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We want to model a high dimension random vector $\boldsymbol{x}$, which can often be described by only a few latent factors $\boldsymbol{z}$. LVM take a two-step generation scheme:
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Normalizing Flows (NF) can model flexible distributions for data sampling and density estimation. The idea is based on change of variables formula, applying a series of transformations on a simple distribution to approximate a complex distribution.
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Convergent Cross Mapping (CCM) is a method for establishing causality from long time series data (\(\geq 25\) observations). The method was first proposed in 1990 by Sugihara et al and revisited in 2012.
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This blog post is some reading notes of the paper Double/Debiased Machine Learning for Treatment and Structural Parameters by Victor Chernozhukov et al.
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The paper “Direct Learning with Guarantees of the Difference DAG Between Structural Equation Models” by Asish Ghoshal, Kevin Bello and Jean Honorio studies the problem of directly estimating the structural difference between two structural equation models (SEMs) with the same topological ordering.
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This blog post is some reading notes of the paper Quasi-oracle estimation of heterogeneous treatment effects by Xinkun Nie and Stefan Wager.
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This blog post is my reading notes of the paper Recursive partitioning for heterogeneous causal effects by Susan Athey and Guido Imbens.
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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Short description of portfolio item number 1
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Short description of portfolio item number 2
Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI) 2020
Recommended citation: C. Améndola, P. Dettling, M. Drton, F. Onori, J. Wu (2020). "Structure Learning for Cyclic Linear Causal Models." UAI 2020. https://proceedings.mlr.press/v124/amendola20a
Under review 2023
Recommended citation: M. Drton, B. Hollering, J. Wu (2023). "Identifiability of Homoscedastic Linear Structural Equation Models using Algebraic Matroids." . https://arxiv.org/abs/2308.01821
IEEE Journal on Selected Areas in Information Theory 2023
Recommended citation: J. Wu and M.Drton (2023). "Partial Homoscedasticity in Causal Discovery With Linear Models." in IEEE Journal on Selected Areas in Information Theory, vol. 4, pp. 639-650, 2023. https://ieeexplore.ieee.org/document/10304270
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.