Behnoosh Zamanlooy

According to our database, Behnoosh Zamanlooy authored at least 11 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

On Privacy Leakage in Tabular Diffusion Models: Influential Factors, Attacker Knowledge, and Metrics.

Masoumeh Shafieinejad, David B. Emerson, Behnoosh Zamanlooy, Elaheh Bassak, Fatemeh Tavakoli, Sara Kodeiri, Marcelo Lotif, Xi He

CoRR, May, 2026

2025

Locally Private Sampling with Public Data.

Behnoosh Zamanlooy, Mario Díaz, Shahab Asoodeh

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

Machine Learning-Powered Course Allocation.

Ermis Soumalias, Behnoosh Zamanlooy, Jakob Weissteiner, Sven Seuken

Proceedings of the 25th ACM Conference on Economics and Computation, 2024

$\mathrm{E}_{\gamma}$-Mixing Time.

Behnoosh Zamanlooy, Shahab Asoodeh, Mario Díaz, Flávio P. Calmon

Proceedings of the IEEE International Symposium on Information Theory, 2024

2023

Strong Data Processing Inequalities for Locally Differentially Private Mechanisms.

Behnoosh Zamanlooy, Shahab Asoodeh

Proceedings of the IEEE International Symposium on Information Theory, 2023

2022

Do ReLU Networks Have An Edge When Approximating Compactly-Supported Functions?

Anastasis Kratsios, Behnoosh Zamanlooy

Trans. Mach. Learn. Res., 2022

Learning sub-patterns in piecewise continuous functions.

Anastasis Kratsios, Behnoosh Zamanlooy

Neurocomputing, 2022

Piecewise-Linear Activations or Analytic Activation Functions: Which Produce More Expressive Neural Networks?

Anastasis Kratsios, Behnoosh Zamanlooy

CoRR, 2022

Universal Approximation Under Constraints is Possible with Transformers.

Anastasis Kratsios, Behnoosh Zamanlooy, Tianlin Liu, Ivan Dokmanic

Proceedings of the Tenth International Conference on Learning Representations, 2022

2020

Overcoming The Limitations of Neural Networks in Composite-Pattern Learning with Architopes.

Anastasis Kratsios, Behnoosh Zamanlooy

CoRR, 2020

Architopes: An Architecture Modification for Composite Pattern Learning, Increased Expressiveness, and Reduced Training Time.

Anastasis Kratsios, Behnoosh Zamanlooy

CoRR, 2020