Ruosong Wang
Ruosong Wang
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Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks
S Arora, S Du, W Hu, Z Li, R Wang
International Conference on Machine Learning, 322-332, 2019
On exact computation with an infinitely wide neural net
S Arora, SS Du, W Hu, Z Li, R Salakhutdinov, R Wang
arXiv preprint arXiv:1904.11955, 2019
Is a good representation sufficient for sample efficient reinforcement learning?
SS Du, SM Kakade, R Wang, LF Yang
arXiv preprint arXiv:1910.03016, 2019
Graph neural tangent kernel: Fusing graph neural networks with graph kernels
SS Du, K Hou, B Póczos, R Salakhutdinov, R Wang, K Xu
arXiv preprint arXiv:1905.13192, 2019
Provably Efficient -learning with Function Approximation via Distribution Shift Error Checking Oracle
SS Du, Y Luo, R Wang, H Zhang
arXiv preprint arXiv:1906.06321, 2019
Harnessing the power of infinitely wide deep nets on small-data tasks
S Arora, SS Du, Z Li, R Salakhutdinov, R Wang, D Yu
arXiv preprint arXiv:1910.01663, 2019
Enhanced convolutional neural tangent kernels
Z Li, R Wang, D Yu, SS Du, W Hu, R Salakhutdinov, S Arora
arXiv preprint arXiv:1911.00809, 2019
Nearly optimal sampling algorithms for combinatorial pure exploration
L Chen, A Gupta, J Li, M Qiao, R Wang
Conference on Learning Theory, 482-534, 2017
Optimism in reinforcement learning with generalized linear function approximation
Y Wang, R Wang, SS Du, A Krishnamurthy
arXiv preprint arXiv:1912.04136, 2019
Exponential separations in the energy complexity of leader election
YJ Chang, T Kopelowitz, S Pettie, R Wang, W Zhan
ACM Transactions on Algorithms (TALG) 15 (4), 1-31, 2019
Provably efficient reinforcement learning with general value function approximation
R Wang, R Salakhutdinov, LF Yang
arXiv preprint arXiv:2005.10804, 2020
k-regret minimizing set: Efficient algorithms and hardness
W Cao, J Li, H Wang, K Wang, R Wang, R Chi-Wing Wong, W Zhan
20th International Conference on Database Theory (ICDT 2017), 2017
Agnostic Q-learning with function approximation in deterministic systems: Tight bounds on approximation error and sample complexity
SS Du, JD Lee, G Mahajan, R Wang
arXiv preprint arXiv:2002.07125, 2020
Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning?
R Wang, SS Du, LF Yang, SM Kakade
arXiv preprint arXiv:2005.00527, 2020
Tight Bounds for p Oblivious Subspace Embeddings
R Wang, DP Woodruff
Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete …, 2019
Efficient near-optimal algorithms for barter exchange
Z Jia, P Tang, R Wang, H Zhang
Proceedings of the 16th Conference on Autonomous Agents and MultiAgent …, 2017
Dimensionality reduction for tukey regression
K Clarkson, R Wang, D Woodruff
International Conference on Machine Learning, 1262-1271, 2019
On reward-free reinforcement learning with linear function approximation
R Wang, SS Du, LF Yang, R Salakhutdinov
arXiv preprint arXiv:2006.11274, 2020
Bounded rationality of restricted turing machines
L Chen, P Tang, R Wang
Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017
Efficient symmetric norm regression via linear sketching
Z Song, R Wang, LF Yang, H Zhang, P Zhong
arXiv preprint arXiv:1910.01788, 2019
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