2 citations · 9 across the 46 of their papers we have counts for
19 papers · 1 filter
Refined Analysis of Federated Averaging and Federated Richardson-Romberg
Paul Mangold, Alain Durmus, Aymeric Dieuleveut +2
In this paper, we present a novel analysis of \FedAvg with constant step size, relying on the Markov property of the underlying process. We demonstrate that the global iterates of…
Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents
Safwan Labbi, Daniil Tiapkin, Lorenzo Mancini +2
In this paper, we present the Federated Upper Confidence Bound Value Iteration algorithm (), a novel extension of the algorithm (Azar et al., 2…
Optimal Design for Reward Modeling in RLHF
Antoine Scheid, Etienne Boursier, Alain Durmus +4
Reinforcement Learning from Human Feedback (RLHF) has become a popular approach to align language models (LMs) with human preferences. This method involves collecting a large datas…
Joint Channel Selection using FedDRL in V2X
Lorenzo Mancini, Safwan Labbi, Karim Abed Meraim +4
Vehicle-to-everything (V2X) communication technology is revolutionizing transportation by enabling interactions between vehicles, devices, and infrastructures. This connectivity en…
Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson-Romberg Extrapolation
Marina Sheshukova, Denis Belomestny, Alain Durmus +3
We address the problem of solving strongly convex and smooth minimization problems using stochastic gradient descent (SGD) algorithm with a constant step size. Previous works sugge…
A New Bound on the Cumulant Generating Function of Dirichlet Processes
Pierre Perrault, Denis Belomestny, Pierre Ménard +4
In this paper, we introduce a novel approach for bounding the cumulant generating function (CGF) of a Dirichlet process (DP) , using superadditivity. In par…
Atlas-Chat: Adapting Large Language Models for Low-Resource Moroccan Arabic Dialect
Guokan Shang, Hadi Abdine, Yousef Khoubrane +9
We introduce Atlas-Chat, the first-ever collection of LLMs specifically developed for dialectal Arabic. Focusing on Moroccan Arabic, also known as Darija, we construct our instruct…
Unravelling in Collaborative Learning
Aymeric Capitaine, Etienne Boursier, Antoine Scheid +4
Collaborative learning offers a promising avenue for leveraging decentralized data. However, collaboration in groups of strategic learners is not a given. In this work, we consider…
Probabilistic Conformal Prediction with Approximate Conditional Validity
Vincent Plassier, Alexander Fishkov, Mohsen Guizani +2
We develop a new method for generating prediction sets that combines the flexibility of conformal methods with an estimate of the conditional distribution . Existing…
Central Limit Theorem for Bayesian Neural Network trained with Variational Inference
Arnaud Descours, Tom Huix, Arnaud Guillin +3
In this paper, we rigorously derive Central Limit Theorems (CLT) for Bayesian two-layerneural networks in the infinite-width limit and trained by variational inference on a regress…
Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians
Tom Huix, Anna Korba, Alain Durmus +1
Variational inference (VI) is a popular approach in Bayesian inference, that looks for the best approximation of the posterior distribution within a parametric family, minimizing a…
Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationality
Antoine Scheid, Aymeric Capitaine, Etienne Boursier +3
In economic theory, the concept of externality refers to any indirect effect resulting from an interaction between players that affects the social welfare. Most of the models withi…
ReALLM: A general framework for LLM compression and fine-tuning
Louis Leconte, Lisa Bedin, Van Minh Nguyen +1
We introduce ReALLM, a novel approach for compression and memory-efficient adaptation of pre-trained language models that encompasses most of the post-training quantization and fin…
Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning
Sergey Samsonov, Eric Moulines, Qi-Man Shao +2
In this paper, we obtain the Berry-Esseen bound for multivariate normal approximation for the Polyak-Ruppert averaged iterates of the linear stochastic approximation (LSA) algorith…
Queuing dynamics of asynchronous Federated Learning
Louis Leconte, Matthieu Jonckheere, Sergey Samsonov +1
We study asynchronous federated learning mechanisms with nodes having potentially different computational speeds. In such an environment, each node is allowed to work on models wit…
Incentivized Learning in Principal-Agent Bandit Games
Antoine Scheid, Daniil Tiapkin, Etienne Boursier +5
This work considers a repeated principal-agent bandit game, where the principal can only interact with her environment through the agent. The principal and the agent have misaligne…
From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation
Nikita Kotelevskii, Vladimir Kondratyev, Martin Takáč +2
There are various measures of predictive uncertainty in the literature, but their relationships to each other remain unclear. This paper uses a decomposition of statistical pointwi…
SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning
Paul Mangold, Sergey Samsonov, Safwan Labbi +4
In this paper, we analyze the sample and communication complexity of the federated linear stochastic approximation (FedLSA) algorithm. We explicitly quantify the effects of local t…
Smoothness Estimation for Whittle-Matérn Processes on Closed Riemannian Manifolds
Moritz Korte-Stapff, Toni Karvonen, Eric Moulines
The family of Matérn kernels are often used in spatial statistics, function approximation and Gaussian process methods in machine learning. One reason for their popularity is the p…