Andrew Bai

According to our database, Andrew Bai authored at least 18 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Reward-Forcing: Autoregressive Video Generation with Reward Feedback.

Jingran Zhang, Ning Li, Yuanhao Ban, Andrew Bai, Justin Cui

CoRR, January, 2026

LoL: Longer than Longer, Scaling Video Generation to Hour.

Justin Cui, Jie Wu, Ming Li, Tao Yang, Xiaojie Li, Rui Wang, Andrew Bai, Yuanhao Ban, Cho-Jui Hsieh

CoRR, January, 2026

2025

When Distance Distracts: Representation Distance Bias in BT-Loss for Reward Models.

Tong Xie, Andrew Bai, Yuanhao Ban, Yunqi Hong, Haoyu Li, Cho-Jui Hsieh

CoRR, December, 2025

Uncertainty-Guided Selective Adaptation Enables Cross-Platform Predictive Fluorescence Microscopy.

Kai-Wen K. Yang, Andrew Bai, Alexandra Bermudez, Yunqi Hong, Zoe Latham, Iris Sloan, Michael Liu, Vishrut Goyal, Cho-Jui Hsieh, Neil Y. C. Lin

CoRR, November, 2025

Self-Forcing++: Towards Minute-Scale High-Quality Video Generation.

Justin Cui, Jie Wu, Ming Li, Tao Yang, Xiaojie Li, Rui Wang, Andrew Bai, Yuanhao Ban, Cho-Jui Hsieh

CoRR, October, 2025

Concepts or Skills? Rethinking Instruction Selection for Multi-modal Models.

Andrew Bai, Justin Cui, Ruochen Wang, Cho-Jui Hsieh

CoRR, August, 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, June, 2025

Exploring Expert Failures Improves LLM Agent Tuning.

Li-Cheng Lan, Andrew Bai, Minhao Cheng, Cho-Jui Hsieh, Tianyi Zhou

CoRR, April, 2025

An Efficient Rehearsal Scheme for Catastrophic Forgetting Mitigation during Multi-stage Fine-tuning.

Andrew Bai, Chih-Kuan Yeh, Cho-Jui Hsieh, Ankur Taly

Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2025, Albuquerque, New Mexico, USA, April 29, 2025

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

On the loss of context-awareness in general instruction fine-tuning.

Yihan Wang, Andrew Bai, Nanyun Peng, 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

Embedding Space Selection for Detecting Memorization and Fingerprinting in Generative Models.

Jack He, Jianxing Zhao, Andrew Bai, Cho-Jui Hsieh

CoRR, 2024

Which Pretrain Samples to Rehearse when Finetuning Pretrained Models?

Andrew Bai, Chih-Kuan Yeh, Cho-Jui Hsieh, Ankur Taly

CoRR, 2024

Defending LLMs against Jailbreaking Attacks via Backtranslation.

Yihan Wang, Zhouxing Shi, Andrew Bai, Cho-Jui Hsieh

Proceedings of the Findings of the Association for Computational Linguistics, 2024

2023

Concept Gradient: Concept-based Interpretation Without Linear Assumption.

Andrew Bai, Chih-Kuan Yeh, Neil Y. C. Lin, Pradeep Kumar Ravikumar, Cho-Jui Hsieh

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

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

2021

Real-time refinery optimization with reduced-order fluidized catalytic cracker model and surrogate-based trust region filter method.

Xinhe Chen, Kai Wu, Andrew Bai, Cornelius M. Masuku, Jacques Niederberger, Fábio S. Liporace, Lorenz T. Biegler

Comput. Chem. Eng., 2021