Papers

Preprints

Assessing the Robustness of Large Language Models At Zero-shot Abstractive Summarization Through the Lens of Relevance Paraphrasing [pdf]
A. Chhabra, H. Askari, P. Mohapatra

Incentivizing News Consumption on Social Media Platforms Using Large Language Models and Realistic Bot Accounts [pdf]
H. Askari, A. Chhabra, B. Hohenberg, M. Heseltine, M. Wojcieszak

Publications

Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Bias, NAACL (2024) [pdf]
A. Chhabra, H. Askari, P. Mohapatra
“What Data Benefits My Classifier?” Enhancing Model Performance and Interpretability Through Influence-Based Data Selection, ICLR (2024) [pdf]
A. Chhabra, P. Li, P. Mohapatra, H. Liu
Towards Fair Video Summarization, TMLR (2023) [pdf]
A. Chhabra, K. Patwari, C. Kuntala, Sristi, D.K. Sharma, P. Mohapatra
Robust Fair Clustering: A Novel Fairness Attack and Defense Framework, ICLR (2023) [pdf]
A. Chhabra, P. Li, P. Mohapatra, H. Liu

On the Robustness of Deep Clustering Models: Attacks and Defenses, NeurIPS (2022) [pdf]
A. Chhabra, A. Sekhari, P. Mohapatra
Suspicion-Free Adversarial Attacks on Clustering Algorithms, AAAI (2020) [pdf]
A. Chhabra, A. Roy, P. Mohapatra
Auditing YouTube’s Recommendation System for Ideologically Congenial, Extreme, and Problematic Recommendations, Proceedings of the National Academy of Sciences (PNAS) 2023 [pdf]
M. Haroon, M. Wojcieszak, A. Chhabra, X. Liu, P. Mohapatra, Z. Shafiq
An Overview of Fairness in Clustering, IEEE Access 2021 [pdf]
A. Chhabra, K. Masalkovaite, P. Mohapatra
Fair Clustering Using Antidote Data, AFCR @ NeurIPS (2021) [pdf]
A. Chhabra, A. Singla, P. Mohapatra
Tensorflex: Tensorflow Bindings for the Elixir Programming Language, MLOSS @ NeurIPS (2018) [pdf]
A. Chhabra, J. Valim
A Moving Target Defense Against Adversarial Machine Learning, ACM/IEEE Symposium on Edge Computing (2019) [pdf]
A. Roy, A. Chhabra, C. Kamhoua, P. Mohapatra
Fair Algorithms for Hierarchical Agglomerative Clustering, IEEE ICMLA (2022) [pdf]
A. Chhabra, P. Mohapatra
Understanding Flows in High-Speed Scientific Networks: A Netflow Data Study, Future Generation Computer Systems (2019) [pdf]
M. Kiran, A. Chhabra
GMMR: A Gaussian Mixture Model Based Unsupervised Machine Learning Approach for Optimal Routing in Opportunistic IoT Networks, Computer Communications (2019) [pdf]
V. Vashishth, A. Chhabra, D.K. Sharma
Enabling Security for the Industrial Internet of Things using Deep Learning, Blockchain, and Coalitions, Transactions on Emerging Telecommunications Technologies (2021) [pdf]
M. Sharma, S. Pant, D.K. Sharma, K.D. Gupta, V. Vashishth, A. Chhabra
RLProph: A Dynamic Programming Based Reinforcement Learning Approach for Optimal Routing in Opportunistic IoT Networks, Wireless Networks (2020) [pdf]
D.K. Sharma, J.J.P.C. Rodrigues, V. Vashishth, A. Khanna, A. Chhabra