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Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration
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Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration

Julian Rodemann
Federico Croppi
Philipp Arens
Yusuf Sale
J. Herbinger
B. Bischl
Eyke Hüllermeier
Thomas Augustin
Conor J. Walsh
Giuseppe Casalicchio
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Papers citing "Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration"

13 / 13 papers shown
Title
Hyperparameter Importance Analysis for Multi-Objective AutoML
Hyperparameter Importance Analysis for Multi-Objective AutoML
Daphne Theodorakopoulos
Frederic Stahl
Marius Lindauer
111
3
0
03 Jan 2025
Explainable Bayesian Optimization
Explainable Bayesian Optimization
Tanmay Chakraborty
Christin Seifert
Christian Wirth
120
6
0
24 Jan 2024
Looping in the Human Collaborative and Explainable Bayesian Optimization
Looping in the Human Collaborative and Explainable Bayesian Optimization
Masaki Adachi
Brady Planden
David A. Howey
Michael A. Osborne
Sebastian Orbell
Natalia Ares
Krikamol Maundet
Siu Lun Chau
64
14
0
26 Oct 2023
Explaining Reinforcement Learning with Shapley Values
Explaining Reinforcement Learning with Shapley Values
Daniel Beechey
Thomas M. S. Smith
Özgür Simsek
TDIFAtt
47
18
0
09 Jun 2023
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Rafael Rafailov
Archit Sharma
E. Mitchell
Stefano Ermon
Christopher D. Manning
Chelsea Finn
ALM
387
4,125
0
29 May 2023
The Utility of Explainable AI in Ad Hoc Human-Machine Teaming
The Utility of Explainable AI in Ad Hoc Human-Machine Teaming
Rohan R. Paleja
Muyleng Ghuy
Nadun R. Arachchige
Reed Jensen
Matthew C. Gombolay
112
65
0
08 Sep 2022
Statistical Comparisons of Classifiers by Generalized Stochastic Dominance
Statistical Comparisons of Classifiers by Generalized Stochastic Dominance
Christoph Jansen
Malte Nalenz
G. Schollmeyer
Thomas Augustin
55
15
0
05 Sep 2022
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges
Christoph Molnar
Giuseppe Casalicchio
B. Bischl
AI4TSAI4CE
87
403
0
19 Oct 2020
Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods
Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods
Eyke Hüllermeier
Willem Waegeman
PERUD
244
1,421
0
21 Oct 2019
A Unified Approach to Interpreting Model Predictions
A Unified Approach to Interpreting Model Predictions
Scott M. Lundberg
Su-In Lee
FAtt
1.1K
22,002
0
22 May 2017
Bayesian optimization for materials design
Bayesian optimization for materials design
P. Frazier
Jialei Wang
AI4CE
56
228
0
03 Jun 2015
Practical Bayesian Optimization of Machine Learning Algorithms
Practical Bayesian Optimization of Machine Learning Algorithms
Jasper Snoek
Hugo Larochelle
Ryan P. Adams
359
7,954
0
13 Jun 2012
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design
Niranjan Srinivas
Andreas Krause
Sham Kakade
Matthias Seeger
149
1,623
0
21 Dec 2009