Learning Graphical Models
Identifying Causal Effects via Context-specific Independence Relations
Santtu Tikka, Antti Hyttinen, Juha Karvanen
Neural Information Processing Systems http://nips.cc/
Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
Dominik Linzner, Michael Schmidt, Heinz Koeppl
The CIMs are the generators of the dynamics of a CTBN.
Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians
Axel Brando, Jose A. Rodriguez, Jordi Vitria, Alberto Rubio Muñoz
On the other hand, another source of uncertainty is "inevitable variability in the response variable", i.e. when the variable to predict exhibits randomness,
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting
Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric J. Horvitz, Stefano Ermon
A learned generative model often produces biased statistics relative to the underlying data distribution.
Co-Generation with GANs using AIS based HMC
Tiantian Fang, Alexander Schwing
Therefore, in this paper, we study the occurring challenges for co-generation with GANs. To address those challenges we develop an annealed importance sampling based Hamiltonian Monte Carlo co-generation algorithm.
Exact Combinatorial Optimization with Graph Convolutional Neural Networks
Maxime Gasse, Didier Chetelat, Nicola Ferroni, Laurent Charlin, Andrea Lodi
Neural Information Processing Systems http://nips.cc/
Re-examination of the Role of Latent Variables in Sequence Modeling
Guokun Lai, Zihang Dai, Yiming Yang, Shinjae Yoo
Neural Information Processing Systems http://nips.cc/
Program Synthesis and Semantic Parsing with Learned Code Idioms
Eui Chul Shin, Miltiadis Allamanis, Marc Brockschmidt, Alex Polozov
Neural Information Processing Systems http://nips.cc/