Robust Channel Tracking in Fast Fading MIMO channels. - Ranjitha Prasad; Krishnamurthy Giridhar (GLOBECOM, 2008)

Bayesian Learning for Joint Sparse OFDM Channel Estimation and Data Detection. - Ranjitha Prasad; Chandra R. Murthy (GLOBECOM, 2010)

Joint data detection and dominant singular mode estimation in time varying reciprocal MIMO systems. - Ranjitha Prasad; Bettagere Nagaraja Bharath; Chandra R. Murthy (ICASSP, 2011)

Cramér-Rao-Type Bounds for Sparse Bayesian Learning. - Ranjitha Prasad; Chandra R. Murthy (IEEE Trans. Signal Process., 2013)

Nested Sparse Bayesian Learning for block-sparse signals with intra-block correlation. - Ranjitha Prasad; Chandra R. Murthy; Bhaskar D. Rao (ICASSP, 2014)

Joint channel estimation and data detection in MIMO-OFDM systems using sparse Bayesian learning. - Ranjitha Prasad; Chandra R. Murthy (NCC, 2014)

Joint Approximately Sparse Channel Estimation and Data Detection in OFDM Systems Using Sparse Bayesian Learning. - Ranjitha Prasad; Chandra R. Murthy; Bhaskar D. Rao (IEEE Trans. Signal Process., 2014)

Online Recovery of Temporally Correlated Sparse Signals Using Multiple Measurement Vectors. - Geethu Joseph; Chandra R. Murthy; Ranjitha Prasad; Bhaskar D. Rao (GLOBECOM, 2015)

Sparse signal recovery in the presence of colored noise and rank-deficient noise covariance matrix: An SBL approach. - Vinuthna Vinjamuri; Ranjitha Prasad; Chandra R. Murthy (ICASSP, 2015)

Joint Channel Estimation and Data Detection in MIMO-OFDM Systems: A Sparse Bayesian Learning Approach. - Ranjitha Prasad; Chandra R. Murthy; Bhaskar D. Rao (IEEE Trans. Signal Process., 2015)

Lower Bounds on the Bayes Risk of the Bayesian BTL Model with Applications to Random Graphs. - Mine Alsan; Ranjitha Prasad; Vincent Y. F. Tan (CoRR, 2017)

Inference Algorithms for the Multiplicative Mixture Mallows Model. - Ranjitha Prasad; Vincent Y. F. Tan (SPCOM, 2018)

Unlabelled Sensing: A Sparse Bayesian Learning Approach. - Ranjitha Prasad (CoRR, 2018)

Lower Bounds on the Bayes Risk of the Bayesian BTL Model With Applications to Comparison Graphs. - Mine Alsan; Ranjitha Prasad; Vincent Y. F. Tan (IEEE J. Sel. Top. Signal Process., 2018)

CRESA: A Deep Learning Approach to Competing Risks, Recurrent Event Survival Analysis. - Garima Gupta; Vishal Sunder; Ranjitha Prasad; Gautam Shroff (PAKDD (2), 2019)

Variational Student: Learning Compact and Sparser Networks in Knowledge Distillation Framework. - Srinidhi Hegde; Ranjitha Prasad; Ramya Hebbalaguppe; Vishwajith Kumar (CoRR, 2019)

MetaCI: Meta-Learning for Causal Inference in a Heterogeneous Population. - Ankit Sharma; Garima Gupta; Ranjitha Prasad; Arnab Chatterjee; Lovekesh Vig; Gautam Shroff (CoRR, 2019)

MultiMBNN: Matched and Balanced Causal Inference with Neural Networks. - Ankit Sharma; Garima Gupta; Ranjitha Prasad; Arnab Chatterjee; Lovekesh Vig; Gautam Shroff (ESANN, 2020)

Variational Student: Learning Compact and Sparser Networks In Knowledge Distillation Framework. - Srinidhi Hegde; Ranjitha Prasad; Ramya Hebbalaguppe; Vishwajeet Kumar (ICASSP, 2020)

CAMTA: Causal Attention Model for Multi-touch Attribution. - Sachin Kumar; Garima Gupta; Ranjitha Prasad; Arnab Chatterjee; Lovekesh Vig; Gautam Shroff (ICDM (Workshops), 2020)

Hi-CI: Deep Causal Inference in High Dimensions. - Ankit Sharma; Garima Gupta; Ranjitha Prasad; Arnab Chatterjee; Lovekesh Vig; Gautam Shroff (CD@KDD, 2020)

CAMTA: Casual Attention Model for Multi-touch Attribution. - Sachin Kumar; Garima Gupta; Ranjitha Prasad; Arnab Chatterjee; Lovekesh Vig; Gautam Shroff (CoRR, 2020)

DAGSurv: Directed Ayclic Graph Based Survival Analysis Using Deep Neural Networks. - Ansh Kumar Sharma; Rahul Kukreja; Ranjitha Prasad; Shilpa Rao 001 (ACML, 2021)

B-Small: A Bayesian Neural Network Approach to Sparse Model-Agnostic Meta-Learning. - Anish Madan; Ranjitha Prasad (ICASSP, 2021)

Path-Aware OMP Algorithms for Provenance Recovery in Vehicular Networks. - Shilpi Mishra; J. Harshan; Ranjitha Prasad (CoRR, 2021)

Locally Interpretable Model Agnostic Explanations using Gaussian Processes. - Aditya Saini; Ranjitha Prasad (CoRR, 2021)

DAGSurv: Directed Acyclic Graph Based Survival Analysis Using Deep Neural Networks. - Ansh Kumar Sharma; Rahul Kukreja; Ranjitha Prasad; Shilpa Rao 001 (CoRR, 2021)

Select Wisely and Explain: Active Learning and Probabilistic Local Post-hoc Explainability. - Aditya Saini; Ranjitha Prasad (AIES, 2022)

Path-Aware OMP Algorithms for Provenance Recovery in Wireless Networks. - Shilpi Mishra; J. Harshan; Ranjitha Prasad (VTC Spring, 2022)

CLIMAX: An Exploration of Classifier-Based Contrastive Explanations. - Praharsh Nanavati; Ranjitha Prasad (CogMI, 2023)

Over-the-air Clustered Wireless Federated Learning. - Ayush Madhan-Sohini; Divin Dominic; Nazreen Shah; Ranjitha Prasad (GLOBECOM (Workshops), 2023)

Deep Survival Analysis and Counterfactual Inference Using Balanced Representations. - Muskan Gupta; Gokul Kannan; Ranjitha Prasad; Garima Gupta (ICASSP, 2023)

Exploiting Sparsity in Over-parameterized Federated Learning over Multiple Access Channels. - Gagandeep Kaur; Ranjitha Prasad (COMAD/CODS, 2024)

Continual Federated Learning with Aggregated Gradients. - Satish Kumar Keshri; Nazreen Shah; Ranjitha Prasad (COMAD/CODS (December), 2024)

Enhancing Federated Learning robustness in Wireless Networks. - Zubair Shaban; Ranjitha Prasad (COMAD/CODS, 2024)

Federated Probabilistic Representation Learning using Information Geometric Approach. - Nazreen Shah; Ranjitha Prasad (COMAD/CODS (December), 2024)

Federated Learning for Wireless Applications: A Prototype. - Varun Laxman Muttepawar; Arjun Mehra; Zubair Shaban; Ranjitha Prasad; Jagadeesh Harshan (COMSNETS, 2024)

Over The Air Federated Learning in the Presence of Impulsive Noise. - Zubair Shaban; Ranjitha Prasad; Pooja Kumari (ICASSP Workshops, 2024)

Importance Sampling Based Federated Unsupervised Representation Learning. - Nazreen Shah; Prachi Goyal; Ranjitha Prasad (ICASSP, 2024)

Seeing is Believing: A Federated Learning Based Prototype to Detect Wireless Injection Attacks. - Aadil Hussain; Nitheesh Gundapu; Sarang Drugkar; Suraj Kiran; J. Harshan; Ranjitha Prasad (VTC Spring, 2024)

On Homomorphic Encryption Based Strategies for Class Imbalance in Federated Learning. - Arpit Guleria; J. Harshan; Ranjitha Prasad; B. N. Bharath 001 (CoRR, 2024)

On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients. - Satish Kumar Keshri; Nazreen Shah; Ranjitha Prasad (AISTATS, 2025)

Robust Over-The-Air Federated Learning In Heterogeneous Networks. - Zubair Shaban; Nazreen Shah; Ranjitha Prasad (ICASSP, 2025)

Noise Resilient Over-The-Air Federated Learning In Heterogeneous Wireless Networks. - Zubair Shaban; Nazreen Shah; Ranjitha Prasad (CoRR, 2025)

Representation Learning Preserving Ignorability and Covariate Matching for Treatment Effects. - Praharsh Nanavati; Ranjitha Prasad; Karthikeyan Shanmugam (CoRR, 2025)