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Kavitha Srinivas
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Publications
- 2024
- [c61]Tom Silver, Soham Dan, Kavitha Srinivas, Joshua B. Tenenbaum, Leslie Pack Kaelbling, Michael Katz:
Generalized Planning in PDDL Domains with Pretrained Large Language Models. AAAI 2024: 20256-20264 - [c60]James T. Oswald, Kavitha Srinivas, Harsha Kokel, Junkyu Lee, Michael Katz, Shirin Sohrabi:
Large Language Models as Planning Domain Generators (Student Abstract). AAAI 2024: 23604-23605 - [c59]James T. Oswald, Kavitha Srinivas, Harsha Kokel, Junkyu Lee, Michael Katz, Shirin Sohrabi:
Large Language Models as Planning Domain Generators. ICAPS 2024: 423-431 - [i21]Michael Katz, Harsha Kokel, Kavitha Srinivas, Shirin Sohrabi:
Planning with Language Models Through The Lens of Efficiency. CoRR abs/2404.11833 (2024) - [i20]James T. Oswald, Kavitha Srinivas, Harsha Kokel, Junkyu Lee, Michael Katz, Shirin Sohrabi:
Large Language Models as Planning Domain Generators. CoRR abs/2405.06650 (2024) - 2023
- [c55]Harsha Kokel, Junkyu Lee, Michael Katz, Kavitha Srinivas, Shirin Sohrabi:
Action Space Reduction for Planning Domains. IJCAI 2023: 5394-5401 - [i15]Tom Silver, Soham Dan, Kavitha Srinivas, Joshua B. Tenenbaum, Leslie Pack Kaelbling, Michael Katz:
Generalized Planning in PDDL Domains with Pretrained Large Language Models. CoRR abs/2305.11014 (2023) - 2022
- [c50]Harsha Kokel, Junkyu Lee, Michael Katz, Shirin Sohrabi, Kavitha Srinivas:
How to Reduce Action Space for Planning Domains? (Student Abstract). AAAI 2022: 12989-12990 - 2021
- [c41]Mark Feblowitz, Oktie Hassanzadeh, Michael Katz, Shirin Sohrabi, Kavitha Srinivas, Octavian Udrea:
IBM Scenario Planning Advisor: A Neuro-Symbolic ERM Solution. AAAI 2021: 16032-16034 - 2020
- [c38]Oktie Hassanzadeh, Debarun Bhattacharjya, Mark Feblowitz, Kavitha Srinivas, Michael Perrone, Shirin Sohrabi, Michael Katz:
Causal Knowledge Extraction through Large-Scale Text Mining. AAAI 2020: 13610-13611 - 2019
- [c33]Oktie Hassanzadeh, Debarun Bhattacharjya, Mark Feblowitz, Kavitha Srinivas, Michael Perrone, Shirin Sohrabi, Michael Katz:
Answering Binary Causal Questions Through Large-Scale Text Mining: An Evaluation Using Cause-Effect Pairs from Human Experts. IJCAI 2019: 5003-5009
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