Kullback-Leibler Proximal Variational Inference

By Mohammad Emtiyaz Khan, Pierre Baqué, Franccois Fleuret, Pascal Fua. 2015

52 citations recorded by the source corpus.

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Citations in the corpus

Composing graphical models with neural networks for structured representations and fast inference (2016, background): “[23] Mohammad E Khan, Pierre Baqué, François Fleuret, and Pascal Fua.”
Automatic Differentiation Variational Inference (2016, background): “Optimization using line search could also improve convergence speed and robustness (Mahsereci and Hennig, 2015), as well as natural gradient approaches for nonconjugate models (Khan et al., 2015).”
Partitioned Variational Inference: A unified framework encompassing federated and continual learning (2018, background): “It is also possible to define optimization approaches analogous to the above that do not linearize the free-energy term and instead perform potentially multiple updates of the nested non-linear optimization problems [Theis and Hoffman, 2015, Khan et al., 2015, 2016].”
Incremental Variational Sparse Gaussian Process Regression (2016, background): “Recently, several researches have considered the manifold structure induced by KL divergence in Bayesian inference [7, 24, 13].”
Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions (2015, methodology): “The approach of Khan et al. (2015) considers (9) in the special case of (6) where we use the exact gradient ∇ f ( λ k ) in the ﬁrst term.”
Advances in Variational Inference (2017, background): “In contrast, approaches based on additional lower bounds with closed-form updates [82], [88], [207] can”