Chèrex créaturex. Retours d’expériences sur un enseignement féministe queer dans une école polytechnique

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Model-free Reinforcement Learning (RL) generally suffers from poor sample complexity, mostly due to the need to exhaustively explore the state-action space to find well-performing policies. On the other hand, we postulate that expert knowledge of the syste ...

Machine learning is often cited as a new paradigm in control theory, but is also often viewed as empirical and less intuitive for students than classical model-based methods. This is particularly the case for reinforcement learning, an approach that does n ...

Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language proc ...

The analysis of motor evoked potentials (MEPs) generated by transcranial magnetic stimulation (TMS) is crucial in research and clinical medical practice. MEPs are characterized by their latency and the treatment of a single patient may require the characte ...

Author summaryIn recent years, the application of deep learning represented a breakthrough in the mass spectrometry (MS) field by improving the assignment of the correct sequence of amino acids from observable MS spectra without prior knowledge, also known ...

In data-parallel optimization of machine learning models, workers collaborate to improve their estimates of the model: more accurate gradients allow them to use larger learning rates and optimize faster. In the decentralized setting, in which workers commu ...

This manuscript offers the perspective of experimentalists on a number of modern data-driven techniques: model predictive control relying on Gaussian processes, adaptive data-driven control based on behavioral theory, and deep reinforcement learning. These ...

We develop a principled approach to end-to-end learning in stochastic optimization. First, we show that the standard end-to-end learning algorithm admits a Bayesian interpretation and trains a posterior Bayes action map. Building on the insights of this an ...

We propose ordering-based approaches for learning the maximal ancestral graph (MAG) of a structural equation model (SEM) up to its Markov equivalence class (MEC) in the presence of unobserved variables. Existing ordering-based methods in the literature rec ...

Chèrex créaturex. Retours d’expériences sur un enseignement féministe queer dans une école polytechnique

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Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules

Reinforcement learning approach to control an inverted pendulum: A general framework for educational purposes

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

DELMEP: a deep learning algorithm for automated annotation of motor evoked potential latencies

Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics

Beyond Spectral Gap: The Role of the Topology in Decentralized Learning

Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning

End-to-End Learning for Stochastic Optimization: A Bayesian Perspective

Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables