Learning Graphical Models
Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics
Molecular dynamics (MD) is a broadly adopted technique to approximate such quantities.
Infinite-Horizon Gaussian Processes
Arno Solin, James Hensman, Richard E. Turner
Neural Information Processing Systems http://nips.cc/
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification
Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi, Lorenzo Rosasco, Maurizio Filippone
Neural Information Processing Systems http://nips.cc/
Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
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/
Nonparametric learning from Bayesian models with randomized objective functions
Simon Lyddon, Stephen Walker, Chris C. Holmes
This assumption is problematic, particularly in complex data environments.
Discriminative Calibration: Check Bayesian
However, SBC has drawbacks: The test statistic is somewhat ad-hoc, interactions are difficult to examine, multiple testing is a challenge, and the resulting p-value is not a divergence metric.