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
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 Inproceedings
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.
The Challenge of Small Data: Dynamic Mode Decomposition, Redux Inproceedings
In: 60th IEEE Conference on Decision and Control, CDC 2021, Austin, TX, USA, December 14-17, 2021, pp. 2276–2281, IEEE, 2021.
Algorithmic Recourse: from Counterfactual Explanations to Interventions Inproceedings
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.
Model-Agnostic Counterfactual Explanations for Consequential Decisions Inproceedings
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 Inproceedings
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.
Algorithmic Recourse: from Counterfactual Explanations to Interventions Journal Article
In: CoRR, vol. abs/2002.06278, 2020.
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach Journal Article
In: CoRR, vol. abs/2006.06831, 2020.
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects Journal Article
In: CoRR, vol. abs/2010.04050, 2020.
Scaling Guarantees for Nearest Counterfactual Explanations Journal Article
In: CoRR, vol. abs/2010.04965, 2020.
On the Fairness of Causal Algorithmic Recourse Journal Article
In: CoRR, vol. abs/2010.06529, 2020.
Data-Driven Approximation of the Perron-Frobenius Operator Using the Wasserstein Metric Journal Article
In: CoRR, vol. abs/2011.00759, 2020.
Model-Agnostic Counterfactual Explanations for Consequential Decisions Journal Article
In: CoRR, vol. abs/1905.11190, 2019.
Distance Correlation Autoencoder Inproceedings
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.
Deep Variational Sufficient Dimensionality Reduction Journal Article
In: CoRR, vol. abs/1812.07641, 2018.
Discovery Radiomics via a Mixture of Deep ConvNet Sequencers for Multi-parametric MRI Prostate Cancer Classification Inproceedings
In: Karray, Fakhri; Campilho, Aurélio; Cheriet, Farida (Ed.): Image Analysis and Recognition - 14th International Conference, ICIAR 2017, Montreal, QC, Canada, July 5-7, 2017, Proceedings, pp. 45–53, Springer, 2017.
Synthesizing Deep Neural Network Architectures using Biological Synaptic Strength Distributions Journal Article
In: CoRR, vol. abs/1707.00081, 2017.