| Fantastic Generalization Measures and Where to Find Them Y Jiang, B Neyshabur, H Mobahi, D Krishnan, S Bengio International Conference on Learning Representations, 2019 | 677 | 2019 |
| Language as an Abstraction for Hierarchical Deep Reinforcement Learning Y Jiang, S Gu, K Murphy, C Finn Advances in Neural Information Processing Systems, 2019 | 255 | 2019 |
| Predicting the generalization gap in deep networks with margin distributions Y Jiang, D Krishnan, H Mobahi, S Bengio International Conference on Learning Representations, 2018 | 234 | 2018 |
| Observational Overfitting in Reinforcement Learning X Song, Y Jiang, S Tu, Y Du, B Neyshabur International Conference on Learning Representations, 2020 | 156 | 2020 |
| Assessing generalization of sgd via disagreement Y Jiang, V Nagarajan, C Baek, JZ Kolter International Conference on Learning Representations, 2021 | 126 | 2021 |
| Agreement-on-the-line: Predicting the performance of neural networks under distribution shift C Baek, Y Jiang, A Raghunathan, JZ Kolter Advances in Neural Information Processing Systems 35, 19274-19289, 2022 | 74 | 2022 |
| Neurips 2020 competition: Predicting generalization in deep learning Y Jiang, P Foret, S Yak, DM Roy, H Mobahi, GK Dziugaite, S Bengio, ... arXiv preprint arXiv:2012.07976, 2020 | 61 | 2020 |
| Permutation equivariant neural functionals A Zhou, K Yang, K Burns, A Cardace, Y Jiang, S Sokota, JZ Kolter, C Finn Advances in neural information processing systems 36, 2024 | 45 | 2024 |
| Adversarial grasp objects D Wang, D Tseng, P Li, Y Jiang, M Guo, M Danielczuk, J Mahler, ... 2019 IEEE 15th International Conference on Automation Science and …, 2019 | 33 | 2019 |
| Neural functional transformers A Zhou, K Yang, Y Jiang, K Burns, W Xu, S Sokota, JZ Kolter, C Finn Advances in neural information processing systems 36, 2024 | 30 | 2024 |
| Methods and analysis of the first competition in predicting generalization of deep learning Y Jiang, P Natekar, M Sharma, SK Aithal, D Kashyap, N Subramanyam, ... NeurIPS 2020 Competition and Demonstration Track, 170-190, 2021 | 29 | 2021 |
| On the importance of exploration for generalization in reinforcement learning Y Jiang, JZ Kolter, R Raileanu Advances in Neural Information Processing Systems 36, 2024 | 28* | 2024 |
| Learning options via compression Y Jiang, E Liu, B Eysenbach, JZ Kolter, C Finn Advances in Neural Information Processing Systems 35, 21184-21199, 2022 | 17 | 2022 |
| Language models are weak learners H Manikandan, Y Jiang, JZ Kolter Advances in Neural Information Processing Systems 36, 50907-50931, 2023 | 16 | 2023 |
| Understanding prompt engineering may not require rethinking generalization V Akinwande, Y Jiang, D Sam, JZ Kolter arXiv preprint arXiv:2310.03957, 2023 | 6 | 2023 |
| Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation Y He, A Robey, N Murata, Y Jiang, J Williams, GJ Pappas, H Hassani, ... arXiv preprint arXiv:2403.19103, 2024 | 4 | 2024 |
| Ask & Explore: Grounded Question Answering for Curiosity-Driven Exploration JN Kaur, Y Jiang, PP Liang arXiv preprint arXiv:2104.11902, 2021 | 3 | 2021 |
| On the Joint Interaction of Models, Data, and Features Y Jiang, C Baek, JZ Kolter arXiv preprint arXiv:2306.04793, 2023 | 2 | 2023 |
| Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws Y Jiang, A Zhou, Z Feng, S Malladi, JZ Kolter arXiv preprint arXiv:2410.11820, 2024 | | 2024 |
| Improving Generalization on the ProcGen Benchmark with Simple Architectural Changes and Scale A Jesson, Y Jiang arXiv preprint arXiv:2410.10905, 2024 | | 2024 |
Zico KolterCarnegie Mellon UniversityVerified email at cs.cmu.edu
