Architopes: An Architecture Modification for Composite Pattern Learning, Increased Expressiveness, and Reduced Training Time. - Anastasis Kratsios; Behnoosh Zamanlooy (CoRR, 2020)

Overcoming The Limitations of Neural Networks in Composite-Pattern Learning with Architopes. - Anastasis Kratsios; Behnoosh Zamanlooy (CoRR, 2020)

Universal Approximation Under Constraints is Possible with Transformers. - Anastasis Kratsios; Behnoosh Zamanlooy; Tianlin Liu; Ivan Dokmanic (ICLR, 2022)

Piecewise-Linear Activations or Analytic Activation Functions: Which Produce More Expressive Neural Networks? - Anastasis Kratsios; Behnoosh Zamanlooy (CoRR, 2022)

Learning sub-patterns in piecewise continuous functions. - Anastasis Kratsios; Behnoosh Zamanlooy (Neurocomputing, 2022)

Do ReLU Networks Have An Edge When Approximating Compactly-Supported Functions? - Anastasis Kratsios; Behnoosh Zamanlooy (Trans. Mach. Learn. Res., 2022)

Strong Data Processing Inequalities for Locally Differentially Private Mechanisms. - Behnoosh Zamanlooy; Shahab Asoodeh (ISIT, 2023)

$ mathrm{E}_{ gamma}$-Mixing Time. - Behnoosh Zamanlooy; Shahab Asoodeh; Mario Díaz; Flávio P. Calmon (ISIT, 2024)

Machine Learning-Powered Course Allocation. - Ermis Soumalias; Behnoosh Zamanlooy; Jakob Weissteiner; Sven Seuken (EC, 2024)

Locally Private Sampling with Public Data. - Behnoosh Zamanlooy; Mario Díaz; Shahab Asoodeh (AISTATS, 2025)