Faster method for Deep Belief Network based Object classification using DWT. - Saurabh Sihag; Pranab Kumar Dutta (CoRR, 2015)

Secure parameter estimation: Fundamental tradeoffs. - Saurabh Sihag; Ali Tajer (GlobalSIP, 2017)

Sequential estimation of distributed parameters in networks. - Saurabh Sihag; Javad Heydari; Ali Tajer (CISS, 2018)

Non-Linear State Estimation in Power Systems Under Model Uncertainty. - Saurabh Sihag; Ali Tajer (GlobalSIP, 2018)

Distributed Estimation Under Network Model Uncertainty. - Saurabh Sihag; Ali Tajer (ICASSP, 2018)

Scalable Network Parameter Estimation in the Presence of Anomalies. - Saurabh Sihag; Ali Tajer (ICASSP, 2018)

Power System State Estimation Under Model Uncertainty. - Saurabh Sihag; Ali Tajer (IEEE J. Sel. Top. Signal Process., 2018)

GSP Analysis of Brain Imaging Data from Athletes with History of Multiple Concussions. - Saurabh Sihag; Sébastien Naze; Foad Taghdiri; Maria Carmela Tartaglia; James R. Kozloski (GlobalSIP, 2019)

Sample Complexity of Joint Structure Learning. - Saurabh Sihag; Ali Tajer (ICASSP, 2019)

Structure Learning of Similar Ising Models: Information-theoretic Bounds. - Saurabh Sihag; Ali Tajer (ISIT, 2019)

Structure Learning with Side Information: Sample Complexity. - Saurabh Sihag; Ali Tajer (NeurIPS, 2019)

Optimal Network Parameter Estimation: Single-Shot Exchange of Local Decisions. - Saurabh Sihag; Ali Tajer (IEEE Signal Process. Lett., 2019)

Approximate Recovery Of Ising Models with Side Information. - Saurabh Sihag; Ali Tajer (ISIT, 2020)

Secure Estimation Under Causative Attacks. - Saurabh Sihag; Ali Tajer (IEEE Trans. Inf. Theory, 2020)

Multimodal Dynamic Brain Connectivity Analysis Based on Graph Signal Processing for Former Athletes With History of Multiple Concussions. - Saurabh Sihag; Sébastien Naze; Foad Taghdiri; Charles Tator; Richard Wennberg; David J. Mikulis; Robin Green; Brenda Colella; Maria Carmela Tartaglia; James R. Kozloski (IEEE Trans. Signal Inf. Process. over Networks, 2020)

Learning Shared Subgraphs in Ising Model Pairs. - Burak Varici; Saurabh Sihag; Ali Tajer (AISTATS, 2021)

Two-Stage Graph-Constrained Group Testing: Theory and Application. - Saurabh Sihag; Ali Tajer; Urbashi Mitra (ICASSP, 2021)

Summary Markov Models for Event Sequences. - Debarun Bhattacharjya; Saurabh Sihag; Oktie Hassanzadeh; Liza Bialik (IJCAI, 2022)

coVariance Neural Networks. - Saurabh Sihag; Gonzalo Mateos; Corey McMillan; Alejandro Ribeiro (NeurIPS, 2022)

Adaptive Graph-Constrained Group Testing. - Saurabh Sihag; Ali Tajer; Urbashi Mitra (IEEE Trans. Signal Process., 2022)

Predicting Brain Age Using Transferable Covariance Neural Networks. - Saurabh Sihag; Gonzalo Mateos; Corey McMillan; Alejandro Ribeiro (ICASSP, 2023)

Explainable Brain Age Prediction using coVariance Neural Networks. - Saurabh Sihag; Gonzalo Mateos; Corey McMillan; Alejandro Ribeiro (NeurIPS, 2023)

Transferability of coVariance Neural Networks and Application to Interpretable Brain Age Prediction using Anatomical Features. - Saurabh Sihag; Gonzalo Mateos; Corey T. McMillan; Alejandro Ribeiro (CoRR, 2023)

Neural Tangent Kernels Motivate Graph Neural Networks with Cross-Covariance Graphs. - Shervin Khalafi; Saurabh Sihag; Alejandro Ribeiro (CoRR, 2023)

Learning Graph Structure from Convolutional Mixtures. - Max Wasserman; Saurabh Sihag; Gonzalo Mateos; Alejandro Ribeiro (Trans. Mach. Learn. Res., 2023)

Estimating Structurally Similar Graphical Models. - Saurabh Sihag; Ali Tajer (IEEE Trans. Inf. Theory, 2023)

Neural Tangent Kernels Motivate Cross-Covariance Graphs in Neural Networks. - Shervin Khalafi; Saurabh Sihag; Alejandro Ribeiro (ICML, 2024)

Towards a Foundation Model for Brain Age Prediction using coVariance Neural Networks. - Saurabh Sihag; Gonzalo Mateos; Alejandro Ribeiro (CoRR, 2024)

Explainable Brain Age Gap Prediction in Neurodegenerative Conditions Using Covariance Neural Networks. - Saurabh Sihag; Gonzalo Mateos; Alejandro Ribeiro (ISBI, 2025)