Puqian Wang

According to our database, Puqian Wang authored at least 10 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

ARO: A New Lens On Matrix Optimization For Large Models.

Wenbo Gong, Javier Zazo, Qijun Luo, Puqian Wang, James Hensman, Chao Ma

CoRR, February, 2026

2025

Robustly Learning Monotone Single-Index Models.

Puqian Wang, Nikos Zarifis, Ilias Diakonikolas, Jelena Diakonikolas

CoRR, August, 2025

Robustly Learning Monotone Generalized Linear Models via Data Augmentation.

Nikos Zarifis, Puqian Wang, Ilias Diakonikolas, Jelena Diakonikolas

Proceedings of the Thirty Eighth Annual Conference on Learning Theory, 2025

2024

Sample and Computationally Efficient Robust Learning of Gaussian Single-Index Models.

Puqian Wang, Nikos Zarifis, Ilias Diakonikolas, Jelena Diakonikolas

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Robustly Learning Single-Index Models via Alignment Sharpness.

Nikos Zarifis, Puqian Wang, Ilias Diakonikolas, Jelena Diakonikolas

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise.

Ilias Diakonikolas, Jelena Diakonikolas, Daniel Kane, Puqian Wang, Nikos Zarifis

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Robustly Learning a Single Neuron via Sharpness.

Puqian Wang, Nikos Zarifis, Ilias Diakonikolas, Jelena Diakonikolas

Proceedings of the International Conference on Machine Learning, 2023

Information-Computation Tradeoffs for Learning Margin Halfspaces with Random Classification Noise.

Ilias Diakonikolas, Jelena Diakonikolas, Daniel M. Kane, Puqian Wang, Nikos Zarifis

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Potential Function-Based Framework for Minimizing Gradients in Convex and Min-Max Optimization.

Jelena Diakonikolas, Puqian Wang

SIAM J. Optim., 2022

2021

Potential Function-based Framework for Making the Gradients Small in Convex and Min-Max Optimization.

Jelena Diakonikolas, Puqian Wang

CoRR, 2021