4 citations · 6 across the 5 of their papers we have counts for
5 papers
Correcting Confounding via Random Selection of Background Variables
You-Lin Chen, Lenon Minorics, Dominik Janzing
We propose a method to distinguish causal influence from hidden confounding in the following scenario: given a target variable Y, potential causal drivers X, and a large number of…
Provably Training Overparameterized Neural Network Classifiers with Non-convex Constraints
You-Lin Chen, Zhaoran Wang, Mladen Kolar
Training a classifier under non-convex constraints has gotten increasing attention in the machine learning community thanks to its wide range of applications such as algorithmic fa…
Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach
Luofeng Liao, You-Lin Chen, Zhuoran Yang +3
Structural equation models (SEMs) are widely used in sciences, ranging from economics to psychology, to uncover causal relationships underlying a complex system under consideration…
Convergence Analysis of Accelerated Stochastic Gradient Descent under the Growth Condition
You-Lin Chen, Sen Na, Mladen Kolar
We study the convergence of accelerated stochastic gradient descent for strongly convex objectives under the growth condition, which states that the variance of stochastic gradient…
Tensor Canonical Correlation Analysis with Convergence and Statistical Guarantees
You-Lin Chen, Mladen Kolar, Ruey S. Tsay
In many applications, such as classification of images or videos, it is of interest to develop a framework for tensor data instead of an ad-hoc way of transforming data to vectors…