Fetching the paper…
Reading the bibliography…
Counterfactual explanations promote explainability in machine learning models by answering the question "how should an input instance be perturbed to obtain a desired predicted label?".
On random graphs i. publicationes mathematicae (debrecen)
P Erdős and A Rényi · 1959
Earlier work this paper cites.
A linear non-gaussian acyclic model for causal discovery
Shohei Shimizu, Patrik O Hoyer, Aapo Hyvärinen, Antti Kerminen, and Michael Jordan · 2006
Earlier work this paper cites.
Rdkit: Open-source cheminformatics
Greg Landrum et al · 2006
Earlier work this paper cites.
Exploring network structure, dynamics, and function using networkx
Aric Hagberg, Pieter Swart, and Daniel S Chult · 2008
Earlier work this paper cites.
Effects of networking on career success: a longitudinal study
Hans-Georg Wolff and Klaus Moser · 2009
Earlier work this paper cites.
Causality
Judea Pearl · 2009
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
Earlier work this paper cites.
Counterfactual explanations without opening the black box: Automated decisions and the gdpr
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2017
Earlier work this paper cites.
Interpretable predictions of tree-based ensembles via actionable feature tweaking
Gabriele Tolomei, Fabrizio Silvestri, Andrew Haines, and Mounia Lalmas · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
Earlier work this paper cites.
Graphvae: Towards generation of small graphs using variational autoencoders
Martin Simonovsky and Nikos Komodakis · 2018
Earlier work this paper cites.
Local rule-based explanations of black box decision systems
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Dino Pedreschi, Franco Turini, and Fosca Giannotti · 2018
Earlier work this paper cites.
Explanations based on the missing: Towards contrastive explanations with pertinent negatives
Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam, and Payel Das · 2018
Earlier work this paper cites.
Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
Earlier work this paper cites.
Graphgan: Graph representation learning with generative adversarial nets
Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, and Minyi Guo · 2018
Earlier work this paper cites.
Graphrnn: Generating realistic graphs with deep auto-regressive models
Jiaxuan You, Rex Ying, Xiang Ren, William Hamilton, and Jure Leskovec · 2018
Cited alongside, same era.
Molgan: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf · 2018
Cited alongside, same era.
Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
Cited alongside, same era.
Fréchet chemnet distance: a metric for generative models for molecules in drug discovery
Kristina Preuer, Philipp Renz, Thomas Unterthiner, Sepp Hochreiter, and Gunter Klambauer · 2018
Cited alongside, same era.
Moleculenet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande · 2018
Cited alongside, same era.
Preserving causal constraints in counterfactual explanations for machine learning classifiers
Variational autoencoders and nonlinear ica: A unifying framework
Ilyes Khemakhem, Diederik Kingma, Ricardo Monti, and Aapo Hyvarinen · 2020
Later among the works it cites.
Learning model-agnostic counterfactual explanations for tabular data
Martin Pawelczyk, Klaus Broelemann, and Gjergji Kasneci · 2020
Later among the works it cites.
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
Amir-Hossein Karimi, Julius Von Kügelgen, Bernhard Schölkopf, and Isabel Valera · 2020
Later among the works it cites.
Causal discovery with general non-linear relationships using non-linear ica
Ricardo Pio Monti, Kun Zhang, and Aapo Hyvärinen · 2020
Later among the works it cites.
Face: Feasible and actionable counterfactual explanations
Rafael Poyiadzi, Kacper Sokol, Raul Santos-Rodriguez, Tijl De Bie, and Peter Flach · 2020
Later among the works it cites.
Explaining machine learning classifiers through diverse counterfactual explanations
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Divyat Mahajan, Chenhao Tan, and Amit Sharma · 2019
Cited alongside, same era.
Gnnexplainer: Generating explanations for graph neural networks
Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec · 2019
Cited alongside, same era.
Nonlinear ica using auxiliary variables and generalized contrastive learning
Aapo Hyvarinen, Hiroaki Sasaki, and Richard Turner · 2019
Cited alongside, same era.
Actionable recourse in linear classification
Berk Ustun, Alexander Spangher, and Yang Liu · 2019
Cited alongside, same era.
Model agnostic contrastive explanations for structured data
Amit Dhurandhar, Tejaswini Pedapati, Avinash Balakrishnan, Pin-Yu Chen, Karthikeyan Shanmugam, and Ruchir Puri · 2019
Cited alongside, same era.
Efficient search for diverse coherent explanations
Chris Russell · 2019
Cited alongside, same era.
Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 2019
Cited alongside, same era.
Ramaravind K Mothilal, Amit Sharma, and Chenhao Tan · 2020
Later among the works it cites.
Multi-objective counterfactual explanations
Susanne Dandl, Christoph Molnar, Martin Binder, and Bernd Bischl · 2020
Later among the works it cites.
Sub-graph contrast for scalable self-supervised graph representation learning
Yizhu Jiao, Yun Xiong, Jiawei Zhang, Yao Zhang, Tianqi Zhang, and Yangyong Zhu · 2020
Later among the works it cites.
Graph representation learning
William L Hamilton · 2020
Later among the works it cites.
A survey on the robustness of feature importance and counterfactual explanations
Saumitra Mishra, Sanghamitra Dutta, Jason Long, and Daniele Magazzeni · 2021
Later among the works it cites.
Meg: Generating molecular counterfactual explanations for deep graph networks
Danilo Numeroso and Davide Bacciu · 2021
Later among the works it cites.
Cf-gnnexplainer: Counterfactual explanations for graph neural networks
Ana Lucic, Maartje ter Hoeve, Gabriele Tolomei, Maarten de Rijke, and Fabrizio Silvestri · 2021
Later among the works it cites.
Robust counterfactual explanations on graph neural networks
Mohit Bajaj, Lingyang Chu, Zi Yu Xue, Jian Pei, Lanjun Wang, Peter Cho-Ho Lam, and Yong Zhang · 2021
Later among the works it cites.
Counterfactual graphs for explainable classification of brain networks
Carlo Abrate and Francesco Bonchi · 2021
Later among the works it cites.
Algorithmic recourse: from counterfactual explanations to interventions
Amir-Hossein Karimi, Bernhard Schölkopf, and Isabel Valera · 2021
Later among the works it cites.
Data augmentation for deep graph learning: A survey
Kaize Ding, Zhe Xu, Hanghang Tong, and Huan Liu · 2022
Closest in time.