10 citations · 22 across the 13 of their papers we have counts for
4 papers · 1 filter
Bayesian Active Learning in the Presence of Nuisance Parameters
Sabina J. Sloman, Ayush Bharti, Julien Martinelli +1
In many settings, such as scientific inference, optimization, and transfer learning, the learner has a well-defined objective, which can be treated as estimation of a target parame…
Learning Robust Statistics for Simulation-based Inference under Model Misspecification
Daolang Huang, Ayush Bharti, Amauri Souza +2
Simulation-based inference (SBI) methods such as approximate Bayesian computation (ABC), synthetic likelihood, and neural posterior estimation (NPE) rely on simulating statistics t…
Learning Relevant Contextual Variables Within Bayesian Optimization
Julien Martinelli, Ayush Bharti, Armi Tiihonen +5
Contextual Bayesian Optimization (CBO) efficiently optimizes black-box functions with respect to design variables, while simultaneously integrating contextual information regarding…
Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference
Ayush Bharti, Masha Naslidnyk, Oscar Key +2
Likelihood-free inference methods typically make use of a distance between simulated and real data. A common example is the maximum mean discrepancy (MMD), which has previously bee…