Aggregating Incomplete and Noisy Rankings. - Dimitris Fotakis 001; Alkis Kalavasis; Konstantinos Stavropoulos (AISTATS, 2021)

Learning and Covering Sums of Independent Random Variables with Unbounded Support. - Alkis Kalavasis; Konstantinos Stavropoulos; Emmanouil Zampetakis (NeurIPS, 2022)

Disjoint dijoins for classes of dicuts in finite and infinite digraphs. - J. Pascal Gollin; Karl Heuer; Konstantinos Stavropoulos (Comb. Theory, 2022)

Agnostically Learning Single-Index Models using Omnipredictors. - Aravind Gollakota; Parikshit Gopalan; Adam R. Klivans; Konstantinos Stavropoulos (NeurIPS, 2023)

Tester-Learners for Halfspaces: Universal Algorithms. - Aravind Gollakota; Adam R. Klivans; Konstantinos Stavropoulos; Arsen Vasilyan (NeurIPS, 2023)

Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension. - Gautam Chandrasekaran; Adam R. Klivans; Vasilis Kontonis; Raghu Meka; Konstantinos Stavropoulos (COLT, 2024)

Testable Learning with Distribution Shift. - Adam R. Klivans; Konstantinos Stavropoulos; Arsen Vasilyan (COLT, 2024)

Learning Intersections of Halfspaces with Distribution Shift: Improved Algorithms and SQ Lower Bounds. - Adam R. Klivans; Konstantinos Stavropoulos; Arsen Vasilyan (COLT, 2024)

An Efficient Tester-Learner for Halfspaces. - Aravind Gollakota; Adam R. Klivans; Konstantinos Stavropoulos; Arsen Vasilyan (ICLR, 2024)

Learning Noisy Halfspaces with a Margin: Massart is No Harder than Random. - Gautam Chandrasekaran; Vasilis Kontonis; Konstantinos Stavropoulos; Kevin Tian (NeurIPS, 2024)

Efficient Discrepancy Testing for Learning with Distribution Shift. - Gautam Chandrasekaran; Adam R. Klivans; Vasilis Kontonis; Konstantinos Stavropoulos; Arsen Vasilyan (NeurIPS, 2024)

Tolerant Algorithms for Learning with Arbitrary Covariate Shift. - Surbhi Goel; Abhishek Shetty; Konstantinos Stavropoulos; Arsen Vasilyan (NeurIPS, 2024)

Learning Constant-Depth Circuits in Malicious Noise Models. - Adam R. Klivans; Konstantinos Stavropoulos; Arsen Vasilyan (COLT, 2025)

Learning Neural Networks with Distribution Shift: Efficiently Certifiable Guarantees. - Gautam Chandrasekaran; Adam R. Klivans; Lin Lin Lee; Konstantinos Stavropoulos (ICLR, 2025)

Testing Noise Assumptions of Learning Algorithms. - Surbhi Goel; Adam R. Klivans; Konstantinos Stavropoulos; Arsen Vasilyan (CoRR, 2025)

The Power of Iterative Filtering for Supervised Learning with (Heavy) Contamination. - Adam R. Klivans; Konstantinos Stavropoulos; Kevin Tian; Arsen Vasilyan (CoRR, 2025)