Ruizhe Deng — accepted AI-conference papers

2026

BFTS: Thompson Sampling with Bayesian Additive Regression Trees

Ruizhe Deng, Bibhas Chakraborty, Ran Chen, Yan Shuo Tan

ICML 2026spotlight

We propose Bayesian Forest Thompson Sampling (BFTS), which performs Thompson sampling using arm-wise Bayesian Additive Regression Trees (BART) to model each action's mean reward and generate MCMC-based posterior draws for decision-making. We derive an information-theoretic Bayesian regret bound of o…

Cited by 1 Source ↗Scholar ↗

2026

UniDrag: Unified Multi-Field Prediction and Robust Shape Optimization for Vehicle Aerodynamics

Ye Liu, Shouyi Liu, Ding Wang, Huiyu Yang, Ruizhe DENG, Qian Li +6

ICML 2026poster

High-fidelity vehicle aerodynamics analysis is bottlenecked by costly CFD simulations. Neural surrogates accelerate prediction but lack inverse design capabilities, while existing generative optimization methods suffer from unstable convergence and frequent engineering constraint violations. We pres…

Cited by 0 Source ↗Scholar ↗