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ORCIDAnshuman Chhabra
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2020 – today
- 2025
[c15]Akshit Achara, Anshuman Chhabra:
Watching the AI Watchdogs: A Fairness and Robustness Analysis of AI Safety Moderation Classifiers. NAACL (Volume 2: Short Papers) 2025: 253-264- [c14]Hadi Askari, Anshuman Chhabra, Muhao Chen, Prasant Mohapatra:
Assessing LLMs for Zero-shot Abstractive Summarization Through the Lens of Relevance Paraphrasing. NAACL (Findings) 2025: 2187-2201 - [i18]Hadi Askari, Shivanshu Gupta, Terry Tong, Fei Wang, Anshuman Chhabra, Muhao Chen:
Unraveling Indirect In-Context Learning Using Influence Functions. CoRR abs/2501.01473 (2025) - [i17]Akshit Achara
, Anshuman Chhabra:
Watching the AI Watchdogs: A Fairness and Robustness Analysis of AI Safety Moderation Classifiers. CoRR abs/2501.13302 (2025) - [i16]Akash Bonagiri, Lucen Li, Rajvardhan Oak, Zeerak Babar, Magdalena Wojcieszak, Anshuman Chhabra:
Towards Safer Social Media Platforms: Scalable and Performant Few-Shot Harmful Content Moderation Using Large Language Models. CoRR abs/2501.13976 (2025) - [i15]Rajvardhan Oak, Muhammad Haroon, Claire Wonjeong Jo, Magdalena Wojcieszak, Anshuman Chhabra:
Re-ranking Using Large Language Models for Mitigating Exposure to Harmful Content on Social Media Platforms. CoRR abs/2501.13977 (2025) - [i14]Muhammad Haroon, Magdalena Wojcieszak, Anshuman Chhabra:
"Whose Side Are You On?" Estimating Ideology of Political and News Content Using Large Language Models and Few-shot Demonstration Selection. CoRR abs/2503.20797 (2025) - 2024
- [c13]Anshuman Chhabra, Peizhao Li, Prasant Mohapatra, Hongfu Liu:
"What Data Benefits My Classifier?" Enhancing Model Performance and Interpretability through Influence-Based Data Selection. ICLR 2024 - [c12]Anshuman Chhabra, Hadi Askari, Prasant Mohapatra:
Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Bias. NAACL (Short Papers) 2024: 1-11 - [i13]Anshuman Chhabra, Hadi Askari, Prasant Mohapatra:
Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Bias. CoRR abs/2401.01989 (2024) - [i12]Hadi Askari, Anshuman Chhabra, Bernhard Clemm von Hohenberg, Michael Heseltine, Magdalena Wojcieszak:
Incentivizing News Consumption on Social Media Platforms Using Large Language Models and Realistic Bot Accounts. CoRR abs/2403.13362 (2024) - [i11]Anshuman Chhabra, Bo Li, Jian Chen, Prasant Mohapatra, Hongfu Liu:
Outlier Gradient Analysis: Efficiently Improving Deep Learning Model Performance via Hessian-Free Influence Functions. CoRR abs/2405.03869 (2024) - [i10]Hadi Askari, Anshuman Chhabra, Muhao Chen, Prasant Mohapatra:
Assessing LLMs for Zero-shot Abstractive Summarization Through the Lens of Relevance Paraphrasing. CoRR abs/2406.03993 (2024) - 2023
- [b1]Anshuman Chhabra:
Towards Robust and Fair Machine Learning. University of California, Davis, USA, 2023 - [j7]Anshuman Chhabra, Kartik Patwari, Chandana Kuntala, Sristi, Deepak Kumar Sharma, Prasant Mohapatra:
Towards Fair Video Summarization. Trans. Mach. Learn. Res. 2023 (2023) - [c11]Anshuman Chhabra, Peizhao Li, Prasant Mohapatra, Hongfu Liu:
Robust Fair Clustering: A Novel Fairness Attack and Defense Framework. ICLR 2023 - 2022
- [c10]Anshuman Chhabra, Prasant Mohapatra:
Fair Algorithms for Hierarchical Agglomerative Clustering. ICMLA 2022: 206-211 - [c9]Anshuman Chhabra, Ashwin Sekhari, Prasant Mohapatra:
On the Robustness of Deep Clustering Models: Adversarial Attacks and Defenses. NeurIPS 2022 - [i9]Muhammad Haroon, Anshuman Chhabra, Xin Liu, Prasant Mohapatra, Zubair Shafiq, Magdalena Wojcieszak:
YouTube, The Great Radicalizer? Auditing and Mitigating Ideological Biases in YouTube Recommendations. CoRR abs/2203.10666 (2022) - [i8]Anshuman Chhabra, Ashwin Sekhari, Prasant Mohapatra:
On the Robustness of Deep Clustering Models: Adversarial Attacks and Defenses. CoRR abs/2210.01940 (2022) - [i7]Anshuman Chhabra, Peizhao Li, Prasant Mohapatra, Hongfu Liu:
Robust Fair Clustering: A Novel Fairness Attack and Defense Framework. CoRR abs/2210.01953 (2022) - 2021
- [j6]Anshuman Chhabra, Karina Masalkovaite, Prasant Mohapatra:
An Overview of Fairness in Clustering. IEEE Access 9: 130698-130720 (2021) - [j5]Mehul Sharma, Shrid Pant, Deepak Kumar Sharma, Koyel Datta Gupta, Vidushi Vashishth, Anshuman Chhabra:
Enabling security for the Industrial Internet of Things using deep learning, blockchain, and coalitions. Trans. Emerg. Telecommun. Technol. 32(7) (2021) - [c8]Anshuman Chhabra, Adish Singla, Prasant Mohapatra:
Fair Clustering Using Antidote Data. AFCR 2021: 19-39 - [c7]Anshuman Chhabra, Prasant Mohapatra:
Moving Target Defense against Adversarial Machine Learning. MTD@CCS 2021: 29-30 - [i6]Anshuman Chhabra, Adish Singla, Prasant Mohapatra:
Fair Clustering Using Antidote Data. CoRR abs/2106.00600 (2021) - [i5]Anshuman Chhabra, Adish Singla, Prasant Mohapatra:
Fairness Degrading Adversarial Attacks Against Clustering Algorithms. CoRR abs/2110.12020 (2021) - 2020
- [j4]Deepak Kumar Sharma, Joel J. P. C. Rodrigues, Vidushi Vashishth, Anirudh Khanna, Anshuman Chhabra:
RLProph: a dynamic programming based reinforcement learning approach for optimal routing in opportunistic IoT networks. Wirel. Networks 26(6): 4319-4338 (2020) - [c6]Anshuman Chhabra, Abhishek Roy, Prasant Mohapatra:
Suspicion-Free Adversarial Attacks on Clustering Algorithms. AAAI 2020: 3625-3632 - [i4]Anshuman Chhabra, Prasant Mohapatra:
Fair Algorithms for Hierarchical Agglomerative Clustering. CoRR abs/2005.03197 (2020)
2010 – 2019
- 2019
- [j3]Vidushi Vashishth, Anshuman Chhabra, Deepak Kumar Sharma:
GMMR: A Gaussian mixture model based unsupervised machine learning approach for optimal routing in opportunistic IoT networks. Comput. Commun. 134: 138-148 (2019) - [j2]Mariam Kiran, Anshuman Chhabra:
Understanding flows in high-speed scientific networks: A Netflow data study. Future Gener. Comput. Syst. 94: 72-79 (2019) - [c5]Abhishek Roy, Anshuman Chhabra, Charles A. Kamhoua, Prasant Mohapatra:
A moving target defense against adversarial machine learning. SEC 2019: 383-388 - [c4]Vidushi Vashishth, Anshuman Chhabra, Deepak Kumar Sharma:
A Machine Learning Approach Using Classifier Cascades for Optimal Routing in Opportunistic Internet of Things Networks. SECON 2019: 1-9 - [i3]Anshuman Chhabra, Abhishek Roy, Prasant Mohapatra:
Suspicion-Free Adversarial Attacks on Clustering Algorithms. CoRR abs/1911.07015 (2019) - 2018
- [j1]Anshuman Chhabra, Vidushi Vashishth, Deepak Kumar Sharma:
A fuzzy logic and game theory based adaptive approach for securing opportunistic networks against black hole attacks. Int. J. Commun. Syst. 31(4) (2018) - [i2]Anshuman Chhabra, Vidushi Vashishth, Anirudh Khanna, Deepak Kumar Sharma, Jyotsna Singh:
An Energy Efficient Routing Protocol for Wireless Internet-of-Things Sensor Networks. CoRR abs/1808.01039 (2018) - [i1]Satvik Jain, Arun Balaji Buduru, Anshuman Chhabra:
An approach to predictively securing critical cloud infrastructures through probabilistic modeling. CoRR abs/1810.11937 (2018) - 2017
- [c3]Anshuman Chhabra, Vidushi Vashishth, Deepak Kumar Sharma:
SEIR: A Stackelberg game based approach for energy-aware and incentivized routing in selfish Opportunistic Networks. CISS 2017: 1-6 - [c2]Anshuman Chhabra, Vidushi Vashishth, Deepak Kumar Sharma:
A game theory based secure model against Black hole attacks in Opportunistic Networks. CISS 2017: 1-6 - [c1]Anshuman Chhabra, Shivam Arora:
An Elliptic Curve Cryptography Based Encryption Scheme for Securing the Cloud against Eavesdropping Attacks. CIC 2017: 243-246
Coauthor Index
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