GRADE-AO: Towards Near-Optimal Spatially-Coupled Codes With High Memories. - Siyi Yang; Ahmed H. Hareedy; Shyam Venkatasubramanian; A. Robert Calderbank; Lara Dolecek (ISIT, 2021)

Toward Data-Driven STAP Radar. - Shyam Venkatasubramanian; Chayut Wongkamthong; Mohammadreza Soltani; Bosung Kang; Sandeep Gogineni; Ali Pezeshki; Muralidhar Rangaswamy; Vahid Tarokh (CoRR, 2022)

Toward Data-Driven Radar STAP. - Shyam Venkatasubramanian; Sandeep Gogineni; Bosung Kang; Ali Pezeshki; Muralidhar Rangaswamy; Vahid Tarokh (CoRR, 2022)

Subspace Perturbation Analysis for Data-Driven Radar Target Localization. - Shyam Venkatasubramanian; Sandeep Gogineni; Bosung Kang; Ali Pezeshki; Muralidhar Rangaswamy; Vahid Tarokh (CoRR, 2023)

Random Linear Projections Loss for Hyperplane-Based Optimization in Regression Neural Networks. - Shyam Venkatasubramanian; Ahmed Aloui; Vahid Tarokh (CoRR, 2023)

Random Linear Projections Loss for Hyperplane-Based Optimization in Neural Networks. - Shyam Venkatasubramanian; Ahmed Aloui; Vahid Tarokh (UAI, 2024)

Data-Driven Target Localization: Benchmarking Gradient Descent Using the Cramér-Rao Bound. - Shyam Venkatasubramanian; Sandeep Gogineni; Bosung Kang; Ali Pezeshki; Muralidhar Rangaswamy; Vahid Tarokh (CoRR, 2024)

RASPNet: A Benchmark Dataset for Radar Adaptive Signal Processing Applications. - Shyam Venkatasubramanian; Bosung Kang; Ali Pezeshki; Muralidhar Rangaswamy; Vahid Tarokh (CoRR, 2024)

Learn2Mix: Training Neural Networks Using Adaptive Data Integration. - Shyam Venkatasubramanian; Vahid Tarokh (CoRR, 2024)

Steinmetz Neural Networks for Complex-Valued Data. - Shyam Venkatasubramanian; Ali Pezeshki; Vahid Tarokh (AISTATS, 2025)

An Information-Theoretic Lower Bound on the Generalization Error of Autoencoders. - Shyam Venkatasubramanian; Sean Moushegian; Ahmed Aloui; Vahid Tarokh (Trans. Mach. Learn. Res., 2025)