| On the opportunities and risks of foundation models R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021 | 1932 | 2021 |
| Isolating sources of disentanglement in variational autoencoders RTQ Chen, X Li, RB Grosse, DK Duvenaud Advances in neural information processing systems 31, 2018 | 1212 | 2018 |
| Alpaca: A strong, replicable instruction-following model R Taori, I Gulrajani, T Zhang, Y Dubois, X Li, C Guestrin, P Liang, ... Stanford Center for Research on Foundation Models. https://crfm. stanford …, 2023 | 773* | 2023 |
| Holistic evaluation of language models P Liang, R Bommasani, T Lee, D Tsipras, D Soylu, M Yasunaga, Y Zhang, ... arXiv preprint arXiv:2211.09110, 2022 | 321 | 2022 |
| Inference Suboptimality in Variational Autoencoders C Cremer, X Li, D Duvenaud International Conference on Machine Learning, 2018 | 271 | 2018 |
| Scalable gradients for stochastic differential equations X Li, TKL Wong, RTQ Chen, D Duvenaud International Conference on Artificial Intelligence and Statistics, 3870-3882, 2020 | 251 | 2020 |
| Large language models can be strong differentially private learners X Li, F Tramer, P Liang, T Hashimoto arXiv preprint arXiv:2110.05679, 2021 | 167 | 2021 |
| Neural sdes as infinite-dimensional gans P Kidger, J Foster, X Li, TJ Lyons International conference on machine learning, 5453-5463, 2021 | 91 | 2021 |
| Stochastic runge-kutta accelerates langevin monte carlo and beyond X Li, Y Wu, L Mackey, MA Erdogdu Advances in neural information processing systems 32, 2019 | 64 | 2019 |
| Alpacafarm: A simulation framework for methods that learn from human feedback Y Dubois, X Li, R Taori, T Zhang, I Gulrajani, J Ba, C Guestrin, P Liang, ... arXiv preprint arXiv:2305.14387, 2023 | 55 | 2023 |
| Scalable gradients and variational inference for stochastic differential equations X Li, TKL Wong, RTQ Chen, DK Duvenaud Symposium on Advances in Approximate Bayesian Inference, 1-28, 2020 | 47 | 2020 |
| Alpacaeval: An automatic evaluator of instruction-following models X Li, T Zhang, Y Dubois, R Taori, I Gulrajani, C Guestrin, P Liang, ... GitHub repository, 2023 | 46 | 2023 |
| Exploiting programmatic behavior of llms: Dual-use through standard security attacks D Kang, X Li, I Stoica, C Guestrin, M Zaharia, T Hashimoto arXiv preprint arXiv:2302.05733, 2023 | 43 | 2023 |
| Infinitely deep bayesian neural networks with stochastic differential equations W Xu, RTQ Chen, X Li, D Duvenaud International Conference on Artificial Intelligence and Statistics, 721-738, 2022 | 38 | 2022 |
| When does preconditioning help or hurt generalization? S Amari, J Ba, R Grosse, X Li, A Nitanda, T Suzuki, D Wu, J Xu arXiv preprint arXiv:2006.10732, 2020 | 31 | 2020 |
| Efficient and accurate gradients for neural sdes P Kidger, J Foster, XC Li, T Lyons Advances in Neural Information Processing Systems 34, 18747-18761, 2021 | 28 | 2021 |
| Foundation models and fair use P Henderson, X Li, D Jurafsky, T Hashimoto, MA Lemley, P Liang arXiv preprint arXiv:2303.15715, 2023 | 25 | 2023 |
| When Does Differentially Private Learning Not Suffer in High Dimensions? X Li, D Liu, TB Hashimoto, HA Inan, J Kulkarni, YT Lee, A Guha Thakurta Advances in Neural Information Processing Systems 35, 28616-28630, 2022 | 24 | 2022 |
| Exploring the limits of differentially private deep learning with group-wise clipping J He, X Li, D Yu, H Zhang, J Kulkarni, YT Lee, A Backurs, N Yu, J Bian arXiv preprint arXiv:2212.01539, 2022 | 16 | 2022 |
| Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe X Yue, HA Inan, X Li, G Kumar, J McAnallen, H Sun, D Levitan, R Sim arXiv preprint arXiv:2210.14348, 2022 | 16 | 2022 |
Tatsunori HashimotoAssistant Professor, StanfordVerified email at stanford.edu
