Stefan Tiegel

According to our database, Stefan Tiegel authored at least 18 papers between 2019 and 2025.

Collaborative distances:

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

2025

Improved Robust Estimation for Erdős-Rényi Graphs: The Sparse Regime and Optimal Breakdown Point.

Hongjie Chen, Jingqiu Ding, Yiding Hua, Stefan Tiegel

CoRR, March, 2025

SoS Certificates for Sparse Singular Values and Their Applications: Robust Statistics, Subspace Distortion, and More.

Ilias Diakonikolas, Samuel B. Hopkins, Ankit Pensia, Stefan Tiegel

Proceedings of the 57th Annual ACM Symposium on Theory of Computing, 2025

SoS Certifiability of Subgaussian Distributions and Its Algorithmic Applications.

Ilias Diakonikolas, Samuel B. Hopkins, Ankit Pensia, Stefan Tiegel

Proceedings of the 57th Annual ACM Symposium on Theory of Computing, 2025

Near-Optimal Time-Sparsity Trade-Offs for Solving Noisy Linear Equations.

Kiril Bangachev, Guy Bresler, Stefan Tiegel, Vinod Vaikuntanathan

Proceedings of the 57th Annual ACM Symposium on Theory of Computing, 2025

Sample-Optimal Private Regression in Polynomial Time.

Prashanti Anderson, Ainesh Bakshi, Mahbod Majid, Stefan Tiegel

Proceedings of the 57th Annual ACM Symposium on Theory of Computing, 2025

2024

Testably Learning Polynomial Threshold Functions.

Lucas Slot, Stefan Tiegel, Manuel Wiedmer

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Robust Mixture Learning when Outliers Overwhelm Small Groups.

Daniil Dmitriev, Rares-Darius Buhai, Stefan Tiegel, Alexander Wolters, Gleb Novikov, Amartya Sanyal, David Steurer, Fanny Yang

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Improved Hardness Results for Learning Intersections of Halfspaces.

Stefan Tiegel

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

Computational-Statistical Gaps for Improper Learning in Sparse Linear Regression.

Rares-Darius Buhai, Jingqiu Ding, Stefan Tiegel

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

2023

Robust Mean Estimation Without a Mean: Dimension-Independent Error in Polynomial Time for Symmetric Distributions.

Gleb Novikov, David Steurer, Stefan Tiegel

CoRR, 2023

Robust Mean Estimation Without Moments for Symmetric Distributions.

Gleb Novikov, David Steurer, Stefan Tiegel

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

Private estimation algorithms for stochastic block models and mixture models.

Hongjie Chen, Vincent Cohen-Addad, Tommaso d'Orsi, Alessandro Epasto, Jacob Imola, David Steurer, Stefan Tiegel

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

Hardness of Agnostically Learning Halfspaces from Worst-Case Lattice Problems.

Stefan Tiegel

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

2022

Optimal SQ Lower Bounds for Learning Halfspaces with Massart Noise.

Rajai Nasser, Stefan Tiegel

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

Fast algorithm for overcomplete order-3 tensor decomposition.

Jingqiu Ding, Tommaso d'Orsi, Chih-Hung Liu, David Steurer, Stefan Tiegel

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

2021

SoS Degree Reduction with Applications to Clustering and Robust Moment Estimation.

David Steurer, Stefan Tiegel

Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms, 2021

Consistent Estimation for PCA and Sparse Regression with Oblivious Outliers.

Tommaso d'Orsi, Chih-Hung Liu, Rajai Nasser, Gleb Novikov, David Steurer, Stefan Tiegel

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2019

A Framework for Searching in Graphs in the Presence of Errors.

Dariusz Dereniowski, Stefan Tiegel, Przemyslaw Uznanski, Daniel Wolleb-Graf

Proceedings of the 2nd Symposium on Simplicity in Algorithms, 2019