Potential Function-based Framework for Making the Gradients Small in Convex and Min-Max Optimization. - Jelena Diakonikolas; Puqian Wang (CoRR, 2021)

Potential Function-Based Framework for Minimizing Gradients in Convex and Min-Max Optimization. - Jelena Diakonikolas; Puqian Wang (SIAM J. Optim., 2022)

Information-Computation Tradeoffs for Learning Margin Halfspaces with Random Classification Noise. - Ilias Diakonikolas; Jelena Diakonikolas; Daniel M. Kane; Puqian Wang; Nikos Zarifis (COLT, 2023)

Robustly Learning a Single Neuron via Sharpness. - Puqian Wang; Nikos Zarifis; Ilias Diakonikolas; Jelena Diakonikolas (ICML, 2023)

Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise. - Ilias Diakonikolas; Jelena Diakonikolas; Daniel Kane 001; Puqian Wang; Nikos Zarifis (NeurIPS, 2023)

Robustly Learning Single-Index Models via Alignment Sharpness. - Nikos Zarifis; Puqian Wang; Ilias Diakonikolas; Jelena Diakonikolas (ICML, 2024)

Sample and Computationally Efficient Robust Learning of Gaussian Single-Index Models. - Puqian Wang; Nikos Zarifis; Ilias Diakonikolas; Jelena Diakonikolas (NeurIPS, 2024)

Robustly Learning Monotone Generalized Linear Models via Data Augmentation. - Nikos Zarifis; Puqian Wang; Ilias Diakonikolas; Jelena Diakonikolas (COLT, 2025)

Robustly Learning Monotone Single-Index Models. - Puqian Wang; Nikos Zarifis; Ilias Diakonikolas; Jelena Diakonikolas (CoRR, 2025)