7 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…
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…
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…
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…
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…