Fetching the paper…
Reading the bibliography…
A multitude of classifiers can be trained on the same data to achieve similar performances during test time, while having learned significantly different classification patterns.
A coefficient of agreement for nominal scales
Jacob Cohen · 1960
Earlier work this paper cites.
On estimation of a probability density function and mode
Emanuel Parzen · 1962
Earlier work this paper cites.
A general coefficient of similarity and some of its properties
John C Gower · 1971
Earlier work this paper cites.
A stepwise discriminant analysis program using density estimation
JDF Habbema, J. Hermans, and K. Van den Broek · 1974
Earlier work this paper cites.
Hedonic prices and the demand for clean air
David Harrison and Daniel Rubinfeld · 1978
Earlier work this paper cites.
Classification and regression trees, 1984
L Breiman, JH Friedman, R Olshen, and CJ Stone · 1984
Earlier work this paper cites.
The multi-purpose incremental learning system aq15 and its testing application to three medical domains
R.S. Michalski, I. Mozetic, and N. Lavrac · 1986
Earlier work this paper cites.
Query by committee
H. S. Seung, M. Opper, and H. Sompolinsky · 1992
Earlier work this paper cites.
Gaussian mixture models
Douglas A Reynolds et al · 2009
Earlier work this paper cites.
Fast and robust earth mover’s distances
Ofir Pele and Michael Werman · 2009
Earlier work this paper cites.
Openml: Networked science in machine learning
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, and Luis Torgo · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
A proactive intelligent decision support system for predicting the popularity of online news
K. Fernandes, P. Vinagre, and P. Cortez · 2015
Earlier work this paper cites.
" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Earlier work this paper cites.
beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2016
Cited alongside, same era.
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
Cited alongside, same era.
Counterfactual explanations without opening the black box: Automated decisions and the gdpr
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2017
Cited alongside, same era.
Controversy rules-discovering regions where classifiers (dis-) agree exceptionally
Oren Zeev-Ben-Mordehai, Wouter Duivesteijn, and Mykola Pechenizkiy · 2018
Cited alongside, same era.
A survey of methods for explaining black box models
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi · 2018
Cited alongside, same era.
Fairwashing explanations with off-manifold detergent
Christopher J Anders, Plamen Pasliev, Ann-Kathrin Dombrowski, Klaus-Robert Müller, and Pan Kessel · 2020
Later among the works it cites.
Fooling lime and shap: Adversarial attacks on post hoc explanation methods
Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, and Himabindu Lakkaraju · 2020
Later among the works it cites.
On counterfactual explanations under predictive multiplicity
Martin Pawelczyk, Klaus Broelemann, and Gjergji Kasneci · 2020
Later among the works it cites.
The hidden assumptions behind counterfactual explanations and principal reasons
Solon Barocas, Andrew D. Selbst, and Manish Raghavan · 2020
Later among the works it cites.
Exploring the cloud of variable importance for the set of all good models
Jiayun Dong and Cynthia Rudin · 2020
Later among the works it cites.
Predictive multiplicity in classification
Charles Marx, Flavio Calmon, and Berk Ustun · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Anchors: High-precision model-agnostic explanations
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
Cited alongside, same era.
Comparison-based inverse classification for interpretability in machine learning
Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki · 2018
Cited alongside, same era.
Fairwashing: the risk of rationalization
Ulrich Aivodji, Hiromi Arai, Olivier Fortineau, Sébastien Gambs, Satoshi Hara, and Alain Tapp · 2019
Cited alongside, same era.
Lesia Semenova, Cynthia Rudin, and Ronald Parr · 2019
Cited alongside, same era.
Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Frank Hutter, Michel Lang, Rafael G. Mantovani, Jan N. van Rijn, and Joaquin Vanschoren · 2019
Cited alongside, same era.
Explanations can be manipulated and geometry is to blame
Ann-Kathrin Dombrowski, Maximillian Alber, Christopher Anders, Marcel Ackermann, Klaus-Robert Müller, and Pan Kessel · 2019
Cited alongside, same era.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
Cited alongside, same era.
Later among the works it cites.
Beyond accuracy: quantifying trial-by-trial behaviour of cnns and humans by measuring error consistency
Robert Geirhos, Kristof Meding, and Felix A Wichmann · 2020
Later among the works it cites.
Underspecification presents challenges for credibility in modern machine learning
Alexander D’Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D Hoffman, et al · 2020
Later among the works it cites.
Can i still trust you?: Understanding the impact of distribution shifts on algorithmic recourses
Kaivalya Rawal, Ece Kamar, and Himabindu Lakkaraju · 2020
Later among the works it cites.
Problems with shapley-value-based explanations as feature importance measures
I Elizabeth Kumar, Suresh Venkatasubramanian, Carlos Scheidegger, and Sorelle Friedler · 2020
Later among the works it cites.
Explaining machine learning classifiers through diverse counterfactual explanations
Ramaravind K Mothilal, Amit Sharma, and Chenhao Tan · 2020
Later among the works it cites.
Autogluon-tabular: Robust and accurate automl for structured data
Nick Erickson, Jonas Mueller, Alexander Shirkov, Hang Zhang, Pedro Larroy, Mu Li, and Alexander Smola · 2020
Later among the works it cites.
When mitigating bias is unfair: A comprehensive study on the impact of bias mitigation algorithms
Natasa Krco, Thibault Laugel, Jean-Michel Loubes, and Marcin Detyniecki · 2023
Closest in time.