Publications

Journals

• M. Alsan, R. Prasad, Vincent Y. F. Tan, Lower Bounds on the Bayes Risk of the Bayesian BTL Model with Applications to Random Graphs, IEEE Journal of Selected Topics in Signal Processing, Vol. 12, Oct 2018.
• R. Prasad, C. R. Murthy, and B. D. Rao, Joint Channel Estimation and Data Detection in MIMO-OFDM Systems: A Sparse Bayesian Learning Approach, accepted with mandatory minor revisions, IEEE Transactions on Signal Processing., Jun. 2015.
• R. Prasad, C. R. Murthy, and B. D. Rao, Joint Approximately Sparse Channel Estimation and Data Detection in OFDM Systems using Sparse Bayesian Learning, IEEE Transactions on Signal Processing., Jul. 2014.
• R. Prasad and C. R. Murthy, Cramer Rao-Type Bounds for Sparse Bayesian Learning, IEEE Transactions on Signal Processing, Vol. 61, No. 3, Jan. 2013, pp. 622 - 632.
• R. Prasad, Unlabelled Sensing: A Sparse Bayesian Learning Approach.

Conference Papers

• Ansh Sharma*, Rahul Kukreja*, Ranjitha Prasad, Shilpa D. Rao, 'DAGSurv: Directed Ayclic Graph Based Survival Analysis Using Deep Neural Networks', ACML 2021.
• Anish Madan, R. Prasad, 'B-SMALL: A Bayesian Neural Network Approach to Sparse Model-agnostic Metalearning', IEEE ICASSP 2021. (virtual conference)
• Sachin Kumar, G. Gupta, R. Prasad, A. Chatterjee, L. Vig, and G. Shroff, CAMTA: Causal Attention Model for Multi-touch Attribution, DMS Workshop, ICDM 2020.
• A. Sharma, G. Gupta, R. Prasad, A. Chatterjee, L. Vig, and G. Shroff, Hi-CI: Deep Causal Inference in High Dimensions, ACM SIGKDD Causal Discovery 2020 (PMLR).
• Srinidhi Hegde, Ranjitha Prasad, Ramya Hebbalaguppe, Vishwajeet Kumar, Variational Student: Learning Compact and Sparser networks in the Knowledge Distillation Framework, ICASSP 2020.
• A. Sharma, G. Gupta, R. Prasad, A. Chatterjee, L. Vig, and G. Shroff, MultiMBNN:Matched and Balanced Causal Inference with Neural Networks, ESANN, 2020.
• A. Sharma, G. Gupta, R. Prasad, A. Chatterjee, L. Vig, and G. Shroff, MetaCI: Meta-Learning for Causal Inference in a Heterogeneous Population, NeurIPS CausalML workshop, 2019.
• G. Gupta, Vishal S., R. Prasad, G. Shroff, CRESA: A Deep Learning Approach to Competing Risks, Recurrent Survival Analysis, PAKDD 2019.
• R. Prasad, Vincent Y. F. Tan, Inference Algorithms for the Multiplicative Mixture Mallows Model, SPCOM, Jul. 2018.
• G. Joseph, C. R. Murthy, R. Prasad, and B. D. Rao, Online Recovery of Temporally Correlated Sparse Signals Using Multiple Measurement Vectors, IEEE Global Telecommunication Conference (Globecom), San Diego, CA, USA, Dec. 2015.
• V. Vinuthna, R. Prasad, and C. R. Murthy, Sparse signal recovery in the presence of colored noise and rank-deficient noise covariance matrix: an SBL approach, Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brisbane, Australia, April 2015.
• R. Prasad, C. R. Murthy, and B. D. Rao, Nested Sparse Bayesian Learning for Block-Sparse Signals with Intra-Block Correlation, to appear in the Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Florence, Italy, May 2014.
• R. Prasad and C. R. Murthy, Joint Approximately Group Sparse Channel Estimation and Data Detection in MIMO-OFDM Systems Using Sparse Bayesian Learning, Accepted, Proc. IEEE National Conference on Communications (NCC), IIT Kanpur, Feb. 2014. (Best paper award, Communications track)
• R. Prasad and C. R. Murthy, Bayesian Learning for Joint Sparse OFDM Channel Estimation and Data Detection, Proc. IEEE Global Communications Conference (Globecom), Miami, USA, Dec. 2010.
• R. Prasad, B. N. Bharath, and C. R. Murthy, Joint Data Detection and Dominant Singular Mode Estimation in Time Varying Reciprocal MIMO Systems, Proc. IEEE International Conference on Speech, Acoustics and Signal Processing (ICASSP), Prague, Czech Republic, May 2011, pp. 3240 - 3243.