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Marc Schoenauer

8 accepted papers

2026

Benchmarking Multi-Agent Reinforcement Learning in Power Grid Operations

Enrico Marchesini, Eva Boguslawski, Alessandro Leite, Christopher Amato, Matthieu DUSSARTRE, Marc Schoenauer +2
ICLR 2026poster

Improving power grid operations is essential for enhancing flexibility and accelerating grid decarbonization. Reinforcement learning (RL) has shown promise in this domain, most notably through the Learning to Run a Power Network competitions, but prior work has primarily focused on single-agent sett…

Cited by 0Source ↗Scholar ↗
2026

CausalProfiler: Generating Synthetic Benchmarks for Rigorous and Transparent Evaluation of Causal Machine Learning

Panayiotis Panayiotou, Audrey Poinsot, Alessandro Leite, Nicolas CHESNEAU, Marc Schoenauer, Özgür Şimşek
ICML 2026poster

Causal machine learning (Causal ML) aims to answer "what if" questions using machine learning algorithms, making it a promising tool for high-stakes decision-making. Yet, empirical evaluation practices in Causal ML remain limited. Existing benchmarks often rely on a handful of hand-crafted or semi-s…

Cited by 0Source ↗Scholar ↗
2025

From Bytes to Ideas: Language Modeling with Autoregressive U-Nets

Mathurin VIDEAU, Badr Youbi Idrissi, Alessandro Leite, Marc Schoenauer, Olivier Teytaud, David Lopez-Paz
NeurIPS 2025poster

Tokenization imposes a fixed granularity on the input text, freezing how a language model operates on data and how far in the future it predicts. Byte Pair Encoding (BPE) and similar schemes split text once, build a static vocabulary, and leave the model stuck with that choice. We relax this rigidit…

Cited by 0Source ↗code ↗Scholar ↗
2025

Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption

Audrey Poinsot, Panayiotis Panayiotou, Alessandro Leite, Nicolas CHESNEAU, Özgür Şimşek, Marc Schoenauer
ICML 2025poster

Causal machine learning has the potential to revolutionize decision-making by combining the predictive power of machine learning algorithms with the theory of causal inference. However, these methods remain underutilized by the broader machine learning community, in part because current empirical ev…

Cited by 0Source ↗PDF ↗Scholar ↗
2024

Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challenges

Audrey Poinsot, Alessandro Leite, Nicolas Chesneau, Michele Sebag, Marc Schoenauer
IJCAI 2024poster

This paper provides a comprehensive review of deep structural causal models (DSCMs), particularly focusing on their ability to answer counterfactual queries using observational data within known causal structures. It delves into the characteristics of DSCMs by analyzing the hypotheses, guarantees, a…

Cited by 4Source ↗PDF ↗Scholar ↗
2022

Learning meta-features for AutoML

Herilalaina Rakotoarison, Louisot Milijaona, Andry RASOANAIVO, Michele Sebag, Marc Schoenauer
ICLR 2022spotlight

This paper tackles the AutoML problem, aimed to automatically select an ML algorithm and its hyper-parameter configuration most appropriate to the dataset at hand. The proposed approach, MetaBu, learns new meta-features via an Optimal Transport procedure, aligning the manually designed \mf s with th…

Cited by 24Source ↗PDF ↗code ↗Scholar ↗
2020

Deep Statistical Solvers

Balthazar Donon, Zhengying Liu, Wenzhuo LIU, Isabelle Guyon, Antoine Marot, Marc Schoenauer
NeurIPS 2020poster

This paper introduces Deep Statistical Solvers (DSS), a new class of trainable solvers for optimization problems, arising e.g., from system simulations. The key idea is to learn a solver that generalizes to a given distribution of problem instances. This is achieved by directly using as loss the obj…

Cited by 27Source ↗PDF ↗code ↗Scholar ↗
2019

Multi-Domain Adversarial Learning

Alice Schoenauer-Sebag, Louise Heinrich, Marc Schoenauer, Michele Sebag, Lani F. Wu, Steve J. Altschuler
ICLR 2019poster

Multi-domain learning (MDL) aims at obtaining a model with minimal average risk across multiple domains. Our empirical motivation is automated microscopy data, where cultured cells are imaged after being exposed to known and unknown chemical perturbations, and each dataset displays significant exper…

Cited by 87Source ↗PDF ↗code ↗Scholar ↗