no code implementations • 23 Feb 2022 • Cameron Haigh, Zichen Zhang, Negar Hassanpour, Khurram Javed, Yingying Fu, Shayan Shahramian, Shawn Zhang, Jun Luo
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.
no code implementations • 11 Nov 2021 • Negar Hassanpour, Russell Greiner
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.
no code implementations • ICLR 2020 • Negar Hassanpour, Russell Greiner
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.
no code implementations • 19 Dec 2019 • Zichen Zhang, Qingfeng Lan, Lei Ding, Yue Wang, Negar Hassanpour, Russell Greiner
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.