Publications 📄

Preprints:

• Towards Safer Social Media Platforms: Scalable and Performant Few-Shot Harmful Content Moderation Using Large Language Models [PDF]
  A. Bonagiri, L. Li, R. Oak, Z. Babar, M. Wojcieszak, A. Chhabra
  
  

• Re-ranking Using Large Language Models for Mitigating Exposure to Harmful Content on Social Media Platforms [PDF]
  R. Oak, M. Haroon, C. Jo, M. Wojcieszak, A. Chhabra
  
  

• Unraveling Indirect In-Context Learning Using Influence Functions [PDF]
  H. Askari, S. Gupta, T. Tong, F. Wang, A. Chhabra, M. Chen

Publications:

• Watching the AI Watchdogs: A Fairness and Robustness Analysis of AI Safety Moderation Classifiers,
  NAACL (2025) [PDF]
  A. Achara, A. Chhabra
  
  

• Assessing LLMs For Zero-shot Abstractive Summarization Through the Lens of Relevance Paraphrasing, NAACL - Findings (2025) [PDF]
  H. Askari, A. Chhabra, M. Chen, P. Mohapatra
  
  

• Incentivizing News Consumption on Social Media Platforms Using Large Language Models and Realistic Bot Accounts, PNAS Nexus (2024) [PDF]
  H. Askari, A. Chhabra, B. Hohenberg, M. Heseltine, M. Wojcieszak
  
  

• 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