From the 1 of 7 linked papers with an AI index.
7 papers
Generative Synthetic Data for Causal Inference: Pitfalls, Remedies, and Opportunities
Yichen Xu
The paper examines how fully generative synthetic data models can preserve predictive performance but distort causal estimates, and proposes a hybrid synthetic-data approach that s…
Adaptive Targeted Maximum Likelihood Estimation of the Mean Potential Outcome under a Treatment Rule
Yichen Xu, Mark J. van der Laan
Estimating the mean counterfactual outcome under a treatment rule is a central problem in causal inference and policy evaluation. Standard estimators, including inverse probability…
Investigating Targeting Strategies and Truncation in TMLE for the Average Treatment Effect under Practical Positivity Violations
Yichen Xu, Susan Gruber, Mark J. van der Laan
Estimating average treatment effects from observational data is challenging under practical violations of the positivity assumption. Targeted Maximum Likelihood Estimators (TMLEs)…
Evolution of Optimization Methods: Algorithms, Scenarios, and Evaluations
Tong Zhang, Jiangning Zhang, Zhucun Xue +9
Balancing convergence speed, generalization capability, and computational efficiency remains a core challenge in deep learning optimization. First-order gradient descent methods, e…
Residual Feature Integration is Sufficient to Prevent Negative Transfer
Yichen Xu, Ryumei Nakada, Linjun Zhang +1
Transfer learning has become a central paradigm in modern machine learning, yet it suffers from the long-standing problem of negative transfer, where leveraging source representati…
Understanding and Guiding Layer Placement in Parameter-Efficient Fine-Tuning of Large Language Models
Yichen Xu, Yuyang Liang, Shan Dai +3
As large language models (LLMs) continue to grow, the cost of full-parameter fine-tuning has made parameter-efficient fine-tuning (PEFT) the default strategy for downstream adaptat…