You can now learn ReLU-based models in polynomial time using tensor-based filtering and boosting—a theoretical breakthrough that removes computational barriers for this fundamental learning problem.
This paper presents the first polynomial-time algorithm for learning positive linear combinations of ReLU activations under Gaussian distributions. The key innovation is a boosting procedure combined with implicit tensor methods that efficiently recovers hidden weight vectors, solving a problem that previously required super-polynomial time.