Learning Graphical Models
Learning latent variable structured prediction models with Gaussian perturbations
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Parsimonious Bayesian deep networks
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Generative Neural Machine Translation
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DAGs with NO TEARS: Continuous Optimization for Structure Learning
Xun Zheng, Bryon Aragam, Pradeep K. Ravikumar, Eric P. Xing
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Dynamic Network Model from Partial Observations
Elahe Ghalebi, Baharan Mirzasoleiman, Radu Grosu, Jure Leskovec
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Direct Estimation of Differences in Causal Graphs
Yuhao Wang, Chandler Squires, Anastasiya Belyaeva, Caroline Uhler
However, the undirected setting is often insufficient: only a causal (i.e., directed) network provides
Learning Signed Determinantal Point Processes through the Principal Minor Assignment Problem
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Statistical Model Aggregation via Parameter Matching
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang
We consider the problem of aggregating models learned from sequestered, possibly heterogeneous datasets.
Bayesian Model-Agnostic Meta-Learning
Jaesik Yoon, Taesup Kim, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, Sungjin Ahn
A robust meta-learning algorithm therefore mustbe able to systematically deal with such uncertainty in order to be applicable to critical problemssuch as healthcare and self-driving cars.