Kavitha Srinivas
18 papers · 2019–2026 · 4 venues · across top CS/AI venues
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large language model
(5)
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language model
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classical planning
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reinforcement learning
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sample complexity
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automated machine learning
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knowledge graph
(2)
causal knowledge extraction
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feature engineering
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search algorithm
(2)
named entity recognition
(1)
plan generation
(1)
model evaluation
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information extraction
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automated planning
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natural language inference
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event forecasting
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attention mechanism
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natural language processing
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Papers
QueryGym: Step-by-Step Interaction with Relational Databases
AAAI 2026
ACPBench: Reasoning About Action, Change, and Planning
AAAI 2025
Automating Thought of Search: A Journey Towards Soundness and Completeness (Student Abstract)
AAAI 2025
Generalized Planning in PDDL Domains with Pretrained Large Language Models
AAAI 2024
Large Language Models as Planning Domain Generators (Student Abstract)
AAAI 2024
Thought of Search: Planning with Language Models Through The Lens of Efficiency
NeurIPS 2024
Action Space Reduction for Planning Domains
IJCAI 2023
CodeStylist: A System for Performing Code Style Transfer Using Neural Networks
AAAI 2023
SemFORMS: Automatic Generation of Semantic Transforms By Mining Data Science Code
IJCAI 2023
Can Machines Read Coding Manuals Yet? β A Benchmark for Building Better Language Models for Code Understanding
AAAI 2022
How to Reduce Action Space for Planning Domains? (Student Abstract)
AAAI 2022
Knowledge-Based News Event Analysis and Forecasting Toolkit
IJCAI 2022
Semantic Feature Discovery with Code Mining and Semantic Type Detection
AAAI 2022
SPOCK @ Causal News Corpus 2022: Cause-Effect-Signal Span Detection Using Span-Based and Sequence Tagging Models
EMNLP 2022
A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving
AAAI 2021
IBM Scenario Planning Advisor: A Neuro-Symbolic ERM Solution
AAAI 2021
Unsupervised Causal Knowledge Extraction from Text using Natural Language Inference (Student Abstract)
AAAI 2021
Answering Binary Causal Questions Through Large-Scale Text Mining: An Evaluation Using Cause-Effect Pairs from Human Experts
IJCAI 2019