| Toward understanding the importance of noise in training neural networks M Zhou, T Liu, Y Li, D Lin, E Zhou, T Zhao International Conference on Machine Learning, 7594-7602, 2019 | 97 | 2019 |
| Towards understanding the importance of shortcut connections in residual networks T Liu, M Chen, M Zhou, SS Du, E Zhou, T Zhao Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 2019 | 66 | 2019 |
| On computation and generalization of generative adversarial imitation learning M Chen, Y Wang, T Liu, Z Yang, X Li, Z Wang, T Zhao International Conference on Learning Representations. 2019., 2020 | 49 | 2020 |
| Risk quantification in stochastic simulation under input uncertainty H Zhu, T Liu, E Zhou ACM Transactions on Modeling and Computer Simulation (TOMACS) 30 (1), 1-24, 2020 | 41 | 2020 |
| A Diffusion Approximation Theory of Momentum Stochastic Gradient Descent in Nonconvex Optimization T Liu, Z Chen, E Zhou, T Zhao Stochastic Systems 11 (4), 307-323, 2021 | 23* | 2021 |
| Online quantification of input uncertainty for parametric models E Zhou, T Liu 2018 Winter Simulation Conference (WSC), 1587-1598, 2018 | 17 | 2018 |
| Let models speak ciphers: Multiagent debate through embeddings C Pham, B Liu, Y Yang, Z Chen, T Liu, J Yuan, BA Plummer, Z Wang, ... arXiv preprint arXiv:2310.06272, 2023 | 14 | 2023 |
| Noisy gradient descent converges to flat minima for nonconvex matrix factorization T Liu, Y Li, S Wei, E Zhou, T Zhao International Conference on Artificial Intelligence and Statistics, 1891-1899, 2021 | 13 | 2021 |
| Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization T Liu, S Li, J Shi, E Zhou, T Zhao International Conference on Neural Information Processing Systems. 2018., 2018 | 13* | 2018 |
| Bayesian learning model predictive control for process-aware source seeking Y Li, T Liu, E Zhou, F Zhang IEEE Control Systems Letters 6, 692-697, 2021 | 12 | 2021 |
| PathFlow: A normalizing flow generator that finds transition paths T Liu, W Gao, Z Wang, C Wang Uncertainty in Artificial Intelligence, 1232-1242, 2022 | 9 | 2022 |
| Bayesian stochastic gradient descent for stochastic optimization with streaming input data T Liu, Y Lin, E Zhou SIAM Journal on Optimization 34 (1), 389-418, 2024 | 8 | 2024 |
| Online quantification of input model uncertainty by two-layer importance sampling T Liu, E Zhou arXiv preprint arXiv:1912.11172, 2019 | 7 | 2019 |
| Label inference attack against split learning under regression setting S Xie, X Yang, Y Yao, T Liu, T Wang, J Sun arXiv preprint arXiv:2301.07284, 2023 | 6 | 2023 |
| A Bayesian approach to online simulation optimization with streaming input data T Liu, Y Lin, E Zhou 2021 Winter Simulation Conference (WSC), 1-12, 2021 | 6 | 2021 |
| Simulation optimization by reusing past replications: Don’t be afraid of dependence T Liu, E Zhou 2020 Winter Simulation Conference (WSC), 2923-2934, 2020 | 6 | 2020 |
| Fast training of deep neural networks for speech recognition G Cong, B Kingsbury, CC Yang, T Liu ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 5 | 2020 |
| Differentially private multi-party data release for linear regression R Wu, X Yang, Y Yao, J Sun, T Liu, KQ Weinberger, C Wang arXiv preprint arXiv:2206.07998, 2022 | 4 | 2022 |
| Differentially private estimation of hawkes process S Zuo, T Liu, T Zhao, H Zha arXiv preprint arXiv:2209.07303, 2022 | 3 | 2022 |
| Machine learning force fields with data cost aware training A Bukharin, T Liu, S Wang, S Zuo, W Gao, W Yan, T Zhao International Conference on Machine Learning, 3219-3232, 2023 | 2 | 2023 |
Tuo ZhaoAssociate Professor, Georgia TechVerified email at gatech.edu
