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Found 5 papers, 1 papers with code
Identifiability of deep generative models without auxiliary information
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 that are commonly used in practice.
Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families
Greedy algorithms have long been a workhorse for learning graphical models, and more broadly for learning statistical models with sparse structure.
Learning latent causal graphs via mixture oracles
We study the problem of reconstructing a causal graphical model from data in the presence of latent variables.
Improved upper bounds for the rigidity of Kronecker products
At MFCS'77, Valiant introduced matrix rigidity as a tool to prove circuit lower bounds for linear functions and since then this notion received much attention and found applications in other areas of complexity theory.
Data Structures and Algorithms Computational Complexity Combinatorics
Exact nuclear norm, completion and decomposition for random overcomplete tensors via degree-4 SOS
In this paper we show that simple semidefinite programs inspired by degree $4$ SOS can exactly solve the tensor nuclear norm, tensor decomposition, and tensor completion problems on tensors with random asymmetric components.
