Learning Graphical Models
A multi-agent reinforcement learning model of common-pool resource appropriation
Humanity faces numerous problems of common-pool resource appropriation.
Experimental Design for Learning Causal Graphs with Latent Variables
Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim
Causality shapes how we view, understand, and react to the world around us.
Deep Dynamic Poisson Factorization Model
A new model, named as deep dynamic poisson factorization model, is proposed in this paper for analyzing sequential count vectors.
Permutation-based Causal Inference Algorithms with Interventions
Yuhao Wang, Liam Solus, Karren Yang, Caroline Uhler
In this paper, we present two algorithms of this type and prove that both are consistent under the faithfulness assumption.
DPSCREEN: Dynamic Personalized Screening
Kartik Ahuja, William Zame, Mihaela van der Schaar
Screening is important for the diagnosis and treatment of a wide variety of diseases. A good screening policy should be personalized to the features of the patient and to the dynamic history of the patient (including the history of screening).
Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes
Taylor W. Killian, Samuel Daulton, George Konidaris, Finale Doshi-Velez
We introduce a new formulation of the Hidden Parameter Markov Decision Process (HiP-MDP), a framework for modeling families of related tasks using low-dimensional latent embeddings.
Neural Variational Inference and Learning in Undirected Graphical Models
Volodymyr Kuleshov, Stefano Ermon
Our approach offers a number of advantages over previous methods.
Introspective Classification with Convolutional Nets
Long Jin, Justin Lazarow, Zhuowen Tu
We employ a reclassification-by-synthesis algorithm to perform training using a formulation stemmed from the Bayes theory.
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data
Wei-Ning Hsu, Yu Zhang, James Glass
Neural Information Processing Systems http://nips.cc/
State Aware Imitation Learning
Yannick Schroecker, Charles L. Isbell
Formally, we define the problem domain as a Markov decision process, i.e. by its states, actions and unknown Markovian transition probabilities