Papers
Sampling/Optimization over measures

A. Korba, F. Portier. Adaptive Importance Sampling meets Mirror Descent: a Biasvariance tradeoff. In Artificial Intelligence and Statistics (AISTATS), 2022. Accepted for Oral presentation (top 10%).
[paper][poster][slides][video]

A. Korba, P.C. AubinFrankowski, S. Majewski, P. Ablin. Kernel Stein Discrepancy Descent. In International Conference of Machine Learning (ICML) 2021. Accepted for Long Oral presentation (top 15%).
[paper][poster][slides][video]

A. Korba, A. Salim, M. Arbel, G. Luise, A. Gretton. A NonAsymptotic Analysis for Stein Variational Gradient Descent. In Advances in Neural Information Processing Systems (NeurIPS) 2020.
[paper][poster]

A. Salim, A. Korba, G. Luise. Wasserstein Proximal Gradient. In Advances in Neural Information Processing Systems (NeurIPS) 2020.
[paper][poster]

M. Arbel, A. Korba, A. Salim , A.Gretton. Maximum Mean Discrepancy Gradient flow. In Advances in Neural Information Processing Systems (NeurIPS) 2019.
[paper][code]
Causality

A. Mastouri, Y. Zhu, L. Gultchin, A. Korba, R. Silva, M. J. Kusner, A. Gretton, K. Muandet. Proximal Causal Learning with Kernels: TwoStage Estimation and Moment Restriction. In International Conference of Machine Learning (ICML) 2021.
[paper]
Ranking
During my PhD, I studied how to analyze preference data in the form of total orders/permutations or pairwise comparisons, and how to handle statistical problems related to such data, including ranking aggregation, distribution estimation, and prediction.
Here is the final version of the manuscript and the slides; and below a list of publications.
 M. Achab (*), A. Korba (*), S. Clémençon. Dimensionality Reduction for (Bucket) Ranking: A Mass Transportation Approach. In Algorithmic Learning Theory (ALT) 2019. (* :) equal contribution
[paper][code]
 A. Korba, A. Garcia, F. D'AlchéBuc. A Structured Prediction Approach for Label Ranking. In
Advances in Neural Information Processing Systems (NeurIPS) 2018.
[paper][code][poster]
 S. Clémençon, A. Korba. On Aggregation in Ranking Median Regression. In European Symposium on Artificial Neural Networks (ESANN) 2018.
[paper]
 S. Clémençon, A. Korba, E. Sibony. Ranking Median Regression: Learning to Order through Local Consensus.
In Algorithmic Learning Theory (ALT) 2018.
[Arxiv long version ] [ALT short version]
 A. Korba, S. Clémençon, E. Sibony. A Learning Theory of Ranking Aggregation. In Artificial Intelligence and Statistics (AISTATS) 2017.
[paper] [poster]
 Y. Jiao, A. Korba, E. Sibony. Controlling the distance to a Kemeny consensus without computing it. In
International Conference on Machine Learning (ICML) 2016.
[paper] [poster]