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08/03/2023
Fast Slate Policy Optimization: Going Beyond Plackett-Luce
An increasingly important building block of large scale machine learning...
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06/27/2023
Debiasing Piecewise Deterministic Markov Process samplers using couplings
Monte Carlo methods - such as Markov chain Monte Carlo (MCMC) and piecew...
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03/22/2023
Scalable Bayesian bi-level variable selection in generalized linear models
Motivated by a real-world application in cardiology, we develop an algor...
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10/24/2022
PAC-Bayesian Offline Contextual Bandits With Guarantees
This paper introduces a new principled approach for offline policy optim...
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10/04/2022
Higher-order stochastic integration through cubic stratification
We propose two novel unbiased estimators of the integral ∫_[0,1]^sf(u) d...
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07/03/2022
On the complexity of backward smoothing algorithms
In the context of state-space models, backward smoothing algorithms rely...
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06/21/2022
De-biasing particle filtering for a continuous time hidden Markov model with a Cox process observation model
We develop a (nearly) unbiased particle filtering algorithm for a specif...
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06/07/2022
Computational Doob's h-transforms for Online Filtering of Discretely Observed Diffusions
This paper is concerned with online filtering of discretely observed non...
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03/18/2022
On resampling schemes for particle filters with weakly informative observations
We consider particle filters with weakly informative observations (or `p...
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02/04/2022
De-Sequentialized Monte Carlo: a parallel-in-time particle smoother
Particle smoothers are SMC (Sequential Monte Carlo) algorithms designed ...
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11/15/2021
Amended Gibbs samplers for Cosmic Microwave Background power spectrum estimation
We study different variants of the Gibbs sampler algorithm from the pers...
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07/09/2021
Fast compression of MCMC output
We propose cube thinning, a novel method for compressing the output of a...
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12/29/2020
Metropolis-Hastings with Averaged Acceptance Ratios
Markov chain Monte Carlo (MCMC) methods to sample from a probability dis...
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11/04/2020
Waste-free Sequential Monte Carlo
A standard way to move particles in a SMC sampler is to apply several st...
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08/23/2018
Adaptive Tuning Of Hamiltonian Monte Carlo Within Sequential Monte Carlo
Sequential Monte Carlo (SMC) samplers form an attractive alternative to ...
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03/26/2018
On the utility of Metropolis-Hastings with asymmetric acceptance ratio
The Metropolis-Hastings algorithm allows one to sample asymptotically fr...
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01/31/2018
Noise contrastive estimation: asymptotics, comparison with MC-MLE
A statistical model is said to be un-normalised when its likelihood func...
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10/03/2017
Improving approximate Bayesian computation via quasi Monte Carlo
ABC (approximate Bayesian computation) is a general approach for dealing...
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02/02/2017
Modelling dependency completion in sentence comprehension as a Bayesian hierarchical mixture process: A case study involving Chinese relative clauses
We present a case-study demonstrating the usefulness of Bayesian hierarc...
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06/12/2015
On the properties of variational approximations of Gibbs posteriors
The PAC-Bayesian approach is a powerful set of techniques to derive non-...
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10/07/2014
PAC-Bayesian AUC classification and scoring
We develop a scoring and classification procedure based on the PAC-Bayes...
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11/26/2012
Bayesian learning of noisy Markov decision processes
We consider the inverse reinforcement learning problem, that is, the pro...
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07/29/2011
