People
Amir-Hossein Karimi
Max Planck Institute for Intelligent Systems
E: amirhkarimi@gmail.com
T: @amirhkarimi_
About me
Amir-Hossein Karimi is a PhD student at the Max Planck ETH Center for Learning Systems, supervised by Prof. Dr. Schölkopf and Prof. Dr. Valera. His work focuses on the intersection of causal and explainable machine learning, primarily on the problem of algorithmic recourse, that is, how to help individuals subject to automated algorithmic systems overcome unfavorable predictions. Amir is supported through the generous support of NSERC, CLS, and Google PhD Fellowships.
Publications
A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential Recommendations Journal Article
In: ACM Comput. Surv., vol. 55, no. 5, pp. 95:1–95:29, 2023.
Robustness Implies Fairness in Causal Algorithmic Recourse Journal Article
In: CoRR, vol. abs/2302.03465, 2023.
A Persian Benchmark for Joint Intent Detection and Slot Filling Journal Article
In: CoRR, vol. abs/2303.00408, 2023.
Prospects and challenges of cancer systems medicine: from genes to disease networks Journal Article
In: Briefings Bioinform., vol. 23, no. 1, 2022.
In: Comput. Methods Programs Biomed., vol. 213, pp. 106524, 2022.
On the Relationship Between Explanation and Prediction: A Causal View Journal Article
In: CoRR, vol. abs/2212.06925, 2022.
On the Fairness of Causal Algorithmic Recourse Proceedings Article
In: Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022, pp. 9584–9594, AAAI Press, 2022.
Statistical Learning in Wasserstein Space Journal Article
In: IEEE Control. Syst. Lett., vol. 5, no. 3, pp. 899–904, 2021.
Scaling Guarantees for Nearest Counterfactual Explanations Proceedings Article
In: Fourcade, Marion; Kuipers, Benjamin; Lazar, Seth; Mulligan, Deirdre K. (Ed.): AIES '21: AAAI/ACM Conference on AI, Ethics, and Society, Virtual Event, USA, May 19-21, 2021, pp. 177–187, ACM, 2021.
Algorithmic Recourse: from Counterfactual Explanations to Interventions Proceedings Article
In: Elish, Madeleine Clare; Isaac, William; Zemel, Richard S. (Ed.): FAccT '21: 2021 ACM Conference on Fairness, Accountability, and Transparency, Virtual Event / Toronto, Canada, March 3-10, 2021, pp. 353–362, ACM, 2021.
Regression analysis of distributional data through Multi-Marginal Optimal transport Journal Article
In: CoRR, vol. abs/2106.15031, 2021.
On the Adversarial Robustness of Causal Algorithmic Recourse Journal Article
In: CoRR, vol. abs/2112.11313, 2021.
The Challenge of Small Data: Dynamic Mode Decomposition, Redux Journal Article
In: CoRR, vol. abs/2104.04005, 2021.
Model-Agnostic Counterfactual Explanations for Consequential Decisions Proceedings Article
In: Chiappa, Silvia; Calandra, Roberto (Ed.): The 23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020, 26-28 August 2020, Online [Palermo, Sicily, Italy], pp. 895–905, PMLR, 2020.
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach Proceedings Article
In: Larochelle, Hugo; Ranzato, Marc'Aurelio; Hadsell, Raia; Balcan, Maria-Florina; Lin, Hsuan-Tien (Ed.): Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020.
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects Journal Article
In: CoRR, vol. abs/2010.04050, 2020.
Data-Driven Approximation of the Perron-Frobenius Operator Using the Wasserstein Metric Journal Article
In: CoRR, vol. abs/2011.00759, 2020.
Towards Causal Algorithmic Recourse Proceedings Article
In: Holzinger, Andreas; Goebel, Randy; Fong, Ruth; Moon, Taesup; Müller, Klaus-Robert; Samek, Wojciech (Ed.): xxAI - Beyond Explainable AI - International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers, pp. 139–166, Springer, 2020.
Distance Correlation Autoencoder Proceedings Article
In: 2018 International Joint Conference on Neural Networks, IJCNN 2018, Rio de Janeiro, Brazil, July 8-13, 2018, pp. 1–8, IEEE, 2018.
SRP: Efficient class-aware embedding learning for large-scale data via supervised random projections Journal Article
In: CoRR, vol. abs/1811.03166, 2018.