2 citations · 2 across the 5 of their papers we have counts for
5 papers
Towards Intrinsically Calibrated Uncertainty Quantification in Industrial Data-Driven Models via Diffusion Sampler
Yiran Ma, Jerome Le Ny, Zhichao Chen +1
In modern process industries, data-driven models are important tools for real-time monitoring when key performance indicators are difficult to measure directly. While accurate pred…
Relaxing Probabilistic Latent Variable Models' Specification via Infinite-Horizon Optimal Control
Zhichao Chen, Hao Wang, Licheng Pan +6
In this paper, we address the issue of model specification in probabilistic latent variable models (PLVMs) using an infinite-horizon optimal control approach. Traditional PLVMs rel…
Proximity Matters: Local Proximity Enhanced Balancing for Treatment Effect Estimation
Hao Wang, Zhichao Chen, Zhaoran Liu +3
Heterogeneous treatment effect (HTE) estimation from observational data poses significant challenges due to treatment selection bias. Existing methods address this bias by minimizi…
Rethinking the Diffusion Models for Numerical Tabular Data Imputation from the Perspective of Wasserstein Gradient Flow
Zhichao Chen, Haoxuan Li, Fangyikang Wang +5
Diffusion models (DMs) have gained attention in Missing Data Imputation (MDI), but there remain two long-neglected issues to be addressed: (1). Inaccurate Imputation, which arises…
Entire Space Counterfactual Learning for Reliable Content Recommendations
Hao Wang, Zhichao Chen, Zhaoran Liu +4
Post-click conversion rate (CVR) estimation is a fundamental task in developing effective recommender systems, yet it faces challenges from data sparsity and sample selection bias.…