Learning Graphical Models
Search-Guided, Lightly-Supervised Training of Structured Prediction Energy Networks
Structured prediction energy networks (SPENs) (Belanger & McCallum, 2016) are a type of energy-based model (LeCun et al., 2006) in which inference is done by gradient descent.
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions
Murat Kocaoglu, Amin Jaber, Karthikeyan Shanmugam, Elias Bareinboim
Neural Information Processing Systems http://nips.cc/
A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning
Francisco Garcia, Philip S. Thomas
Neural Information Processing Systems http://nips.cc/
Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models
Ruoxi Sun, Scott Linderman, Ian Kinsella, Liam Paninski
Neural Information Processing Systems http://nips.cc/
Learning low-dimensional state embeddings and metastable clusters from time series data
Yifan Sun, Yaqi Duan, Hao Gong, Mengdi Wang
Markov state trajectories, where the transition kernel has a small intrinsic rank. In the spirit of diffusion map, we propose an efficient method for learning a low-dimensional state embedding and capturing the process's dynamics.
Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms
Shahana Ibrahim, Xiao Fu, Nikolaos Kargas, Kejun Huang
The data deluge comes with high demands for data labeling.
Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes
Lingge Li, Dustin Pluta, Babak Shahbaba, Norbert Fortin, Hernando Ombao, Pierre Baldi
Neural Information Processing Systems http://nips.cc/
Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning
We propose a novel active learning (AL) model that integrates Bayesian and discriminative kernel machines for fast and accurate multi-class data sampling.
