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2020 – today
- 2025
[i17]Hongjie Chen, Jingqiu Ding, Yiding Hua, Stefan Tiegel:
Improved Robust Estimation for Erdős-Rényi Graphs: The Sparse Regime and Optimal Breakdown Point. CoRR abs/2503.03923 (2025)- [i16]Prashanti Anderson, Ainesh Bakshi, Mahbod Majid, Stefan Tiegel:
Sample-Optimal Private Regression in Polynomial Time. CoRR abs/2503.24321 (2025) - 2024
- [c12]Rares-Darius Buhai, Jingqiu Ding, Stefan Tiegel:
Computational-Statistical Gaps for Improper Learning in Sparse Linear Regression. COLT 2024: 752-771 - [c11]Stefan Tiegel:
Improved Hardness Results for Learning Intersections of Halfspaces. COLT 2024: 4764-4786 - [c10]Daniil Dmitriev, Rares-Darius Buhai, Stefan Tiegel, Alexander Wolters, Gleb Novikov, Amartya Sanyal, David Steurer, Fanny Yang:
Robust Mixture Learning when Outliers Overwhelm Small Groups. NeurIPS 2024 - [c9]Lucas Slot, Stefan Tiegel, Manuel Wiedmer:
Testably Learning Polynomial Threshold Functions. NeurIPS 2024 - [i15]Rares-Darius Buhai, Jingqiu Ding, Stefan Tiegel:
Computational-Statistical Gaps for Improper Learning in Sparse Linear Regression. CoRR abs/2402.14103 (2024) - [i14]Stefan Tiegel:
Improved Hardness Results for Learning Intersections of Halfspaces. CoRR abs/2402.15995 (2024) - [i13]Lucas Slot, Stefan Tiegel, Manuel Wiedmer:
Testably Learning Polynomial Threshold Functions. CoRR abs/2406.06106 (2024) - [i12]Daniil Dmitriev, Rares-Darius Buhai, Stefan Tiegel, Alexander Wolters, Gleb Novikov, Amartya Sanyal, David Steurer, Fanny Yang:
Robust Mixture Learning when Outliers Overwhelm Small Groups. CoRR abs/2407.15792 (2024) - [i11]Ilias Diakonikolas, Samuel B. Hopkins, Ankit Pensia, Stefan Tiegel:
SoS Certifiability of Subgaussian Distributions and its Algorithmic Applications. CoRR abs/2410.21194 (2024) - [i10]Kiril Bangachev, Guy Bresler, Stefan Tiegel, Vinod Vaikuntanathan:
Near-Optimal Time-Sparsity Trade-Offs for Solving Noisy Linear Equations. CoRR abs/2411.12512 (2024) - [i9]Ilias Diakonikolas, Samuel B. Hopkins, Ankit Pensia, Stefan Tiegel:
SoS Certificates for Sparse Singular Values and Their Applications: Robust Statistics, Subspace Distortion, and More. CoRR abs/2412.21203 (2024) - 2023
- [c8]Stefan Tiegel:
Hardness of Agnostically Learning Halfspaces from Worst-Case Lattice Problems. COLT 2023: 3029-3064 - [c7]Hongjie Chen, Vincent Cohen-Addad, Tommaso d'Orsi, Alessandro Epasto, Jacob Imola, David Steurer, Stefan Tiegel:
Private estimation algorithms for stochastic block models and mixture models. NeurIPS 2023 - [c6]Gleb Novikov, David Steurer, Stefan Tiegel:
Robust Mean Estimation Without Moments for Symmetric Distributions. NeurIPS 2023 - [i8]Hongjie Chen, Vincent Cohen-Addad, Tommaso d'Orsi, Alessandro Epasto, Jacob Imola, David Steurer, Stefan Tiegel:
Private estimation algorithms for stochastic block models and mixture models. CoRR abs/2301.04822 (2023) - [i7]Gleb Novikov, David Steurer, Stefan Tiegel:
Robust Mean Estimation Without a Mean: Dimension-Independent Error in Polynomial Time for Symmetric Distributions. CoRR abs/2302.10844 (2023) - 2022
- [c5]Rajai Nasser, Stefan Tiegel:
Optimal SQ Lower Bounds for Learning Halfspaces with Massart Noise. COLT 2022: 1047-1074 - [c4]Jingqiu Ding, Tommaso d'Orsi, Chih-Hung Liu, David Steurer
, Stefan Tiegel:
Fast algorithm for overcomplete order-3 tensor decomposition. COLT 2022: 3741-3799 - [i6]Rajai Nasser, Stefan Tiegel:
Optimal SQ Lower Bounds for Learning Halfspaces with Massart Noise. CoRR abs/2201.09818 (2022) - [i5]Jingqiu Ding, Tommaso d'Orsi, Chih-Hung Liu, Stefan Tiegel, David Steurer:
Fast algorithm for overcomplete order-3 tensor decomposition. CoRR abs/2202.06442 (2022) - [i4]Stefan Tiegel:
Hardness of Agnostically Learning Halfspaces from Worst-Case Lattice Problems. CoRR abs/2207.14030 (2022) - 2021
- [c3]Tommaso d'Orsi, Chih-Hung Liu, Rajai Nasser, Gleb Novikov, David Steurer, Stefan Tiegel:
Consistent Estimation for PCA and Sparse Regression with Oblivious Outliers. NeurIPS 2021: 25427-25438 - [c2]David Steurer, Stefan Tiegel:
SoS Degree Reduction with Applications to Clustering and Robust Moment Estimation. SODA 2021: 374-393 - [i3]David Steurer, Stefan Tiegel:
SoS Degree Reduction with Applications to Clustering and Robust Moment Estimation. CoRR abs/2101.01509 (2021) - [i2]Tommaso d'Orsi, Chih-Hung Liu, Rajai Nasser, Gleb Novikov, David Steurer, Stefan Tiegel:
Consistent Estimation for PCA and Sparse Regression with Oblivious Outliers. CoRR abs/2111.02966 (2021)
2010 – 2019
- 2019
- [c1]Dariusz Dereniowski, Stefan Tiegel, Przemyslaw Uznanski, Daniel Wolleb-Graf:
A Framework for Searching in Graphs in the Presence of Errors. SOSA 2019: 4:1-4:17 - 2018
- [i1]Dariusz Dereniowski, Daniel Graf, Stefan Tiegel, Przemyslaw Uznanski:
A Framework for Searching in Graphs in the Presence of Errors. CoRR abs/1804.02075 (2018)
Coauthor Index
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