6 papers
A Theory on Flow Matching with Neural Networks
Yihan He, Qishuo Yin, Yuan Cao +2
In this work, we develop theoretical foundation for flow matching with neural-network-parameterized conditional velocity fields. We establish convergence guarantees for gradient de…
Goedel-Architect: Streamlining Formal Theorem Proving with Blueprint Generation and Refinement
Jui-Hui Chung, Ziyang Cai, Zihao Li +14
We introduce Goedel-Architect, an agentic framework for formal theorem proving in Lean 4 centered on blueprint generation and refinement. A blueprint is a dependency graph of defin…
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…
SMART Fine-tuning Factor Augmented Neural Lasso
Jinhang Chai, Jianqing Fan, Cheng Gao +1
Fine-tuning is a widely used strategy for adapting pre-trained models to new tasks, yet its methodology and theoretical properties in high-dimensional nonparametric settings with v…
Factor Informed Double Deep Learning For Average Treatment Effect Estimation
Jianqing Fan, Soham Jana, Sanjeev Kulkarni +1
We investigate the problem of estimating the average treatment effect (ATE) under a very general setup where the covariates can be high-dimensional, highly correlated, and can have…
Covariate-Balancing-Aware Interpretable Deep Learning models for Treatment Effect Estimation
Kan Chen, Qishuo Yin, Qi Long
Estimating treatment effects is of great importance for many biomedical applications with observational data. Particularly, interpretability of the treatment effects is preferable…