4 citations · 5 across the 4 of their papers we have counts for
4 papers
Causal Regularization
Dominik Janzing
I argue that regularizing terms in standard regression methods not only help against overfitting finite data, but sometimes also yield better causal models in the infinite sample r…
Causal Regularization
Mohammad Taha Bahadori, Krzysztof Chalupka, Edward Choi +3
In application domains such as healthcare, we want accurate predictive models that are also causally interpretable. In pursuit of such models, we propose a causal regularizer to st…
Sparse Hierarchical Tucker Factorization and its Application to Healthcare
Ioakeim Perros, Robert Chen, Richard Vuduc +1
We propose a new tensor factorization method, called the Sparse Hierarchical-Tucker (Sparse H-Tucker), for sparse and high-order data tensors. Sparse H-Tucker is inspired by its na…
Guaranteed Scalable Learning of Latent Tree Models
Furong Huang, Niranjan U. N., Ioakeim Perros +3
We present an integrated approach for structure and parameter estimation in latent tree graphical models. Our overall approach follows a "divide-and-conquer" strategy that learns m…