Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Nikos Zarifis (COLT, 2020)

Learning Halfspaces with Massart Noise Under Structured Distributions. - Ilias Diakonikolas; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (COLT, 2020)

Non-Convex SGD Learns Halfspaces with Adversarial Label Noise. - Ilias Diakonikolas; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (NeurIPS, 2020)

Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals. - Ilias Diakonikolas; Daniel Kane 001; Nikos Zarifis (NeurIPS, 2020)

Learning Halfspaces with Tsybakov Noise. - Ilias Diakonikolas; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (CoRR, 2020)

A Polynomial Time Algorithm for Learning Halfspaces with Tsybakov Noise. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (CoRR, 2020)

Agnostic Proper Learning of Halfspaces under Gaussian Marginals. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (COLT, 2021)

The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals in the SQ Model. - Ilias Diakonikolas; Daniel M. Kane; Thanasis Pittas; Nikos Zarifis (COLT, 2021)

Learning Online Algorithms with Distributional Advice. - Ilias Diakonikolas; Vasilis Kontonis; Christos Tzamos; Ali Vakilian; Nikos Zarifis (ICML, 2021)

Efficiently learning halfspaces with Tsybakov noise. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (STOC, 2021)

The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals. - Ilias Diakonikolas; Daniel M. Kane; Thanasis Pittas; Nikos Zarifis (CoRR, 2021)

Threshold Phenomena in Learning Halfspaces with Massart Noise. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (CoRR, 2021)

Reallocating multiple facilities on the line. - Dimitris Fotakis 001; Loukas Kavouras; Panagiotis Kostopanagiotis; Philip Lazos; Stratis Skoulakis; Nikos Zarifis (Theor. Comput. Sci., 2021)

Learning a Single Neuron with Adversarial Label Noise via Gradient Descent. - Ilias Diakonikolas; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (COLT, 2022)

Learning General Halfspaces with Adversarial Label Noise via Online Gradient Descent. - Ilias Diakonikolas; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (ICML, 2022)

Learning general halfspaces with general Massart noise under the Gaussian distribution. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (STOC, 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)

SQ Lower Bounds for Learning Mixtures of Separated and Bounded Covariance Gaussians. - Ilias Diakonikolas; Daniel M. Kane; Thanasis Pittas; Nikos Zarifis (COLT, 2023)

Self-Directed Linear Classification. - Ilias Diakonikolas; Vasilis Kontonis; Christos Tzamos; 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)

Efficient Testable Learning of Halfspaces with Adversarial Label Noise. - Ilias Diakonikolas; Daniel Kane 001; Vasilis Kontonis; Sihan Liu; Nikos Zarifis (NeurIPS, 2023)

SQ Lower Bounds for Learning Bounded Covariance GMMs. - Ilias Diakonikolas; Daniel M. Kane; Thanasis Pittas; Nikos Zarifis (CoRR, 2023)

Testable Learning of General Halfspaces with Adversarial Label Noise. - Ilias Diakonikolas; Daniel M. Kane; Sihan Liu; Nikos Zarifis (COLT, 2024)

Statistical Query Lower Bounds for Learning Truncated Gaussians. - Ilias Diakonikolas; Daniel M. Kane; Thanasis Pittas; Nikos Zarifis (COLT, 2024)

Agnostically Learning Multi-Index Models with Queries. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (FOCS, 2024)

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

Reliable Learning of Halfspaces under Gaussian Marginals. - Ilias Diakonikolas; Lisheng Ren; Nikos Zarifis (NeurIPS, 2024)

A Near-optimal Algorithm for Learning Margin Halfspaces with Massart Noise. - Ilias Diakonikolas; Nikos Zarifis (NeurIPS, 2024)

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

Super Non-singular Decompositions of Polynomials and Their Application to Robustly Learning Low-Degree PTFs. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Sihan Liu; Nikos Zarifis (STOC, 2024)

Online Learning of Halfspaces with Massart Noise. - Ilias Diakonikolas; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (CoRR, 2024)

Efficient Testable Learning of General Halfspaces with Adversarial Label Noise. - Ilias Diakonikolas; Daniel M. Kane; Sihan Liu; Nikos Zarifis (CoRR, 2024)

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

Robust Learning of Multi-index Models via Iterative Subspace Approximation. - Ilias Diakonikolas; Giannis Iakovidis; Daniel M. Kane; Nikos Zarifis (CoRR, 2025)

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