Learning Graphical Models
Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors
Fei Jiang, Guosheng Yin, Francesca Dominici
Based on non-local prior distributions, we propose a Bayesian model selection (BMS) procedure for boundary detection in a sequence of data with multiple systematic mean changes. The BMS method can effectively suppress the non-boundary spike points with large instantaneous changes.
Distributionally Robust Graphical Models
Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian Ziebart
Neural Information Processing Systems http://nips.cc/
Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation
Chaitanya Ryali, Gautam Reddy, Angela J. Yu
It has been found that DBM captures human behavior better than FBM, even though the latter more veridically captures experimental design in a variety of tasks, e.g.
Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects
Adam Kosiorek, Hyunjik Kim, Yee Whye Teh, Ingmar Posner
Neural Information Processing Systems http://nips.cc/
Causal Inference via Kernel Deviance Measures
Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
In many areas of science, we strive to answer questions that are fundamentally causal in nature.
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization
Bargav Jayaraman, Lingxiao Wang, David Evans, Quanquan Gu
In our output perturbation method, the parties combine local models within a secure computation and then add the required differential privacy noise before revealing the model.
Multi-domain Causal Structure Learning in Linear Systems
AmirEmad Ghassami, Negar Kiyavash, Biwei Huang, Kun Zhang
Our approach unifies the idea in those works and generalizes to the case that there is no such invariance across the domains. Our proposed methods are generally capable of identifying causal direction from fewer than ten domains.
A Bayes-Sard Cubature Method
Toni Karvonen, Chris J. Oates, Simo Sarkka
Neural Information Processing Systems http://nips.cc/
Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks
Agastya Kalra, Abdullah Rashwan, Wei-Shou Hsu, Pascal Poupart, Prashant Doshi, Georgios Trimponias
Neural Information Processing Systems http://nips.cc/
Learning Others' Intentional Models in Multi-Agent Settings Using Interactive POMDPs
Yanlin Han, Piotr Gmytrasiewicz
It extends POMDPs to multi-agent settings by including models of other agents in the state space and forming a hierarchical belief structure. In order to predict other agents' actions using I-POMDPs, we