Tootfinder

Tipping Point Sensitivity Analysis for Missing Data in Time-to-Event Endpoints: Model-Based and Model-Free Approaches
Ajmal Oodally, Craig Wang, Zheng Li, Arunava Chakravartty
https://arxiv.org/abs/2506.19988

Tipping Point Sensitivity Analysis for Missing Data in Time-to-Event Endpoints: Model-Based and Model-Free Approaches
Missing data frequently occurs in clinical trials with time-to-event endpoints, often due to administrative censoring. Other reasons, such as loss-to-follow up and patient withdrawal of consent, can violate the censoring-at-random assumption hence lead to biased estimates of the treatment effect under treatment policy estimand. Numerous methods have been proposed to conduct sensitivity analyses in these situations, one of which is the tipping point analysis. It aims to evaluate the robustness o…

Inverse design of the transmission matrix in a random system using Reinforcement Learning
Yuhao Kang
https://arxiv.org/abs/2506.13057 https://

Inverse design of the transmission matrix in a random system using Reinforcement Learning
This work presents an approach to the inverse design of scattering systems by modifying the transmission matrix using reinforcement learning. We utilize Proximal Policy Optimization to navigate the highly non-convex landscape of the object function to achieve three types of transmission matrices: (1) Fixed-ratio power conversion and zero-transmission mode in rank-1 matrices, (2) exceptional points with degenerate eigenvalues and unidirectional mode conversion, and (3) uniform channel participat…

Convex Approximations of Random Constrained Markov Decision Processes
V Varagapriya, Vikas Vikram Singh, Abdel Lisser
https://arxiv.org/abs/2505.24815 http…

Convex Approximations of Random Constrained Markov Decision Processes
Constrained Markov decision processes (CMDPs) are used as a decision-making framework to study the long-run performance of a stochastic system. It is well-known that a stationary optimal policy of a CMDP problem under discounted cost criterion can be obtained by solving a linear programming problem when running costs and transition probabilities are exactly known. In this paper, we consider a discounted cost CMDP problem where the running costs and transition probabilities are defined using ran…