14 citations · 18 across the 6 of their papers we have counts for
4 papers · 1 filter
Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces
Kirill Struminsky, Artyom Gadetsky, Denis Rakitin +2
Structured latent variables allow incorporating meaningful prior knowledge into deep learning models. However, learning with such variables remains challenging because of their dis…
Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution
Artyom Gadetsky, Kirill Struminsky, Christopher Robinson +2
Learning models with discrete latent variables using stochastic gradient descent remains a challenge due to the high variance of gradient estimates. Modern variance reduction techn…
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates
Kirill Struminsky, Simon Lacoste-Julien, Anton Osokin
We study consistency properties of machine learning methods based on minimizing convex surrogates. We extend the recent framework of Osokin et al. (2017) for the quantitative analy…
Robust Variational Inference
Michael Figurnov, Kirill Struminsky, Dmitry Vetrov
Variational inference is a powerful tool for approximate inference. However, it mainly focuses on the evidence lower bound as variational objective and the development of other mea…