Grouped sparse projection. - Nicolas Gillis; Riyasat Ohib; Sergey M. Plis; Vamsi K. Potluru (CoRR, 2019)

Single-Shot Pruning for Offline Reinforcement Learning. - Samin Yeasar Arnob; Riyasat Ohib; Sergey M. Plis; Doina Precup (CoRR, 2021)

Explicit Group Sparse Projection with Applications to Deep Learning and NMF. - Riyasat Ohib; Nicolas Gillis; Niccolò Dalmasso; Sameena Shah; Vamsi K. Potluru; Sergey M. Plis (Trans. Mach. Learn. Res., 2022)

SalientGrads: Sparse Models for Communication Efficient and Data Aware Distributed Federated Training. - Riyasat Ohib; Bishal Thapaliya; Pratyush Gaggenapalli; Jingyu Liu 001; Vince D. Calhoun; Sergey M. Plis (CoRR, 2023)

Learning low-dimensional dynamics from whole-brain data improves task capture. - Eloy Geenjaar; Donghyun Kim; Riyasat Ohib; Marlena Duda; Amrit Kashyap; Sergey M. Plis; Vince D. Calhoun (CoRR, 2023)

Efficient Reinforcement Learning by Discovering Neural Pathways. - Samin Yeasar Arnob; Riyasat Ohib; Sergey M. Plis; Amy Zhang 001; Alessandro Sordoni; Doina Precup (NeurIPS, 2024)

Unmasking Efficiency: Learning Salient Sparse Models in Non-IID Federated Learning. - Riyasat Ohib; Bishal Thapaliya; Gintare Karolina Dziugaite; Jingyu Liu 001; Vince D. Calhoun; Sergey M. Plis (CoRR, 2024)

Efficient federated learning for distributed neuroimaging data. - Bishal Thapaliya; Riyasat Ohib; Eloy Geenjaar; Jingyu Liu 001; Vince D. Calhoun; Sergey M. Plis (Frontiers Neuroinformatics, 2024)

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts. - Samin Yeasar Arnob; Zhan Su 002; Minseon Kim; Oleksiy Ostapenko; Riyasat Ohib; Esra'a Saleh; Doina Precup; Lucas Caccia; Alessandro Sordoni (CoRR, 2025)