Feng, Yunzhen

13 publications

ICLR 2025 Beyond Model Collapse: Scaling up with Synthesized Data Requires Verification Yunzhen Feng, Elvis Dohmatob, Pu Yang, Francois Charton, Julia Kempe

ICML 2025 PILAF: Optimal Human Preference Sampling for Reward Modeling Yunzhen Feng, Ariel Kwiatkowski, Kunhao Zheng, Julia Kempe, Yaqi Duan

ICLRW 2025 PILAF: Optimal Human Preference Sampling for Reward Modeling Yunzhen Feng, Ariel Kwiatkowski, Kunhao Zheng, Julia Kempe, Yaqi Duan

NeurIPS 2025 Spend Wisely: Maximizing Post-Training Gains in Iterative Synthetic Data Bootstrapping Pu Yang, Yunzhen Feng, Ziyuan Chen, Yuhang Wu, Zhuoyuan Li

ICLR 2025 Strong Model Collapse Elvis Dohmatob, Yunzhen Feng, Arjun Subramonian, Julia Kempe

ICML 2024 A Tale of Tails: Model Collapse as a Change of Scaling Laws Elvis Dohmatob, Yunzhen Feng, Pu Yang, Francois Charton, Julia Kempe

ICLRW 2024 A Tale of Tails: Model Collapse as a Change of Scaling Laws Yunzhen Feng, Elvis Dohmatob, Pu Yang, Francois Charton, Julia Kempe

TMLR 2024 Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks Yunzhen Feng, Tim G. J. Rudner, Nikolaos Tsilivis, Julia Kempe

ICMLW 2024 Beyond Model Collapse: Scaling up with Synthesized Data Requires Reinforcement Yunzhen Feng, Elvis Dohmatob, Pu Yang, Francois Charton, Julia Kempe

ICML 2024 Do Efficient Transformers Really Save Computation? Kai Yang, Jan Ackermann, Zhenyu He, Guhao Feng, Bohang Zhang, Yunzhen Feng, Qiwei Ye, Di He, Liwei Wang

ICLR 2024 Embarrassingly Simple Dataset Distillation Yunzhen Feng, Shanmukha Ramakrishna Vedantam, Julia Kempe

NeurIPSW 2023 Embarrassingly Simple Dataset Distillation Yunzhen Feng, Shanmukha Ramakrishna Vedantam, Julia Kempe

ICMLW 2021 Enhancing Certified Robustness via Smoothed Weighted Ensembling Chizhou Liu, Yunzhen Feng, Ranran Wang, Bin Dong