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Strategic Incentivization for Locally Differentially Private Federated Learning
Yashwant Krishna Pagoti, Arunesh Sinha, Shamik Sural
In Federated Learning (FL), multiple clients jointly train a machine learning model by sharing gradient information, instead of raw data, with a server over multiple rounds. To add…
There are Levels to It: Red Teaming Dialogue With Hierarchical Reinforcement Learning
Roman Belaire, Arunesh Sinha, Pradeep Varakantham
Red teaming is essential for securing Large Language Models, yet current automated methods remain limited by templates and single-turn attacks. To simulate the complex, interactive…
On Learning Informative Trajectory Embeddings for Imitation, Classification and Regression
Zichang Ge, Changyu Chen, Arunesh Sinha +1
In real-world sequential decision making tasks like autonomous driving, robotics, and healthcare, learning from observed state-action trajectories is critical for tasks like imitat…
Towards Neural Network based Cognitive Models of Dynamic Decision-Making by Humans
Changyu Chen, Shashank Reddy Chirra, Maria José Ferreira +3
Modeling human cognitive processes in dynamic decision-making tasks has been an endeavor in AI for a long time because such models can help make AI systems more intuitive, personal…
On Minimizing Adversarial Counterfactual Error in Adversarial RL
Roman Belaire, Arunesh Sinha, Pradeep Varakantham
Deep Reinforcement Learning (DRL) policies are highly susceptible to adversarial noise in observations, which poses significant risks in safety-critical scenarios. The challenge in…