Cikkek nyilvánosan hozzáférhető megbízással - Xuezhou ZhangTovábbi információ
Valahol hozzáférhető: 22
Adaptive reward-poisoning attacks against reinforcement learning
X Zhang, Y Ma, A Singla, X Zhu
International Conference on Machine Learning, 11225-11234, 2020
Megbízások: US National Science Foundation
Policy poisoning in batch reinforcement learning and control
Y Ma, X Zhang, W Sun, X Zhu
NIPS 2019, 2019
Megbízások: US National Science Foundation
Online Data Poisoning Attack
X Zhang, L Lessard, X Zhu
Learning for Dynamics and Control, 2020, 2019
Megbízások: US National Science Foundation
Training set debugging using trusted items
X Zhang, X Zhu, SJ Wright
AAAI 2018, 2018
Megbízások: US National Science Foundation, US Department of Defense
Efficient reinforcement learning in block mdps: A model-free representation learning approach
X Zhang, Y Song, M Uehara, M Wang, A Agarwal, W Sun
International Conference on Machine Learning, 26517-26547, 2022
Megbízások: US National Science Foundation, US Department of Defense
Corruption-robust offline reinforcement learning
X Zhang, Y Chen, X Zhu, W Sun
International Conference on Artificial Intelligence and Statistics, 5757-5773, 2022
Megbízások: US National Science Foundation, US Department of Defense
Decentralized gossip-based stochastic bilevel optimization over communication networks
S Yang, X Zhang, M Wang
Advances in neural information processing systems 35, 238-252, 2022
Megbízások: US National Science Foundation, US Department of Defense
Robust policy gradient against strong data corruption
X Zhang, Y Chen, X Zhu, W Sun
International Conference on Machine Learning, 12391-12401, 2021
Megbízások: US National Science Foundation
An Optimal Control Approach to Sequential Machine Teaching
L Lessard, X Zhang, X Zhu
AISTATS 2019, 2019
Megbízások: US National Science Foundation
Provable benefits of representational transfer in reinforcement learning
A Agarwal, Y Song, W Sun, K Wang, M Wang, X Zhang
The Thirty Sixth Annual Conference on Learning Theory, 2114-2187, 2023
Megbízások: US National Science Foundation
Controllable and diverse text generation in e-commerce
H Shao, J Wang, H Lin, X Zhang, A Zhang, H Ji, T Abdelzaher
Proceedings of the Web Conference 2021, 2392-2401, 2021
Megbízások: US National Science Foundation, US Department of Defense
Provable defense against backdoor policies in reinforcement learning
S Bharti, X Zhang, A Singla, J Zhu
Advances in Neural Information Processing Systems 35, 14704-14714, 2022
Megbízások: US National Science Foundation, US Department of Defense
Byzantine-robust online and offline distributed reinforcement learning
Y Chen, X Zhang, K Zhang, M Wang, X Zhu
International Conference on Artificial Intelligence and Statistics, 3230-3269, 2023
Megbízások: US National Science Foundation, US Department of Defense
Off-policy fitted q-evaluation with differentiable function approximators: Z-estimation and inference theory
R Zhang, X Zhang, C Ni, M Wang
International Conference on Machine Learning, 26713-26749, 2022
Megbízások: US National Science Foundation, US Department of Defense
Teacher Improves Learning by Selecting a Training Subset
Y Ma, R Nowak, P Rigollet, X Zhang, X Zhu
AISTATS 2018, 2018
Megbízások: US National Science Foundation, US Department of Defense
Provably efficient representation learning with tractable planning in low-rank pomdp
J Guo, Z Li, H Wang, M Wang, Z Yang, X Zhang
International Conference on Machine Learning, 11967-11997, 2023
Megbízások: US National Science Foundation, US Department of Defense
Training set camouflage
A Sen, S Alfeld, X Zhang, A Vartanian, Y Ma, X Zhu.
Conference on Decision and Game Theory for Security (GameSec), 2018., 2018
Megbízások: US National Science Foundation
Bandit theory and thompson sampling-guided directed evolution for sequence optimization
H Yuan, C Ni, H Wang, X Zhang, L Cong, C Szepesvári, M Wang
Advances in Neural Information Processing Systems 35, 38291-38304, 2022
Megbízások: US National Science Foundation, US Department of Defense, US National …
Optimal estimation of policy gradient via double fitted iteration
C Ni, R Zhang, X Ji, X Zhang, M Wang
International Conference on Machine Learning, 16724-16783, 2022
Megbízások: US National Science Foundation, US Department of Defense
Learning adversarial low-rank markov decision processes with unknown transition and full-information feedback
C Zhao, R Yang, B Wang, X Zhang, S Li
Advances in Neural Information Processing Systems 36, 59107-59123, 2023
Megbízások: National Natural Science Foundation of China
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