Real-time refinery optimization with reduced-order fluidized catalytic cracker model and surrogate-based trust region filter method. - Xinhe Chen; Kai Wu 004; Andrew Bai; Cornelius M. Masuku; Jacques Niederberger; Fábio S. Liporace; Lorenz T. Biegler (Comput. Chem. Eng., 2021)

Reducing Training Sample Memorization in GANs by Training with Memorization Rejection. - Andrew Bai; Cho-Jui Hsieh; Wendy Chi-wen Kan; Hsuan-Tien Lin (CoRR, 2022)

Concept Gradient: Concept-based Interpretation Without Linear Assumption. - Andrew Bai; Chih-Kuan Yeh; Neil Y. C. Lin; Pradeep Kumar Ravikumar; Cho-Jui Hsieh (ICLR, 2023)

Defending LLMs against Jailbreaking Attacks via Backtranslation. - Yihan Wang; Zhouxing Shi; Andrew Bai; Cho-Jui Hsieh (ACL (Findings), 2024)

Which Pretrain Samples to Rehearse when Finetuning Pretrained Models? - Andrew Bai; Chih-Kuan Yeh; Cho-Jui Hsieh; Ankur Taly (CoRR, 2024)

Embedding Space Selection for Detecting Memorization and Fingerprinting in Generative Models. - Jack He; Jianxing Zhao; Andrew Bai; Cho-Jui Hsieh (CoRR, 2024)

CLUE: Concept-Level Uncertainty Estimation for Large Language Models. - Yu-Hsiang Wang; Andrew Bai; Che-Ping Tsai; Cho-Jui Hsieh (CoRR, 2024)

On the loss of context-awareness in general instruction fine-tuning. - Yihan Wang; Andrew Bai; Nanyun Peng 001; Cho-Jui Hsieh (CoRR, 2024)

Data Attribution for Diffusion Models: Timestep-induced Bias in Influence Estimation. - Tong Xie; Haoyu Li; Andrew Bai; Cho-Jui Hsieh (Trans. Mach. Learn. Res., 2024)

An Efficient Rehearsal Scheme for Catastrophic Forgetting Mitigation during Multi-stage Fine-tuning. - Andrew Bai; Chih-Kuan Yeh; Cho-Jui Hsieh; Ankur Taly (NAACL (Findings), 2025)

Exploring Expert Failures Improves LLM Agent Tuning. - Li-Cheng Lan; Andrew Bai; Minhao Cheng; Cho-Jui Hsieh; Tianyi Zhou 001 (CoRR, 2025)

Unlabeled Data Improves Fine-Grained Image Zero-shot Classification with Multimodal LLMs. - Yunqi Hong; Sohyun An; Andrew Bai; Neil Y. C. Lin; Cho-Jui Hsieh (CoRR, 2025)

Concepts or Skills? Rethinking Instruction Selection for Multi-modal Models. - Andrew Bai; Justin Cui; Ruochen Wang; Cho-Jui Hsieh (CoRR, 2025)