1 citations · 1 across the 9 of their papers we have counts for
9 papers
Self-Consistency via Marginal Sharpening
Aleksei Arzhantsev, Otmane Sakhi, Nicolas Chopin
Inference-time sampling can elicit strong reasoning abilities from language models without additional training. Existing power-sampling methods do so by sharpening the distribution…
A forward-only scheme for online learning of proposal distributions in particle filters
Sylvain Procope-Mamert, Nicolas Chopin, Maud Delattre +1
We introduce a new online approach for constructing proposal distributions in particle filters using a forward scheme. Our method progressively incorporates future observations to…
Extrapolation of Tempered Posteriors
Mengxin Xi, Zheyang Shen, Marina Riabiz +2
Tempering is a popular tool in Bayesian computation, being used to transform a posterior distribution into a reference distribution that is more easily approximated. Se…
Least squares variational inference
Yvann Le Fay, Nicolas Chopin, Simon Barthelmé
Variational inference consists in finding the best approximation of a target distribution within a certain family, where `best' means (typically) smallest Kullback-Leiber divergenc…
Saddlepoint Monte Carlo and its Application to Exact Ecological Inference
Théo Voldoire, Nicolas Chopin, Guillaume Rateau +1
Assuming X is a random vector and A a non-invertible matrix, one sometimes need to perform inference while only having access to samples of Y = AX. The corresponding likelihood is…
Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and Learning
Otmane Sakhi, Imad Aouali, Pierre Alquier +1
This work investigates the offline formulation of the contextual bandit problem, where the goal is to leverage past interactions collected under a behavior policy to evaluate, sele…
Towards a turnkey approach to unbiased Monte Carlo estimation of smooth functions of expectations
Nicolas Chopin, Francesca R. Crucinio, Sumeetpal S. Singh
Given a smooth function , we develop a general approach to turn Monte Carlo samples with expectation into an unbiased estimate of . Specifically, we develop estimators…
A connection between Tempering and Entropic Mirror Descent
Nicolas Chopin, Francesca R. Crucinio, Anna Korba
This paper explores the connections between tempering (for Sequential Monte Carlo; SMC) and entropic mirror descent to sample from a target probability distribution whose unnormali…
Computational aspects of Bayesian spectral density estimation
Nicolas Chopin, Judith Rousseau, Brunero Liseo
Gaussian time-series models are often specified through their spectral density. Such models present several computational challenges, in particular because of the non-sparse nature…