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Tianyi Liu
Tianyi Liu
Georgia Institute of Technolodgy
Verified email at gatech.edu
Title
Cited by
Cited by
Year
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
972019
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
662019
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
492020
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
412020
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
172018
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
142023
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
132021
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
122021
PathFlow: A normalizing flow generator that finds transition paths
T Liu, W Gao, Z Wang, C Wang
Uncertainty in Artificial Intelligence, 1232-1242, 2022
92022
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
82024
Online quantification of input model uncertainty by two-layer importance sampling
T Liu, E Zhou
arXiv preprint arXiv:1912.11172, 2019
72019
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
62023
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
62021
Simulation optimization by reusing past replications: Don’t be afraid of dependence
T Liu, E Zhou
2020 Winter Simulation Conference (WSC), 2923-2934, 2020
62020
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
52020
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
42022
Differentially private estimation of hawkes process
S Zuo, T Liu, T Zhao, H Zha
arXiv preprint arXiv:2209.07303, 2022
32022
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
22023
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