13 citations · 20 across the 4 of their papers we have counts for
6 papers
Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model
Alexander Buchholz, Ben London, Giuseppe di Benedetto +1
A critical need for industrial recommender systems is the ability to evaluate recommendation policies offline, before deploying them to production. Unfortunately, widely used off-p…
Low-variance estimation in the Plackett-Luce model via quasi-Monte Carlo sampling
Alexander Buchholz, Jan Malte Lichtenberg, Giuseppe Di Benedetto +3
The Plackett-Luce (PL) model is ubiquitous in learning-to-rank (LTR) because it provides a useful and intuitive probabilistic model for sampling ranked lists. Counterfactual offlin…
Distributed Computation for Marginal Likelihood based Model Choice
Alexander Buchholz, Daniel Ahfock, Sylvia Richardson
We propose a general method for distributed Bayesian model choice, using the marginal likelihood, where a data set is split in non-overlapping subsets. These subsets are only acces…
Adaptive Tuning Of Hamiltonian Monte Carlo Within Sequential Monte Carlo
Alexander Buchholz, Nicolas Chopin, Pierre E. Jacob
Sequential Monte Carlo (SMC) samplers form an attractive alternative to MCMC for Bayesian computation. However, their performance depends strongly on the Markov kernels used to rej…
Quasi-Monte Carlo Variational Inference
Alexander Buchholz, Florian Wenzel, Stephan Mandt
Many machine learning problems involve Monte Carlo gradient estimators. As a prominent example, we focus on Monte Carlo variational inference (MCVI) in this paper. The performance…
Improving approximate Bayesian computation via quasi-Monte Carlo
Alexander Buchholz, Nicolas Chopin
ABC (approximate Bayesian computation) is a general approach for dealing with models with an intractable likelihood. In this work, we derive ABC algorithms based on QMC (quasi- Mon…