2 citations · 3 across the 5 of their papers we have counts for
5 papers
Hide&Seek: Learning to Explain in an End-to-End Differentiable Network
Tal Ellinson, Hadi Mohasel Afshar, Sally Cripps
Instance-wise feature selection is a valuable tool for interpreting labeled data and the predictions of black-box models. In contrast to global feature selection techniques, instan…
A Beta Cauchy-Cauchy (BECCA) shrinkage prior for Bayesian variable selection
Linduni M. Rodrigo, Robert Kohn, Hadi M. Afshar +1
This paper introduces a novel Bayesian approach for variable selection in high-dimensional and potentially sparse regression settings. Our method replaces the indicator variables i…
Covariate Dependent Mixture of Bayesian Networks
Roman Marchant, Dario Draca, Gilad Francis +4
Learning the structure of Bayesian networks from data provides insights into underlying processes and the causal relationships that generate the data, but its usefulness depends on…
Optimal Particle-based Approximation of Discrete Distributions (OPAD)
Hadi Mohasel Afshar, Gilad Francis, Sally Cripps
Particle-based methods include a variety of techniques, such as Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC), for approximating a probabilistic target distribut…
Bayesian Adaptive Trials for Social Policy
Sally Cripps, Anna Lopatnikova, Hadi Mohasel Afshar +5
This paper proposes Bayesian Adaptive Trials (BAT) as both an efficient method to conduct trials and a unifying framework for evaluation social policy interventions, addressing lim…