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ORCIDRanjitha Prasad
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2020 – today
- 2024
[c26]Zubair Shaban
, Ranjitha Prasad:
Enhancing Federated Learning robustness in Wireless Networks. COMAD/CODS 2024: 597-598- [c25]Gagandeep Kaur, Ranjitha Prasad:
Exploiting Sparsity in Over-parameterized Federated Learning over Multiple Access Channels. COMAD/CODS 2024: 605-606 - [c24]Varun Laxman Muttepawar, Arjun Mehra, Zubair Shaban, Ranjitha Prasad, Jagadeesh Harshan:
Federated Learning for Wireless Applications: A Prototype. COMSNETS 2024: 300-302 - [c23]Zubair Shaban, Ranjitha Prasad, Pooja Kumari:
Over The Air Federated Learning in the Presence of Impulsive Noise. ICASSP Workshops 2024: 429-433 - [c22]Nazreen Shah, Prachi Goyal, Ranjitha Prasad:
Importance Sampling Based Federated Unsupervised Representation Learning. ICASSP 2024: 7565-7569 - [c21]Aadil Hussain, Nitheesh Gundapu, Sarang Drugkar, Suraj Kiran, J. Harshan, Ranjitha Prasad:
Seeing is Believing: A Federated Learning Based Prototype to Detect Wireless Injection Attacks. VTC Spring 2024: 1-6 - 2023
- [c20]Praharsh Nanavati, Ranjitha Prasad:
CLIMAX: An Exploration of Classifier-Based Contrastive Explanations. CogMI 2023: 49-58 - [c19]Ayush Madhan-Sohini, Divin Dominic, Nazreen Shah, Ranjitha Prasad:
Over-the-air Clustered Wireless Federated Learning. GLOBECOM (Workshops) 2023: 203-208 - [c18]Muskan Gupta, Gokul Kannan, Ranjitha Prasad, Garima Gupta:
Deep Survival Analysis and Counterfactual Inference Using Balanced Representations. ICASSP 2023: 1-5 - [i16]Praharsh Nanavati, Ranjitha Prasad:
CLIMAX: An exploration of Classifier-Based Contrastive Explanations. CoRR abs/2307.00680 (2023) - [i15]Aadil Hussain, Nitheesh Gundapu, Sarang Drugkar, Suraj Kiran, J. Harshan, Ranjitha Prasad:
Seeing is Believing: A Federated Learning Based Prototype to Detect Wireless Injection Attacks. CoRR abs/2311.06564 (2023) - [i14]Varun Laxman Muttepawar, Arjun Mehra, Zubair Shaban, Ranjitha Prasad, Harshan Jagadeesh:
Federated Learning for Wireless Applications: A Prototype. CoRR abs/2312.08577 (2023) - 2022
- [c17]Aditya Saini, Ranjitha Prasad:
Select Wisely and Explain: Active Learning and Probabilistic Local Post-hoc Explainability. AIES 2022: 599-608 - [c16]Shilpi Mishra, J. Harshan, Ranjitha Prasad:
Path-Aware OMP Algorithms for Provenance Recovery in Wireless Networks. VTC Spring 2022: 1-7 - [i13]Ayush Madhan-Sohini, Divin Dominic, Nazreen Shah, Ranjitha Prasad:
Over-The-Air Clustered Wireless Federated Learning. CoRR abs/2211.03363 (2022) - 2021
- [c15]Ansh Kumar Sharma, Rahul Kukreja, Ranjitha Prasad, Shilpa Rao:
DAGSurv: Directed Ayclic Graph Based Survival Analysis Using Deep Neural Networks. ACML 2021: 1065-1080 - [c14]Anish Madan, Ranjitha Prasad:
B-Small: A Bayesian Neural Network Approach to Sparse Model-Agnostic Meta-Learning. ICASSP 2021: 2730-2734 - [i12]Anish Madan, Ranjitha Prasad:
B-SMALL: A Bayesian Neural Network approach to Sparse Model-Agnostic Meta-Learning. CoRR abs/2101.00203 (2021) - [i11]Shilpi Mishra, J. Harshan, Ranjitha Prasad:
Path-Aware OMP Algorithms for Provenance Recovery in Vehicular Networks. CoRR abs/2105.12456 (2021) - [i10]Aditya Saini, Ranjitha Prasad:
Locally Interpretable Model Agnostic Explanations using Gaussian Processes. CoRR abs/2108.06907 (2021) - [i9]Ansh Kumar Sharma, Rahul Kukreja, Ranjitha Prasad, Shilpa Rao:
DAGSurv: Directed Acyclic Graph Based Survival Analysis Using Deep Neural Networks. CoRR abs/2111.01482 (2021) - 2020
- [c13]Ankit Sharma, Garima Gupta, Ranjitha Prasad, Arnab Chatterjee, Lovekesh Vig, Gautam Shroff:
MultiMBNN: Matched and Balanced Causal Inference with Neural Networks. ESANN 2020: 25-30 - [c12]Srinidhi Hegde, Ranjitha Prasad, Ramya Hebbalaguppe, Vishwajeet Kumar:
Variational Student: Learning Compact and Sparser Networks In Knowledge Distillation Framework. ICASSP 2020: 3247-3251 - [c11]Sachin Kumar, Garima Gupta, Ranjitha Prasad, Arnab Chatterjee, Lovekesh Vig, Gautam Shroff:
CAMTA: Causal Attention Model for Multi-touch Attribution. ICDM (Workshops) 2020: 79-86 - [c10]Ankit Sharma, Garima Gupta, Ranjitha Prasad, Arnab Chatterjee, Lovekesh Vig, Gautam Shroff:
Hi-CI: Deep Causal Inference in High Dimensions. CD@KDD 2020: 39-61 - [i8]Ankit Sharma, Garima Gupta, Ranjitha Prasad, Arnab Chatterjee, Lovekesh Vig, Gautam Shroff:
MultiMBNN: Matched and Balanced Causal Inference with Neural Networks. CoRR abs/2004.13446 (2020) - [i7]Ankit Sharma, Garima Gupta, Ranjitha Prasad, Arnab Chatterjee, Lovekesh Vig, Gautam Shroff:
Hi-CI: Deep Causal Inference in High Dimensions. CoRR abs/2008.09858 (2020) - [i6]Sachin Kumar, Garima Gupta, Ranjitha Prasad, Arnab Chatterjee, Lovekesh Vig, Gautam Shroff:
CAMTA: Casual Attention Model for Multi-touch Attribution. CoRR abs/2012.11403 (2020)
2010 – 2019
- 2019
- [c9]Garima Gupta, Vishal Sunder, Ranjitha Prasad, Gautam Shroff:
CRESA: A Deep Learning Approach to Competing Risks, Recurrent Event Survival Analysis. PAKDD (2) 2019: 108-122 - [i5]Srinidhi Hegde, Ranjitha Prasad, Ramya Hebbalaguppe, Vishwajith Kumar:
Variational Student: Learning Compact and Sparser Networks in Knowledge Distillation Framework. CoRR abs/1910.12061 (2019) - [i4]Ankit Sharma, Garima Gupta, Ranjitha Prasad, Arnab Chatterjee, Lovekesh Vig, Gautam Shroff:
MetaCI: Meta-Learning for Causal Inference in a Heterogeneous Population. CoRR abs/1912.03960 (2019) - 2018
- [j4]Mine Alsan, Ranjitha Prasad, Vincent Y. F. Tan:
Lower Bounds on the Bayes Risk of the Bayesian BTL Model With Applications to Comparison Graphs. IEEE J. Sel. Top. Signal Process. 12(5): 975-988 (2018) - [c8]Ranjitha Prasad, Vincent Y. F. Tan:
Inference Algorithms for the Multiplicative Mixture Mallows Model. SPCOM 2018: 482-486 - [i3]Ranjitha Prasad:
Unlabelled Sensing: A Sparse Bayesian Learning Approach. CoRR abs/1802.00559 (2018) - 2017
- [i2]Mine Alsan, Ranjitha Prasad, Vincent Y. F. Tan:
Lower Bounds on the Bayes Risk of the Bayesian BTL Model with Applications to Random Graphs. CoRR abs/1709.09676 (2017) - 2015
- [j3]Ranjitha Prasad, Chandra R. Murthy, Bhaskar D. Rao:
Joint Channel Estimation and Data Detection in MIMO-OFDM Systems: A Sparse Bayesian Learning Approach. IEEE Trans. Signal Process. 63(20): 5369-5382 (2015) - [c7]Geethu Joseph, Chandra R. Murthy, Ranjitha Prasad, Bhaskar D. Rao:
Online Recovery of Temporally Correlated Sparse Signals Using Multiple Measurement Vectors. GLOBECOM 2015: 1-6 - [c6]Vinuthna Vinjamuri, Ranjitha Prasad, Chandra R. Murthy:
Sparse signal recovery in the presence of colored noise and rank-deficient noise covariance matrix: An SBL approach. ICASSP 2015: 3761-3765 - 2014
- [j2]Ranjitha Prasad, Chandra R. Murthy, Bhaskar D. Rao:
Joint Approximately Sparse Channel Estimation and Data Detection in OFDM Systems Using Sparse Bayesian Learning. IEEE Trans. Signal Process. 62(14): 3591-3603 (2014) - [c5]Ranjitha Prasad, Chandra R. Murthy, Bhaskar D. Rao:
Nested Sparse Bayesian Learning for block-sparse signals with intra-block correlation. ICASSP 2014: 7183-7187 - [c4]Ranjitha Prasad, Chandra R. Murthy:
Joint channel estimation and data detection in MIMO-OFDM systems using sparse Bayesian learning. NCC 2014: 1-6 - 2013
- [j1]Ranjitha Prasad, Chandra R. Murthy:
Cramér-Rao-Type Bounds for Sparse Bayesian Learning. IEEE Trans. Signal Process. 61(3): 622-632 (2013) - 2012
- [i1]Ranjitha Prasad, Chandra R. Murthy:
Cramer Rao-Type Bounds for Sparse Bayesian Learning. CoRR abs/1202.1119 (2012) - 2011
- [c3]Ranjitha Prasad, Bettagere Nagaraja Bharath, Chandra R. Murthy:
Joint data detection and dominant singular mode estimation in time varying reciprocal MIMO systems. ICASSP 2011: 3240-3243 - 2010
- [c2]Ranjitha Prasad, Chandra R. Murthy:
Bayesian Learning for Joint Sparse OFDM Channel Estimation and Data Detection. GLOBECOM 2010: 1-6
2000 – 2009
- 2008
- [c1]Ranjitha Prasad, Krishnamurthy Giridhar:
Robust Channel Tracking in Fast Fading MIMO channels. GLOBECOM 2008: 4329-4333
Coauthor Index
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