A Framework for Searching in Graphs in the Presence of Errors. - Dariusz Dereniowski; Stefan Tiegel; Przemyslaw Uznanski; Daniel Wolleb-Graf (SOSA, 2019)

Consistent Estimation for PCA and Sparse Regression with Oblivious Outliers. - Tommaso d'Orsi; Chih-Hung Liu 001; Rajai Nasser; Gleb Novikov; David Steurer; Stefan Tiegel (NeurIPS, 2021)

SoS Degree Reduction with Applications to Clustering and Robust Moment Estimation. - David Steurer; Stefan Tiegel (SODA, 2021)

Fast algorithm for overcomplete order-3 tensor decomposition. - Jingqiu Ding; Tommaso d'Orsi; Chih-Hung Liu 001; David Steurer; Stefan Tiegel (COLT, 2022)

Optimal SQ Lower Bounds for Learning Halfspaces with Massart Noise. - Rajai Nasser; Stefan Tiegel (COLT, 2022)

Hardness of Agnostically Learning Halfspaces from Worst-Case Lattice Problems. - Stefan Tiegel (COLT, 2023)

Private estimation algorithms for stochastic block models and mixture models. - Hongjie Chen 004; Vincent Cohen-Addad; Tommaso d'Orsi; Alessandro Epasto; Jacob Imola; David Steurer; Stefan Tiegel (NeurIPS, 2023)

Robust Mean Estimation Without Moments for Symmetric Distributions. - Gleb Novikov; David Steurer; Stefan Tiegel (NeurIPS, 2023)

Robust Mean Estimation Without a Mean: Dimension-Independent Error in Polynomial Time for Symmetric Distributions. - Gleb Novikov; David Steurer; Stefan Tiegel (CoRR, 2023)

Computational-Statistical Gaps for Improper Learning in Sparse Linear Regression. - Rares-Darius Buhai; Jingqiu Ding; Stefan Tiegel (COLT, 2024)

Improved Hardness Results for Learning Intersections of Halfspaces. - Stefan Tiegel (COLT, 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 (NeurIPS, 2024)

Testably Learning Polynomial Threshold Functions. - Lucas Slot; Stefan Tiegel; Manuel Wiedmer (NeurIPS, 2024)

Sample-Optimal Private Regression in Polynomial Time. - Prashanti Anderson; Ainesh Bakshi; Mahbod Majid; Stefan Tiegel (STOC, 2025)

Near-Optimal Time-Sparsity Trade-Offs for Solving Noisy Linear Equations. - Kiril Bangachev; Guy Bresler; Stefan Tiegel; Vinod Vaikuntanathan (STOC, 2025)

SoS Certifiability of Subgaussian Distributions and Its Algorithmic Applications. - Ilias Diakonikolas; Samuel B. Hopkins 001; Ankit Pensia; Stefan Tiegel (STOC, 2025)

SoS Certificates for Sparse Singular Values and Their Applications: Robust Statistics, Subspace Distortion, and More. - Ilias Diakonikolas; Samuel B. Hopkins 001; Ankit Pensia; Stefan Tiegel (STOC, 2025)

Improved Robust Estimation for Erdős-Rényi Graphs: The Sparse Regime and Optimal Breakdown Point. - Hongjie Chen 004; Jingqiu Ding; Yiding Hua; Stefan Tiegel (CoRR, 2025)