Jiang, Yiding

22 publications

ICLR 2025 Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws Yiding Jiang, Allan Zhou, Zhili Feng, Sadhika Malladi, J Zico Kolter

TMLR 2025 Automated Black-Box Prompt Engineering for Personalized Text-to-Image Generation Yutong He, Alexander Robey, Naoki Murata, Yiding Jiang, Joshua Nathaniel Williams, George J. Pappas, Hamed Hassani, Yuki Mitsufuji, Ruslan Salakhutdinov, J Zico Kolter

NeurIPS 2025 Learning Parameterized Skills from Demonstrations Vedant Gupta, Haotian Fu, Calvin Luo, Yiding Jiang, George Konidaris

NeurIPS 2025 Safety Pretraining: Toward the Next Generation of Safe AI Pratyush Maini, Sachin Goyal, Dylan Sam, Alexander Robey, Yash Savani, Yiding Jiang, Andy Zou, Matt Fredrikson, Zachary Chase Lipton, J Zico Kolter

ICML 2025 Training a Generally Curious Agent Fahim Tajwar, Yiding Jiang, Abitha Thankaraj, Sumaita Sadia Rahman, J Zico Kolter, Jeff Schneider, Russ Salakhutdinov

ICLRW 2025 Training a Generally Curious Agent Fahim Tajwar, Yiding Jiang, Abitha Thankaraj, Sumaita Sadia Rahman, J Zico Kolter, Jeff Schneider, Ruslan Salakhutdinov

ICLR 2024 On the Joint Interaction of Models, Data, and Features Yiding Jiang, Christina Baek, J Zico Kolter

ICLR 2024 Understanding Prompt Engineering May Not Require Rethinking Generalization Victor Akinwande, Yiding Jiang, Dylan Sam, J Zico Kolter

NeurIPS 2023 Language Models Are Weak Learners Hariharan Manikandan, Yiding Jiang, J. Zico Kolter

ICMLW 2023 Language Models Are Weak Learners Hariharan Manikandan, Yiding Jiang, J Zico Kolter

NeurIPS 2023 Neural Functional Transformers Allan Zhou, Kaien Yang, Yiding Jiang, Kaylee Burns, Winnie Xu, Samuel Sokota, J. Zico Kolter, Chelsea Finn

NeurIPS 2023 On the Importance of Exploration for Generalization in Reinforcement Learning Yiding Jiang, J. Zico Kolter, Roberta Raileanu

NeurIPS 2023 Permutation Equivariant Neural Functionals Allan Zhou, Kaien Yang, Kaylee Burns, Adriano Cardace, Yiding Jiang, Samuel Sokota, J. Zico Kolter, Chelsea Finn

ICMLW 2023 Understanding Prompt Engineering Does Not Require Rethinking Generalization Victor Akinwande, Yiding Jiang, Dylan Sam, J Zico Kolter

NeurIPS 2022 Agreement-on-the-Line: Predicting the Performance of Neural Networks Under Distribution Shift Christina Baek, Yiding Jiang, Aditi Raghunathan, J. Zico Kolter

ICLR 2022 Assessing Generalization of SGD via Disagreement Yiding Jiang, Vaishnavh Nagarajan, Christina Baek, J Zico Kolter

NeurIPS 2022 Learning Options via Compression Yiding Jiang, Evan Liu, Benjamin Eysenbach, J. Zico Kolter, Chelsea Finn

NeurIPSW 2022 Uncertainty-Driven Exploration for Generalization in Reinforcement Learning Yiding Jiang, J Zico Kolter, Roberta Raileanu

ICLR 2020 Fantastic Generalization Measures and Where to Find Them Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, Samy Bengio

ICLR 2020 Observational Overfitting in Reinforcement Learning Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, Behnam Neyshabur

NeurIPS 2019 Language as an Abstraction for Hierarchical Deep Reinforcement Learning YiDing Jiang, Shixiang Gu, Kevin P. Murphy, Chelsea Finn

ICLR 2019 Predicting the Generalization Gap in Deep Networks with Margin Distributions Yiding Jiang, Dilip Krishnan, Hossein Mobahi, Samy Bengio