Nikos Zarifis

Orcid: 0000-0003-0578-8514

Affiliations:

University of Wisconsin-Madison, WI, USA
National Technical University of Athens, School of Electrical and Computer Engineering, Greece

According to our database¹, Nikos Zarifis authored at least 30 papers between 2020 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Bibliography

2024

Efficient Testable Learning of General Halfspaces with Adversarial Label Noise.

[BibT_eX]

[DOI]

CoRR, 2024

Online Learning of Halfspaces with Massart Noise.

[BibT_eX]

[DOI]

CoRR, 2024

Super Non-singular Decompositions of Polynomials and Their Application to Robustly Learning Low-Degree PTFs.

[BibT_eX]

[DOI]

Proceedings of the 56th Annual ACM Symposium on Theory of Computing, 2024

Robustly Learning Single-Index Models via Alignment Sharpness.

[BibT_eX]

[DOI]

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

Agnostically Learning Multi-Index Models with Queries.

[BibT_eX]

[DOI]

Proceedings of the 65th IEEE Annual Symposium on Foundations of Computer Science, 2024

Statistical Query Lower Bounds for Learning Truncated Gaussians.

[BibT_eX]

[DOI]

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

Testable Learning of General Halfspaces with Adversarial Label Noise.

[BibT_eX]

[DOI]

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

2023

SQ Lower Bounds for Learning Bounded Covariance GMMs.

[BibT_eX]

[DOI]

CoRR, 2023

Efficient Testable Learning of Halfspaces with Adversarial Label Noise.

[BibT_eX]

[DOI]

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

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

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Self-Directed Linear Classification.

[BibT_eX]

[DOI]

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

SQ Lower Bounds for Learning Mixtures of Separated and Bounded Covariance Gaussians.

[BibT_eX]

[DOI]

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

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

[BibT_eX]

[DOI]

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

2022

Learning general halfspaces with general Massart noise under the Gaussian distribution.

[BibT_eX]

[DOI]

Proceedings of the STOC '22: 54th Annual ACM SIGACT Symposium on Theory of Computing, Rome, Italy, June 20, 2022

Learning General Halfspaces with Adversarial Label Noise via Online Gradient Descent.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Learning a Single Neuron with Adversarial Label Noise via Gradient Descent.

[BibT_eX]

[DOI]

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

Reallocating multiple facilities on the line.

[BibT_eX]

[DOI]

Dimitris Fotakis

Loukas Kavouras

Panagiotis Kostopanagiotis

Philip Lazos

Stratis Skoulakis

Nikos Zarifis

Theor. Comput. Sci., 2021

Threshold Phenomena in Learning Halfspaces with Massart Noise.

[BibT_eX]

[DOI]

CoRR, 2021

The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals.

[BibT_eX]

[DOI]

CoRR, 2021

Efficiently learning halfspaces with Tsybakov noise.

[BibT_eX]

[DOI]

Proceedings of the STOC '21: 53rd Annual ACM SIGACT Symposium on Theory of Computing, 2021

Learning Online Algorithms with Distributional Advice.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals in the SQ Model.

[BibT_eX]

[DOI]

Proceedings of the Conference on Learning Theory, 2021

Agnostic Proper Learning of Halfspaces under Gaussian Marginals.

[BibT_eX]

[DOI]

Proceedings of the Conference on Learning Theory, 2021

2020

A Polynomial Time Algorithm for Learning Halfspaces with Tsybakov Noise.

[BibT_eX]

[DOI]

CoRR, 2020

Learning Halfspaces with Tsybakov Noise.

[BibT_eX]

[DOI]

CoRR, 2020

Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals.

[BibT_eX]

[DOI]

Ilias Diakonikolas

Daniel Kane

Nikos Zarifis

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Non-Convex SGD Learns Halfspaces with Adversarial Label Noise.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning Halfspaces with Massart Noise Under Structured Distributions.

[BibT_eX]

[DOI]

Proceedings of the Conference on Learning Theory, 2020

Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks.

[BibT_eX]

[DOI]

Proceedings of the Conference on Learning Theory, 2020

Nikos Zarifis

Timeline

Legend:

Links

Online presence:

On csauthors.net:

Bibliography

Loading...