Shunhua Jiang

Orcid: 0000-0003-2226-7980

According to our database1, Shunhua Jiang authored at least 16 papers between 2017 and 2024.

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Bibliography

2024
Acceleration Meets Inverse Maintenance: Faster ℓ<sub>∞</sub>-Regression.
CoRR, 2024

2023
Near-Optimal Time-Energy Tradeoffs for Deterministic Leader Election.
ACM Trans. Algorithms, October, 2023

A Faster Interior-Point Method for Sum-of-Squares Optimization.
Algorithmica, September, 2023

The Complexity of Dynamic Least-Squares Regression.
Proceedings of the 64th IEEE Annual Symposium on Foundations of Computer Science, 2023

2022
Tight Revenue Gaps among Multiunit Mechanisms.
SIAM J. Comput., 2022

Dynamic Least-Squares Regression.
CoRR, 2022

The Energy Complexity of Las Vegas Leader Election.
Proceedings of the SPAA '22: 34th ACM Symposium on Parallelism in Algorithms and Architectures, Philadelphia, PA, USA, July 11, 2022

Solving SDP Faster: A Robust IPM Framework and Efficient Implementation.
Proceedings of the 63rd IEEE Annual Symposium on Foundations of Computer Science, 2022

Fast Graph Neural Tangent Kernel via Kronecker Sketching.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Solving Tall Dense SDPs in the Current Matrix Multiplication Time.
CoRR, 2021

A faster algorithm for solving general LPs.
Proceedings of the STOC '21: 53rd Annual ACM SIGACT Symposium on Theory of Computing, 2021

Near-Optimal Time-Energy Trade-Offs for Deterministic Leader Election.
Proceedings of the SPAA '21: 33rd ACM Symposium on Parallelism in Algorithms and Architectures, 2021

Tight Revenue Gaps among Multi-Unit Mechanisms.
Proceedings of the EC '21: The 22nd ACM Conference on Economics and Computation, 2021

2020
Faster Dynamic Matrix Inverse for Faster LPs.
CoRR, 2020

2019
A Faster External Memory Priority Queue with DecreaseKeys.
Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms, 2019

2017
Learning Gradient Descent: Better Generalization and Longer Horizons.
Proceedings of the 34th International Conference on Machine Learning, 2017


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