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Zhou Lu
Zhou Lu
PhD student at Princeton
Verified email at princeton.edu - Homepage
Title
Cited by
Cited by
Year
The expressive power of neural networks: A view from the width
Z Lu, H Pu, F Wang, Z Hu, L Wang
Advances in neural information processing systems 30, 2017
12592017
Towards certifying l-infinity robustness using neural networks with l-inf-dist neurons
B Zhang, T Cai, Z Lu, D He, L Wang
International Conference on Machine Learning, 12368-12379, 2021
68*2021
Boosting for control of dynamical systems
N Agarwal, N Brukhim, E Hazan, Z Lu
International Conference on Machine Learning, 96-103, 2020
22*2020
Projection-free adaptive regret with membership oracles
Z Lu, N Brukhim, P Gradu, E Hazan
International Conference on Algorithmic Learning Theory, 1055-1073, 2023
152023
Non-convex online learning via algorithmic equivalence
U Ghai, Z Lu, E Hazan
Advances in Neural Information Processing Systems 35, 22161-22172, 2022
152022
Adaptive online learning of quantum states
X Chen, E Hazan, T Li, Z Lu, X Wang, R Yang
Quantum 8, 1471, 2024
112024
Adaptive gradient methods with local guarantees
Z Lu, W Xia, S Arora, E Hazan
arXiv preprint arXiv:2203.01400, 2022
112022
Lower bounds for differentially private erm: Unconstrained and non-euclidean
D Liu, Z Lu
arXiv preprint arXiv:2105.13637, 2021
6*2021
A theory of multimodal learning
Z Lu
Advances in Neural Information Processing Systems 36, 57244-57255, 2023
52023
On the computational efficiency of adaptive and dynamic regret minimization
Z Lu, E Hazan
arXiv preprint arXiv:2207.00646, 2022
32022
Non-uniform Online Learning: Towards Understanding Induction
Z Lu
arXiv preprint arXiv:2312.00170, 2023
22023
The Convergence Rate of SGD's Final Iterate: Analysis on Dimension Dependence
D Liu, Z Lu
arXiv preprint arXiv:2106.14588, 2021
22021
A note on the representation power of ghhs
Z Lu
arXiv preprint arXiv:2101.11286, 2021
22021
On the Computational Benefit of Multimodal Learning
Z Lu
International Conference on Algorithmic Learning Theory, 810-821, 2024
12024
Efficient adaptive regret minimization
Z Lu, E Hazan
arXiv preprint arXiv:2207.00646, 2022
12022
A Note on John Simplex with Positive Dilation
Z Lu
arXiv preprint arXiv:2012.03427, 2020
12020
The Benefit of Being Bayesian in Online Conformal Prediction
Z Zhang, Z Lu, H Yang
arXiv preprint arXiv:2410.02561, 2024
2024
Tight Rates for Bandit Control Beyond Quadratics
YJ Sun, Z Lu
arXiv preprint arXiv:2410.00993, 2024
2024
Online Control in Population Dynamics
N Golowich, E Hazan, Z Lu, D Rohatgi, YJ Sun
arXiv preprint arXiv:2406.01799, 2024
2024
Adaptive Regret for Bandits Made Possible: Two Queries Suffice
Z Lu, Q Zhang, X Chen, F Zhang, D Woodruff, E Hazan
arXiv preprint arXiv:2401.09278, 2024
2024
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