no code implementations • 2 Feb 2017 • Shravan Vasishth, Nicolas Chopin, Robin Ryder, Bruno Nicenboim
We present a case-study demonstrating the usefulness of Bayesian hierarchical mixture modelling for investigating cognitive processes.
no code implementations • 12 Jun 2015 • Pierre Alquier, James Ridgway, Nicolas Chopin
We consider instead variational approximations of the Gibbs posterior, which are fast to compute.
no code implementations • 11 Jun 2014 • Simon Barthelmé, Nicolas Chopin
Here we show that inferring the parameters of a unnormalised model on a space $\Omega$ can be mapped onto an equivalent problem of estimating the intensity of a Poisson point process on $\Omega$.
no code implementations • 5 Jun 2014 • Pierre Alquier, Vincent Cottet, Nicolas Chopin, Judith Rousseau
While the behaviour of algorithms based on nuclear norm minimization is now well understood, an as yet unexplored avenue of research is the behaviour of Bayesian algorithms in this context.
no code implementations • NeurIPS 2014 • James Ridgway, Pierre Alquier, Nicolas Chopin, Feng Liang
We also extend our method to a class of non-linear score functions, essentially leading to a nonparametric procedure, by considering a Gaussian process prior.
no code implementations • 24 Oct 2022 • Otmane Sakhi, Pierre Alquier, Nicolas Chopin
This paper introduces a new principled approach for off-policy learning in contextual bandits.
no code implementations • 3 Aug 2023 • Otmane Sakhi, David Rohde, Nicolas Chopin
We compare our method to the commonly adopted Plackett-Luce policy class and demonstrate the effectiveness of our approach on problems with action space sizes in the order of millions.
no code implementations • 18 Oct 2023 • Nicolas Chopin, Francesca R. Crucinio, Anna Korba
We establish that tempering SMC corresponds to entropic mirror descent applied to the reverse Kullback-Leibler (KL) divergence and obtain convergence rates for the tempering iterates.
1 code implementation • 7 Jun 2022 • Nicolas Chopin, Andras Fulop, Jeremy Heng, Alexandre H. Thiery
This paper is concerned with online filtering of discretely observed nonlinear diffusion processes.
1 code implementation • 4 Feb 2022 • Adrien Corenflos, Nicolas Chopin, Simo Särkkä
We propose dSMC (de-Sequentialized Monte Carlo), a new particle smoother that is able to process $T$ observations in $\mathcal{O}(\log T)$ time on parallel architecture.