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S Sivaranjani

2 accepted papers

2025

Globally Optimal Policy Gradient Algorithms for Reinforcement Learning with PID Control Policies

NeurIPS 2025poster

We develop policy gradient algorithms with global optimality and convergence guarantees for reinforcement learning (RL) with proportional-integral-derivative (PID) parameterized control policies. RL enables learning control policies through direct interaction with a system, without explicit model kn…

Cited by 0SourceScholar
2024

ECLipsE: Efficient Compositional Lipschitz Constant Estimation for Deep Neural Networks

NeurIPS 2024spotlight

The Lipschitz constant plays a crucial role in certifying the robustness of neural networks to input perturbations. Since calculating the exact Lipschitz constant is NP-hard, efforts have been made to obtain tight upper bounds on the Lipschitz constant. Typically, this involves solving a large matri…

Cited by 1SourcePDFScholar