You can also find my articles on my Google Scholar profile.
Published in arXiv Pre-print, 2024
Recommended citation: Gundawar, Atharva, et al. "Robust Planning with LLM-Modulo Framework: Case Study in Travel Planning." arXiv preprint arXiv:2405.20625 (2024). https://arxiv.org/pdf/2405.20625
Published in arXiv Pre-print, 2024
Recommended citation: Bhambri, Siddhant, et al. "Efficient Reinforcement Learning via Large Language Model-based Search." arXiv preprint arXiv:2405.15194 (2024). https://arxiv.org/abs/2405.15194
Published in arXiv Pre-print, 2024
Recommended citation: Verma, Mudit, Siddhant Bhambri, and Subbarao Kambhampati. "On the Brittle Foundations of ReAct Prompting for Agentic Large Language Models." arXiv preprint arXiv:2405.13966 (2024). https://arxiv.org/pdf/2405.13966
Published in Human Robot Interaction (HRI), 2024
Recommended citation: Verma, Mudit, Siddhant Bhambri, and Subbarao Kambhampati. "Theory of Mind abilities of Large Language Models in Human-Robot Interaction: An Illusion?." arXiv preprint arXiv:2401.05302 (2024). https://arxiv.org/pdf/2401.05302
Published in arXiv Pre-print, 2023
Recommended citation: Bhambri, Siddhant, Mudit Verma, Anil Murthy, and Subbarao Kambhampati. "Benchmarking Multi-Agent Preference-based Reinforcement Learning for Human-AI Teaming." arXiv preprint arXiv:2312.14292 (2023). https://arxiv.org/pdf/2312.14292
Published in ICML - Workshop on Theory of Mind in Communicating Agents, and Many Facets of Preference Learning Workshop, 2023
Recommended citation: Verma, Mudit, Siddhant Bhambri, and Subbarao Kambhampati. "Preference Proxies: Evaluating Large Language Models in capturing Human Preferences in Human-AI Tasks." In ICML 2023 Workshop The Many Facets of Preference-Based Learning. 2023. https://sbhambr1.github.io/files/Preference%20Proxies:%20Evaluating%20Large%20Language%20Models%20in%20capturing%20Human%20Preferences%20in%20Human-AI%20Tasks.pdf
Published in IEEE Conference on Games (CoG), 2023
Recommended citation: Bhambri, Siddhant, Amrita Bhattacharjee, and Dimitri Bertsekas. "Reinforcement Learning Methods for Wordle: A POMDP/Adaptive Control Approach." arXiv preprint arXiv:2211.10298 (2022). https://arxiv.org/abs/2211.10298
Published in The AAAI Workshop on Representation Learning for Responsible Human-Centric AI (R2HCAI), and ICML - Many Facets of Preference Learning Workshop, 2023
Recommended citation: Verma, Mudit, Siddhant Bhambri, and Subbarao Kambhampati. "Exploiting Unlabeled Data for Feedback Efficient Human Preference based Reinforcement Learning." arXiv preprint arXiv:2302.08738 (2023). https://arxiv.org/abs/2302.08738
Published in Decision and Game Theory for Security: 13th International Conference, GameSec, 2022
Recommended citation: Bhambri, Siddhant, Purv Chauhan, Frederico Araujo, Adam Doupé, and Subbarao Kambhampati. "Using Deception in Markov Game to Understand Adversarial Behaviors Through a Capture-The-Flag Environment." In International Conference on Decision and Game Theory for Security, pp. 87-106. Cham: Springer International Publishing, 2022. https://arxiv.org/pdf/2210.15011
Published in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021
Recommended citation: Y. Zha, S. Bhambri and L. Guan, "Contrastively Learning Visual Attention as Affordance Cues from Demonstrations for Robotic Grasping," 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, pp. 7835-7842, doi: 10.1109/IROS51168.2021.9636760. https://ieeexplore.ieee.org/document/9636760
Published in IEEE International Conference on Smart Data Services (SMDS), 2020
Recommended citation: S. Gupta, S. Bhambri, K. Dhingra, A. B. Buduru and P. Kumaraguru, "Multi-objective Reinforcement Learning based approach for User-Centric Power Optimization in Smart Home Environments," 2020 IEEE International Conference on Smart Data Services (SMDS), 2020, pp. 89-96, doi: 10.1109/SMDS49396.2020.00018. https://ieeexplore.ieee.org/document/9288505
Published in arXiv Pre-print, 2020
Recommended citation: Bhambri, S., Muku, S., Tulasi, A., & Buduru, A. B. (2019). A survey of black-box adversarial attacks on computer vision models. arXiv preprint arXiv:1912.01667. https://arxiv.org/abs/1912.01667
Published in Eighth International Conference on Advances in Computing, Communication and Information Technology CCIT, 2019
Recommended citation: Kumar V, Bhambri S, Shambharkar PG. Multiple resource management and burst time prediction using deep reinforcement learning. In: Eighth International Conference on advances in computing, communication and information technology CCIT, 2019, pp. 51–58. https://www.seekdl.org/conferences/paper/details/10091.html