37 citations · 40 across the 7 of their papers we have counts for
6 papers · 1 filter
Optimal use of a black-box learner in semiparametric estimation
Yihong Gu
Consider the partial linear model and in the structure-agnostic setting, where we are blind to the structure and $π_…
Unveiling Invariant and Transferable Latent Factors Across Heterogeneous Environments via ATLAS
Yihong Gu, Katherine Liao, Tianxi Cai
This paper considers a multi-environment factor model in which high-dimensional covariates are collected from heterogeneous environments, with auxiliary labels available in a subse…
Optimally taming biases in black-box models for efficient semiparametric estimation
Yihong Gu, Qishuo Yin, Tianxi Cai +1
Modern semiparametric estimation often relies on flexible black-box machine learning methods to estimate nuisance functions, raising a fundamental question: how do nuisance estimat…
Fundamental Computational Limits in Pursuing Invariant Causal Prediction and Invariance-Guided Regularization
Yihong Gu, Cong Fang, Yang Xu +2
Pursuing invariant prediction from heterogeneous environments opens the door to learning causality in a purely data-driven way and has several applications in causal discovery and…
Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning
Yihong Gu, Cong Fang, Peter Bühlmann +1
Pursuing causality from data is a fundamental problem in scientific discovery, treatment intervention, and transfer learning. This paper introduces a novel algorithmic method for a…
Environment Invariant Linear Least Squares
Jianqing Fan, Cong Fang, Yihong Gu +1
This paper considers a multi-environment linear regression model in which data from multiple experimental settings are collected. The joint distribution of the response variable an…