Arsen Vasilyan

According to our database, Arsen Vasilyan authored at least 26 papers between 2019 and 2026.

Collaborative distances:

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

2026

Proper Agnostic Learning of Functions of Halfspaces under Gaussian Marginals.

Sergei Tikhonov, Arsen Vasilyan

CoRR, May, 2026

Iterative Chow Filtering for Learning with Distribution Shift.

Gautam Chandrasekaran, Georgios Gkrinias, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan

CoRR, May, 2026

Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift.

Adam R. Klivans, Shyamal Patel, Konstantinos Stavropoulos, Arsen Vasilyan

CoRR, May, 2026

Learning AC⁰ Under Graphical Models.

Gautam Chandrasekaran, Jason Gaitonde, Ankur Moitra, Arsen Vasilyan

CoRR, April, 2026

Sandwiching Polynomials for Geometric Concepts with Low Intrinsic Dimension.

Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan

CoRR, February, 2026

A Fully Polynomial-Time Algorithm for Robustly Learning Halfspaces over the Hypercube.

Gautam Chandrasekaran, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan

Proceedings of the 58th Annual ACM Symposium on Theory of Computing, 2026

Testable Algorithms for Approximately Counting Edges and Triangles in Sublinear Time and Space.

Talya Eden, Ronitt Rubinfeld, Arsen Vasilyan

Proceedings of the 17th Innovations in Theoretical Computer Science Conference, 2026

2025

Testing Noise Assumptions of Learning Algorithms.

Surbhi Goel, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan

CoRR, January, 2025

Local Lipschitz Filters for Bounded-Range Functions with Applications to Arbitrary Real-Valued Functions.

Jane Lange, Ephraim Linder, Sofya Raskhodnikova, Arsen Vasilyan

Proceedings of the 2025 Annual ACM-SIAM Symposium on Discrete Algorithms, 2025

Robust learning of halfspaces under log-concave marginals.

Jane Lange, Arsen Vasilyan

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

The Power of Iterative Filtering for Supervised Learning with (Heavy) Contamination.

Adam R. Klivans, Konstantinos Stavropoulos, Kevin Tian, Arsen Vasilyan

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Learning Constant-Depth Circuits in Malicious Noise Models.

Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan

Proceedings of the Thirty Eighth Annual Conference on Learning Theory, 2025

2024

Tolerant Algorithms for Learning with Arbitrary Covariate Shift.

Surbhi Goel, Abhishek Shetty, Konstantinos Stavropoulos, Arsen Vasilyan

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Plant-and-Steal: Truthful Fair Allocations via Predictions.

Ilan Reuven Cohen, Alon Eden, Talya Eden, Arsen Vasilyan

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Efficient Discrepancy Testing for Learning with Distribution Shift.

Gautam Chandrasekaran, Adam R. Klivans, Vasilis Kontonis, Konstantinos Stavropoulos, Arsen Vasilyan

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

An Efficient Tester-Learner for Halfspaces.

Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Learning Intersections of Halfspaces with Distribution Shift: Improved Algorithms and SQ Lower Bounds.

Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan

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

Testable Learning with Distribution Shift.

Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan

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

2023

Local Lipschitz Filters for Bounded-Range Functions.

Jane Lange, Ephraim Linder, Sofya Raskhodnikova, Arsen Vasilyan

CoRR, 2023

Testing Distributional Assumptions of Learning Algorithms.

Ronitt Rubinfeld, Arsen Vasilyan

Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 2023

Tester-Learners for Halfspaces: Universal Algorithms.

Aravind Gollakota, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan

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

Agnostic proper learning of monotone functions: beyond the black-box correction barrier.

Jane Lange, Arsen Vasilyan

Proceedings of the 64th IEEE Annual Symposium on Foundations of Computer Science, 2023

2022

Properly learning monotone functions via local reconstruction.

Jane Lange, Ronitt Rubinfeld, Arsen Vasilyan

CoRR, 2022

Properly learning monotone functions via local correction.

Jane Lange, Ronitt Rubinfeld, Arsen Vasilyan

Proceedings of the 63rd IEEE Annual Symposium on Foundations of Computer Science, 2022

2020

Monotone Probability Distributions over the Boolean Cube Can Be Learned with Sublinear Samples.

Ronitt Rubinfeld, Arsen Vasilyan

Proceedings of the 11th Innovations in Theoretical Computer Science Conference, 2020

2019

Approximating the Noise Sensitivity of a Monotone Boolean Function.

Ronitt Rubinfeld, Arsen Vasilyan

Proceedings of the Approximation, 2019