The field of stochastic modeling and control is witnessing significant developments, with a focus on integrating machine learning and neural networks to improve predictive accuracy and decision-making. Researchers are exploring innovative approaches to address complex challenges, such as safe reinforcement learning, distributional transport, and nonparametric estimation of conditional probability distributions. Notably, the use of neural ordinary differential equations, generative models, and latent stochastic differential equations is becoming increasingly prominent. These advancements have the potential to revolutionize various applications, including healthcare, finance, and engineering.

Some noteworthy papers in this area include: The paper on TSODE, which proposes a safety-aware controller that integrates Thompson Sampling RL with a Neural Ordinary Differential Equation forecaster, achieving 87.9% time-in-range with less than 10% time below 70 mg/dL in the FDA-approved UVa/Padova simulator. The BlinDNO paper, which formulates a distribution-to-function neural operator to recover the parameters of the underlying evolution operator from unordered density snapshots. The Departures paper, which approximates Schrödinger Bridge to directly align the distributions of control and perturbed single-cell populations, achieving state-of-the-art performance on public genetic and drug perturbation datasets. The paper on conditional push-forward neural networks, which introduces a generative framework for conditional distribution estimation, achieving performance competitive with state-of-the-art methods. The paper on generative modeling of clinical time series via latent stochastic differential equations, which proposes a framework that views clinical time series as discrete-time partial observations of an underlying controlled stochastic dynamical system, outperforming ordinary differential equation and long short-term memory baseline models.