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Found 4 papers, 3 papers with code
Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity
We study the problem of online sequential decision-making given auxiliary demonstrations from experts who made their decisions based on unobserved contextual information.
Order-based Structure Learning with Normalizing Flows
Estimating the causal structure of observational data is a challenging combinatorial search problem that scales super-exponentially with graph size.
Partial Identification of Treatment Effects with Implicit Generative Models
We propose a new method for partial identification of average treatment effects(ATEs) in general causal graphs using implicit generative models comprising continuous and discrete random variables.
Learning to Switch Among Agents in a Team via 2-Layer Markov Decision Processes
Reinforcement learning agents have been mostly developed and evaluated under the assumption that they will operate in a fully autonomous manner -- they will take all actions.
