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Found 4 papers, 0 papers with code
Drawing Inductor Layout with a Reinforcement Learning Agent: Method and Application for VCO Inductors
In light of the need to tweak the target specifications throughout the circuit design cycle, we also develop a variant in which the agent can learn to quickly adapt to draw new inductors for moderately different target specifications.
Variational Auto-Encoder Architectures that Excel at Causal Inference
In this paper, we take a generative approach that builds on the recent advances in Variational Auto-Encoders to simultaneously learn those underlying factors as well as the causal effects.
Learning Disentangled Representations for CounterFactual Regression
We consider the challenge of estimating treatment effects from observational data; and point out that, in general, only some factors based on the observed covariates X contribute to selection of the treatment T, and only some to determining the outcomes Y.
Reducing Selection Bias in Counterfactual Reasoning for Individual Treatment Effects Estimation
We learn two groups of latent random variables, where one group corresponds to variables that only cause selection bias, and the other group is relevant for outcome prediction.
