shown

Title
Composite Feature Selection using Deep Ensembles F. Imrie Alexander Norcliffe Pietro Lio M. Schaar 69 12 0 01 Nov 2022
AMLB: an AutoML Benchmark Pieter Gijsbers Marcos L. P. Bueno Stefan Coors E. LeDell Sébastien Poirier Janek Thomas B. Bischl Joaquin Vanschoren 63 57 0 25 Jul 2022
Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overview Florian Karl Tobias Pielok Julia Moosbauer Florian Pfisterer Stefan Coors ... Jakob Richter Michel Lang Eduardo C. Garrido-Merchán Juergen Branke B. Bischl AI4CE 61 60 0 15 Jun 2022
Neural Basis Models for Interpretability Filip Radenovic Abhimanyu Dubey D. Mahajan FAtt 97 47 0 27 May 2022
Scalable Interpretability via Polynomials Abhimanyu Dubey Filip Radenovic D. Mahajan 49 31 0 27 May 2022
A survey on multi-objective hyperparameter optimization algorithms for Machine Learning A. Hernández I. Nieuwenhuyse Sebastian Rojas Gonzalez 49 102 0 23 Nov 2021
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization Marius Lindauer Katharina Eggensperger Matthias Feurer André Biedenkapp Difan Deng C. Benjamins Tim Ruhopf René Sass Frank Hutter 121 345 0 20 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 228 493 0 13 Jul 2021
Revisiting Deep Learning Models for Tabular Data Yu. V. Gorishniy Ivan Rubachev Valentin Khrulkov Artem Babenko LMTD 119 760 0 22 Jun 2021
Well-tuned Simple Nets Excel on Tabular Datasets Arlind Kadra Marius Lindauer Frank Hutter Josif Grabocka 46 197 0 21 Jun 2021
Tabular Data: Deep Learning is Not All You Need Ravid Shwartz-Ziv Amitai Armon LMTD 159 1,269 0 06 Jun 2021
NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning C. Chang R. Caruana Anna Goldenberg AI4CE 81 80 0 03 Jun 2021
GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions Zebin Yang Aijun Zhang Agus Sudjianto FAtt 150 128 0 16 Mar 2020
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data Nick Erickson Jonas W. Mueller Alexander Shirkov Hang Zhang Pedro Larroy Mu Li Alex Smola LMTD 210 625 0 13 Mar 2020
Testing Monotonicity of Machine Learning Models Arnab Sharma Heike Wehrheim 57 9 0 27 Feb 2020
InterpretML: A Unified Framework for Machine Learning Interpretability Harsha Nori Samuel Jenkins Paul Koch R. Caruana AI4CE 153 487 0 19 Sep 2019
Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability Christoph Molnar Giuseppe Casalicchio B. Bischl FAtt 44 60 0 08 Apr 2019
A Simple and Effective Model-Based Variable Importance Measure Brandon M. Greenwell Bradley C. Boehmke Andrew J. McCarthy FAtt TDI 40 229 0 12 May 2018
Tunability: Importance of Hyperparameters of Machine Learning Algorithms Philipp Probst B. Bischl A. Boulesteix 60 616 0 26 Feb 2018
Hyperparameter Importance Across Datasets J. N. van Rijn Frank Hutter 43 240 0 12 Oct 2017
OpenML Benchmarking Suites B. Bischl Giuseppe Casalicchio Matthias Feurer Pieter Gijsbers Frank Hutter Michel Lang R. G. Mantovani Jan N. van Rijn Joaquin Vanschoren VLM ELM 89 162 0 11 Aug 2017
XGBoost: A Scalable Tree Boosting System Tianqi Chen Carlos Guestrin 809 38,961 0 09 Mar 2016