Learning Powers of Poisson Binomial Distributions. - Dimitris Fotakis 001; Vasilis Kontonis; Piotr Krysta; Paul G. Spirakis (CoRR, 2017)

Efficient Truncated Statistics with Unknown Truncation. - Vasilis Kontonis; Christos Tzamos; Manolis Zampetakis (FOCS, 2019)

Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Nikos Zarifis (COLT, 2020)

Learning Halfspaces with Massart Noise Under Structured Distributions. - Ilias Diakonikolas; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (COLT, 2020)

Non-Convex SGD Learns Halfspaces with Adversarial Label Noise. - Ilias Diakonikolas; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (NeurIPS, 2020)

Learning Halfspaces with Tsybakov Noise. - Ilias Diakonikolas; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (CoRR, 2020)

A Polynomial Time Algorithm for Learning Halfspaces with Tsybakov Noise. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (CoRR, 2020)

Convergence and Sample Complexity of SGD in GANs. - Vasilis Kontonis; Sihan Liu; Christos Tzamos (CoRR, 2020)

A Statistical Taylor Theorem and Extrapolation of Truncated Densities. - Constantinos Daskalakis; Vasilis Kontonis; Christos Tzamos; Emmanouil Zampetakis (COLT, 2021)

Agnostic Proper Learning of Halfspaces under Gaussian Marginals. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (COLT, 2021)

Efficient Algorithms for Learning from Coarse Labels. - Dimitris Fotakis 001; Alkis Kalavasis; Vasilis Kontonis; Christos Tzamos (COLT, 2021)

Learning Online Algorithms with Distributional Advice. - Ilias Diakonikolas; Vasilis Kontonis; Christos Tzamos; Ali Vakilian; Nikos Zarifis (ICML, 2021)

Efficiently learning halfspaces with Tsybakov noise. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (STOC, 2021)

Threshold Phenomena in Learning Halfspaces with Massart Noise. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (CoRR, 2021)

Learning a Single Neuron with Adversarial Label Noise via Gradient Descent. - Ilias Diakonikolas; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (COLT, 2022)

Learning General Halfspaces with Adversarial Label Noise via Online Gradient Descent. - Ilias Diakonikolas; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (ICML, 2022)

Linear Label Ranking with Bounded Noise. - Dimitris Fotakis 001; Alkis Kalavasis; Vasilis Kontonis; Christos Tzamos (NeurIPS, 2022)

Weighted Distillation with Unlabeled Examples. - Fotis Iliopoulos; Vasilis Kontonis; Cenk Baykal; Gaurav Menghani; Khoa Trinh; Erik Vee (NeurIPS, 2022)

Learning general halfspaces with general Massart noise under the Gaussian distribution. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (STOC, 2022)

Self-Directed Linear Classification. - Ilias Diakonikolas; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (COLT, 2023)

Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Method. - Constantine Caramanis; Dimitris Fotakis 001; Alkis Kalavasis; Vasilis Kontonis; Christos Tzamos (NeurIPS, 2023)

Efficient Testable Learning of Halfspaces with Adversarial Label Noise. - Ilias Diakonikolas; Daniel Kane 001; Vasilis Kontonis; Sihan Liu; Nikos Zarifis (NeurIPS, 2023)

SLaM: Student-Label Mixing for Distillation with Unlabeled Examples. - Vasilis Kontonis; Fotis Iliopoulos; Khoa Trinh; Cenk Baykal; Gaurav Menghani; Erik Vee (NeurIPS, 2023)

The Gain from Ordering in Online Learning. - Vasilis Kontonis; Mingchen Ma; Christos Tzamos (NeurIPS, 2023)

SLaM: Student-Label Mixing for Semi-Supervised Knowledge Distillation. - Vasilis Kontonis; Fotis Iliopoulos; Khoa Trinh; Cenk Baykal; Gaurav Menghani; Erik Vee (CoRR, 2023)

Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Methods. - Constantine Caramanis; Dimitris Fotakis 001; Alkis Kalavasis; Vasilis Kontonis; Christos Tzamos (CoRR, 2023)

Opinion Dynamics with Limited Information. - Dimitris Fotakis 001; Anthimos Vardis Kandiros; Vasilis Kontonis; Stratis Skoulakis (Algorithmica, 2023)

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

Active Learning with Simple Questions. - Vasilis Kontonis; Mingchen Ma; Christos Tzamos (COLT, 2024)

Agnostically Learning Multi-Index Models with Queries. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (FOCS, 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)

Active Classification with Few Queries under Misspecification. - Vasilis Kontonis; Mingchen Ma; Christos Tzamos (NeurIPS, 2024)

Super Non-singular Decompositions of Polynomials and Their Application to Robustly Learning Low-Degree PTFs. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Sihan Liu; Nikos Zarifis (STOC, 2024)

Online Learning of Halfspaces with Massart Noise. - Ilias Diakonikolas; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (CoRR, 2024)

Learning general Gaussian mixtures with efficient score matching. - Sitan Chen; Vasilis Kontonis; Kulin Shah (COLT, 2025)

Oracle efficient truncated statistics. - Konstantinos Karatapanis; Vasilis Kontonis; Christos Tzamos (ICLR, 2025)

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions. - Jaeyeon Kim; Kulin Shah; Vasilis Kontonis; Sham M. Kakade; Sitan Chen (CoRR, 2025)