📜Accepted Papers:
1. Adversarially-Robust TD Learning with Markovian Data: Finite-Time Rates and Fundamental Limits [Accepted at the 28th International Conference on AI and Statistics AISTATS 2025]
[Paper ♾️] [Proceedings 📜] [Code 🖳][Slides 📈]
2. Robust Q Learning under Corrupted Rewards [Accepted at the 63rd IEEE Conference on Decision and Control CDC 2024]
[Paper ♾️] [Proceedings 📜] [Code 🖳] [Slides 📈]
🏗️Papers Under Review/Preparation:
1. Robust Federated Q-Learning with Almost No Communication [Under Review at the 64th IEEE Conference on Decision and Control CDC 2025]
2. Fragile object transportation by a multi-robot system in an unknown environment using a semi-decentralized control approach [Under Review Paper 🏗️]
Symposium Posters/Technical Reports:
1. Towards Finite-Time Rates for Adversarially-Robust Reinforcement Learning: Mathematical Guarantees and Fundamental Limits. (Invited Talk / Poster at Northeast Systems and Control Symposium (NESCW 2025), Columbia University, New York) [Poster 🪧]
2. Adversarially-Robust Deep Q-Network for Algorithmic Trading (Poster at Students Symposium (2025), Neural Networks at North Carolina State University) [Poster 🪧]
3. Robust Algorithms for Adversarial Reinforcement Learning (Poster at Applied AI Symposium (2024), Theoretical Machine Learning Track at North Carolina State University) [Poster 🪧]