Bayesian Inference
Thompson Sampling with Information Relaxation Penalties Seungki Min Columbia Business School Costis Maglaras Columbia Business School Ciamac C. Moallemi Columbia Business School
We consider a finite-horizon multi-armed bandit (MAB) problem in a Bayesian setting, for which we propose an information relaxation sampling framework.
Pseudo-Extended Markov Chain Monte Carlo
In this paper, we introduce the pseudo-extended MCMC method as a simple approach for improving the mixing of the MCMC sampler for multi-modal posterior distributions.
Structure Learning with Side Information: Sample Complexity
Neural Information Processing Systems http://nips.cc/
The Functional Neural Process
Christos Louizos, Xiahan Shi, Klamer Schutte, Max Welling
Neural Information Processing Systems http://nips.cc/
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.
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/
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/
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.