6 citations · 24 across the 9 of their papers we have counts for
3 papers · 1 filter
Identifiability of deep generative models without auxiliary information
Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar +1
We prove identifiability of a broad class of deep latent variable models that (a) have universal approximation capabilities and (b) are the decoders of variational autoencoders tha…
Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families
Goutham Rajendran, Bohdan Kivva, Ming Gao +1
Greedy algorithms have long been a workhorse for learning graphical models, and more broadly for learning statistical models with sparse structure. In the context of learning direc…
Learning latent causal graphs via mixture oracles
Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar +1
We study the problem of reconstructing a causal graphical model from data in the presence of latent variables. The main problem of interest is recovering the causal structure over…