Taming Non-stationary Bandits: A Bayesian Approach. - Vishnu Raj; Sheetal Kalyani (CoRR, 2017)

An aggregating strategy for shifting experts in discrete sequence prediction. - Vishnu Raj; Sheetal Kalyani (CoRR, 2017)

A Non-parametric Multi-stage Learning Framework for Cognitive Spectrum Access in IoT Networks. - Thulasi Tholeti; Vishnu Raj; Sheetal Kalyani (CoRR, 2018)

Backpropagating Through the Air: Deep Learning at Physical Layer Without Channel Models. - Vishnu Raj; Sheetal Kalyani (IEEE Commun. Lett., 2018)

Spectrum Access In Cognitive Radio Using a Two-Stage Reinforcement Learning Approach. - Vishnu Raj; Irene Dias; Thulasi Tholeti; Sheetal Kalyani (IEEE J. Sel. Top. Signal Process., 2018)

Blind decoding in α-Stable noise: An online learning approach. - Vishnu Raj; Sheetal Kalyani (CoRR, 2019)

Deep Reinforcement Learning based Blind mmWave MIMO Beam Alignment. - Vishnu Raj; Sheetal Kalyani (CoRR, 2020)

Understanding Learning Dynamics of Binary Neural Networks via Information Bottleneck. - Vishnu Raj; Nancy Nayak; Sheetal Kalyani (CoRR, 2020)

Leveraging Online Learning for CSS in Frugal IoT Network. - Nancy Nayak; Vishnu Raj; Sheetal Kalyani (IEEE Trans. Cogn. Commun. Netw., 2020)

Design of Communication Systems Using Deep Learning: A Variational Inference Perspective. - Vishnu Raj; Sheetal Kalyani (IEEE Trans. Cogn. Commun. Netw., 2020)

Beyond 5G: Leveraging Cell Free TDD Massive MIMO Using Cascaded Deep Learning. - Navaneet Athreya; Vishnu Raj; Sheetal Kalyani (IEEE Wirel. Commun. Lett., 2020)

Look beyond labels: Incorporating functional summary information in Bayesian neural networks. - Vishnu Raj; Tianyu Cui; Markus Heinonen; Pekka Marttinen (CoRR, 2022)

Deep Reinforcement Learning Based Blind mmWave MIMO Beam Alignment. - Vishnu Raj; Nancy Nayak; Sheetal Kalyani (IEEE Trans. Wirel. Commun., 2022)

Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach. - Vishnu Raj; Tianyu Cui; Markus Heinonen; Pekka Marttinen (AISTATS, 2023)

HAPNEST: efficient, large-scale generation and evaluation of synthetic datasets for genotypes and phenotypes. - Sophie Wharrie; Zhiyu Yang; Vishnu Raj; Remo Monti; Rahul Gupta; Ying Wang 074; Alicia Martin; Luke J. O'Connor; Samuel Kaski; Pekka Marttinen; Pier Francesco Palamara; Christoph Lippert; et al. (Bioinform., 2023)

VMS: Interactive Visualization to Support the Sensemaking and Selection of Predictive Models. - Chen He 003; Vishnu Raj; Hans Moen; Tommi Gröhn; Chen Wang 145; Laura-Maria Peltonen; Saila Koivusalo; Pekka Marttinen; Giulio Jacucci (IUI, 2024)

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model. - Hans Moen; Vishnu Raj; Andrius Vabalas; Markus Perola; Samuel Kaski; Andrea Ganna; Pekka Marttinen (CoRR, 2024)