Learning Graphical Models
Parsimonious Bayesian deep networks
Neural Information Processing Systems http://nips.cc/
Generative Neural Machine Translation
Neural Information Processing Systems http://nips.cc/
DAGs with NO TEARS: Continuous Optimization for Structure Learning
Xun Zheng, Bryon Aragam, Pradeep K. Ravikumar, Eric P. Xing
Neural Information Processing Systems http://nips.cc/
Dynamic Network Model from Partial Observations
Elahe Ghalebi, Baharan Mirzasoleiman, Radu Grosu, Jure Leskovec
Neural Information Processing Systems http://nips.cc/
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
Neural Information Processing Systems http://nips.cc/
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.
Deep Generative Markov State Models
Hao Wu, Andreas Mardt, Luca Pasquali, Frank Noe
We propose a deep generative Markov State Model (DeepGenMSM) learningframework for inference of metastable dynamical systems and prediction of tra-jectories.