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Algorithmic recourse seeks to provide actionable recommendations for individuals to overcome unfavorable classification outcomes from automated decision-making systems.
Predicting recidivism in north carolina, 1978 and 1980
Schmidt, P. and Witte, A. D · 1988
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Uci adult data set
Kohavi, R. and Becker, B · 1996
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Statistical modeling: The two cultures
Breiman, L · 2001
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Convex optimization
Boyd, S. and Vandenberghe, L · 2004
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Varieties of causal intervention
Korb, K. B., Hope, L. R., Nicholson, A. E., and Axnick, K · 2004
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Interventions and causal inference
Eberhardt, F. and Scheines, R · 2007
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Robust optimization
Ben-Tal, A., El Ghaoui, L., and Nemirovski, A · 2009
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Causality
Pearl, J · 2009
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Dataset shift in machine learning
Quiñonero-Candela, J., Sugiyama, M., Lawrence, N. D., and Schwaighofer, A · 2009
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Robustness and regularization of support vector machines
Xu, H., Caramanis, C., and Mannor, S · 2009
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A unifying view on dataset shift in classification
Moreno-Torres, J. G., Raeder, T., Alaiz-Rodríguez, R., Chawla, N. V., and Herrera, F · 2012
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Algorithmic recourse in the wild: Understanding the impact of data and model shifts
Rawal, K., Kamar, E., and Lakkaraju, H · 2012
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Can i still trust you?: Understanding the impact of distribution shifts on algorithmic recourses
Rawal, K., Kamar, E., and Lakkaraju, H · 2012
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Domain generalization via invariant feature representation
Muandet, K., Balduzzi, D., and Schölkopf, B · 2013
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Robustness of classifiers: from adversarial to random noise
Fawzi, A., Moosavi-Dezfooli, S.-M., and Frossard, P · 2016
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How we analyzed the compas recidivism algorithm
Larson, J., Mattu, S., Kirchner, L., and Angwin, J · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
Moosavi-Dezfooli, S.-M., Fawzi, A., and Frossard, P · 2016
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Improving the robustness of deep neural networks via stability training
Zheng, S., Song, Y., Leung, T., and Goodfellow, I · 2016
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Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2017
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Shortcut learning in deep neural networks
Geirhos, R., Jacobsen, J.-H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., and Wichmann, F. A · 2020
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Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
Karimi, A.-H., von Kügelgen, J., Schölkopf, B., and Valera, I · 2020
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On counterfactual explanations under predictive multiplicity
Pawelczyk, M., Broelemann, K., and Kasneci, G · 2020
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Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation
Powers, D. M · 2020
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The philosophical basis of algorithmic recourse
Venkatasubramanian, S. and Alfano, M · 2020
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Evaluating robustness of counterfactual explanations
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Counterfactual explanations without opening the black box: Automated decisions and the gdpr
Wachter, S., Mittelstadt, B., and Russell, C · 2017
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
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Fair inference on outcomes
Nabi, R. and Shpitser, I · 2018
Cited alongside, same era.
Robust classification
Bertsimas, D., Dunn, J., Pawlowski, C., and Zhuo, Y. D · 2019
Cited alongside, same era.
South german credit data: Correcting a widely used data set
Groemping, U · 2019
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Equalizing recourse across groups
Gupta, V., Nokhiz, P., Roy, C. D., and Venkatasubramanian, S · 2019
Cited alongside, same era.
Artelt, A., Vaquet, V., Velioglu, R., Hinder, F., Brinkrolf, J., Schilling, M., and Hammer, B · 2021
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Algorithmic recourse: from counterfactual explanations to interventions
Karimi, A.-H., Schölkopf, B., and Valera, I · 2021
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Learning models for actionable recourse
Ross, A., Lakkaraju, H., and Bastani, O · 2021
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Counterfactual explanations can be manipulated
Slack, D., Hilgard, S., Lakkaraju, H., and Singh, S · 2021
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Towards robust and reliable algorithmic recourse
Upadhyay, S., Joshi, S., and Lakkaraju, H · 2021
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Consistent counterfactuals for deep models
Black, E., Wang, Z., Fredrikson, M., and Datta, A · 2022
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Counterfactual plans under distributional ambiguity
Bui, N., Nguyen, D., and Nguyen, V. A · 2022
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Rocoursenet: Distributionally robust training of a prediction aware recourse model
Guo, H., Jia, F., Chen, J., Squicciarini, A., and Yadav, A · 2022
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A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
Karimi, A.-H., Barthe, G., Schölkopf, B., and Valera, I · 2022
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Algorithmic recourse in the face of noisy human responses
Pawelczyk, M., Datta, T., van-den Heuvel, J., Kasneci, G., and Lakkaraju, H · 2022
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On the robustness of counterfactual explanations to adverse perturbations
Virgolin, M. and Fracaros, S · 2022
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On the fairness of causal algorithmic recourse
von Kügelgen, J., Karimi, A.-H., Bhatt, U., Valera, I., Weller, A., and Schölkopf, B · 2022
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