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portfolio
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publications
SILC: Smoother Imitation with Lipschitz Costs
Published in Workshop on Goal Specification in Reinforcement Learning, ICML, 2018
Introduces Lipschitz-constrained cost functions to achieve smoother and more robust imitation learning.
Recommended citation: Sapana Chaudhary, Akshat Dave, Balaraman Ravindran. (2018). SILC: Smoother Imitation with Lipschitz Costs. ICML Workshop on Goal Specification in RL. https://sites.google.com/view/goalsrl/accepted-papers?authuser=0
Smooth Imitation Learning via Smooth Costs and Smooth Policies
Published in CoDS-COMAD (ACM Digital Library), 2022
Proposes smooth cost functions and policy regularization to improve stability and performance in imitation learning.
Recommended citation: Sapana Chaudhary, Balaraman Ravindran. (2022). Smooth Imitation Learning via Smooth Costs and Smooth Policies. CoDS-COMAD 2022. https://arxiv.org/abs/2111.02354
Safe Online Convex Optimization with Unknown Linear Safety Constraints
Published in AAAI Conference on Artificial Intelligence, 2022
Develops algorithms for online convex optimization that learn and satisfy unknown linear safety constraints during execution.
Recommended citation: Sapana Chaudhary, Dileep Kalathil. (2022). Safe Online Convex Optimization with Unknown Linear Safety Constraints. AAAI 2022. https://arxiv.org/abs/2111.07430
Enhanced Meta Reinforcement Learning via Demonstrations in Sparse Reward Environments
Published in Neural Information Processing Systems (NeurIPS), 2022
Improves meta-RL by incorporating demonstrations to accelerate learning in challenging sparse reward environments.
Recommended citation: Desik Rengarajan, Sapana Chaudhary, Jaewon Kim, Dileep Kalathil, Srinivas Shakkottai. (2022). Enhanced Meta Reinforcement Learning via Demonstrations in Sparse Reward Environments. NeurIPS 2022. https://arxiv.org/abs/2209.13048
Dynamic Regret Analysis of Safe Distributed Online Optimization for Convex and Non-convex Problems
Published in Transactions of Machine Learning Research (TMLR), 2023
Analyzes regret bounds for distributed online optimization under safety constraints in both convex and non-convex settings.
Recommended citation: Ting-Jui Chang, Sapana Chaudhary, Dileep Kalathil, Shahin Shahrampour. (2023). Dynamic Regret Analysis of Safe Distributed Online Optimization for Convex and Non-convex Problems. TMLR 2023. https://openreview.net/forum?id=xiQXHvL1eN
On Safety and Adaptivity in Sequential Decision Making
Published in International Joint Conference on Artificial Intelligence (IJCAI) Doctoral Consortium, 2023
Doctoral consortium paper exploring the interplay between safety constraints and adaptive learning in sequential decision problems.
Recommended citation: Sapana Chaudhary. (2023). On Safety and Adaptivity in Sequential Decision Making. IJCAI Doctoral Consortium 2023. https://www.ijcai.org/proceedings/2023/0813.pdf
Pedagogical Alignment of Large Language Models
Published in Empirical Methods in Natural Language Processing (EMNLP), 2024
Aligns LLMs with pedagogical principles to improve their effectiveness as educational tools and tutors.
Recommended citation: Shashank Sonkar, Kangqi Ni, Sapana Chaudhary, Richard G. Baraniuk. (2024). Pedagogical Alignment of Large Language Models. EMNLP 2024. https://arxiv.org/abs/2402.05000
Risk-Averse Finetuning of Large Language Models
Published in Neural Information Processing Systems (NeurIPS), 2024
Introduces risk-averse objectives for LLM finetuning to ensure robust and safe model behavior across diverse scenarios.
Recommended citation: Sapana Chaudhary, Ujwal Dinesha, Dileep Kalathil, Srinivas Shakkottai. (2024). Risk-Averse Finetuning of Large Language Models. NeurIPS 2024. https://arxiv.org/abs/2501.06911v1
AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents
Published in International Conference on Learning Representations (ICLR), 2025
Presents a minimalist approach to building effective web agents using LLMs with strong empirical performance.
Recommended citation: Ke Yang, Yao Liu, Sapana Chaudhary, Rasool Fakoor, Pratik Chaudhari, George Karypis, Huzefa Rangwala. (2025). AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents. ICLR 2025. https://arxiv.org/pdf/2410.13825v1
Offline Learning and Forgetting for Reasoning with Large Language Models
Published in Transactions on Machine Learning Research (TMLR), 2025
Explores offline learning dynamics and catastrophic forgetting in LLM reasoning tasks using reinforcement learning techniques.
Recommended citation: Tianwei Ni, Allen Nie, Sapana Chaudhary, Yao Liu, Huzefa Rangwala, Rasool Fakoor. (2025). Offline Learning and Forgetting for Reasoning with Large Language Models. TMLR. https://openreview.net/pdf?id=RF6raEUATc
VeriCoT: Neuro-symbolic Chain-of-Thought Validation via Logical Consistency Checks
Published in arXiv preprint, 2025
Combines neural and symbolic methods to validate chain-of-thought reasoning through logical consistency verification.
Recommended citation: Yu Feng, Nathaniel Weir, Kaj Bostrom, Sam Bayless, Darion Cassel, Sapana Chaudhary, Benjamin Kiesl-Reiter, Huzefa Rangwala. (2025). VeriCoT: Neuro-symbolic Chain-of-Thought Validation via Logical Consistency Checks. arXiv preprint. https://arxiv.org/abs/2511.04662
MaxCode: A Max-Reward Reinforcement Learning Framework for Automated Code Optimization
Published in arXiv preprint, 2026
A reinforcement learning framework that optimizes code by maximizing reward signals through automated search and refinement.
Recommended citation: Jiefu Ou, Sapana Chaudhary, Kaj Bostrom, Nathaniel Weir, Shuai Zhang, Huzefa Rangwala, George Karypis. (2026). MaxCode: A Max-Reward Reinforcement Learning Framework for Automated Code Optimization. arXiv preprint. https://arxiv.org/abs/2601.05475
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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