Learning Graphical Models
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.
Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs
Marek Petrik, Reazul Hasan Russel
Robust MDPs (RMDPs) can be used to compute policies with provable worst-case guarantees in reinforcement learning.
Machine Teaching of Active Sequential Learners
Tomi Peltola, Mustafa Mert Çelikok, Pedram Daee, Samuel Kaski
Neural Information Processing Systems http://nips.cc/
Expressive power of tensor-network factorizations for probabilistic modeling
Ivan Glasser, Ryan Sweke, Nicola Pancotti, Jens Eisert, Ignacio Cirac
Many problems in diverse areas of computer science and physics involve constructing efficient representations of high-dimensional functions. Neural networks are a particular example of such representations that have enjoyed great empirical success, and much effort has been dedicated to understanding their expressive power - i.e. the set of functions that they can efficiently represent. Analogously, tensor networks are a class of powerful representations of high-dimensional arrays (tensors), for which a variety of algorithms and methods have been developed.