Martin Bompaire

According to our database, Martin Bompaire authored at least 10 papers between 2017 and 2022.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2022

Offline Evaluation of Reward-Optimizing Recommender Systems: The Case of Simulation.

Imad Aouali, Amine Benhalloum, Martin Bompaire, Benjamin Heymann, Olivier Jeunen, David Rohde, Otmane Sakhi, Flavian Vasile

CoRR, 2022

Reward Optimizing Recommendation using Deep Learning and Fast Maximum Inner Product Search.

Imad Aouali, Amine Benhalloum, Martin Bompaire, Achraf Ait Sidi Hammou, Sergey Ivanov, Benjamin Heymann, David Rohde, Otmane Sakhi, Flavian Vasile, Maxime Vono

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

2021

Causal Models for Real Time Bidding with Repeated User Interactions.

Martin Bompaire, Alexandre Gilotte, Benjamin Heymann

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

2020

Sparse and low-rank multivariate Hawkes processes.

Emmanuel Bacry, Martin Bompaire, Stéphane Gaïffas, Jean-François Muzy

J. Mach. Learn. Res., 2020

Bidding Through the Lens of Attribution: Pick the Right Labels!

Martin Bompaire, Antoine Désir, Benjamin Heymann

CoRR, 2020

Joint Policy-Value Learning for Recommendation.

Olivier Jeunen, David Rohde, Flavian Vasile, Martin Bompaire

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

2019

Machine learning based on Hawkes processes and stochastic optimization. (Apprentissage automatique avec les processus de Hawkes et l'optimisation stochastique).

Martin Bompaire

PhD thesis, 2019

Learning from Bandit Feedback: An Overview of the State-of-the-art.

Olivier Jeunen, Dmytro Mykhaylov, David Rohde, Flavian Vasile, Alexandre Gilotte, Martin Bompaire

CoRR, 2019

2018

Dual optimization for convex constrained objectives without the gradient-Lipschitz assumption.

Martin Bompaire, Emmanuel Bacry, Stéphane Gaïffas

CoRR, 2018

2017

tick: a Python Library for Statistical Learning, with an emphasis on Hawkes Processes and Time-Dependent Models.

Emmanuel Bacry, Martin Bompaire, Philip Deegan, Stéphane Gaïffas, Søren Poulsen

J. Mach. Learn. Res., 2017