144 citations · 324 across the 35 of their papers we have counts for
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cs.LG2023★ 2 cited
Provable convergence guarantees for black-box variational inference
Justin Domke, Guillaume Garrigos, Robert Gower
Black-box variational inference is widely used in situations where there is no proof that its stochastic optimization succeeds. We suggest this is due to a theoretical gap in exist…
cs.LG2023
Sample Average Approximation for Black-Box VI
Javier Burroni, Justin Domke, Daniel Sheldon
We present a novel approach for black-box VI that bypasses the difficulties of stochastic gradient ascent, including the task of selecting step-sizes. Our approach involves using a…
stat.ML2023
Discriminative calibration: Check Bayesian computation from simulations and flexible classifier
Yuling Yao, Justin Domke
To check the accuracy of Bayesian computations, it is common to use rank-based simulation-based calibration (SBC). However, SBC has drawbacks: The test statistic is somewhat ad-hoc…