Conservative Continuous-Time Treatment Optimization

• URL: http://arxiv.org/abs/2603.16789v1
• Date: Tue, 17 Mar 2026 17:01:23 GMT
• Title: Conservative Continuous-Time Treatment Optimization
• Abstract summary: We develop a conservative continuous-time control framework for treatment optimization from irregularly sampled patient trajectories.<n>Experiments on benchmark datasets show improved robustness and performance compared to non-conservative baselines.
• Score: 9.553261527696067
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We develop a conservative continuous-time stochastic control framework for treatment optimization from irregularly sampled patient trajectories. The unknown patient dynamics are modeled as a controlled stochastic differential equation with treatment as a continuous-time control. Naive model-based optimization can exploit model errors and propose out-of-support controls, so optimizing the estimated dynamics may not optimize the true dynamics. To limit extrapolation, we add a consistent signature-based MMD regularizer on path space that penalizes treatment plans whose induced trajectory distribution deviates from observed trajectories. The resulting objective minimizes a computable upper bound on the true cost. Experiments on benchmark datasets show improved robustness and performance compared to non-conservative baselines.

Related papers

• Nonlinear Model Predictive Control via Sequential Convex Programming for Drone-to-Drone Docking [0.0]
  This work formulates the docking task as a finite-horizon optimal control problem based on a reduced-order nonlinear model augmented with disturbance states.<n>The resulting problem is solved using sequential convex programming within a receding-horizon framework to generate dynamically feasible docking trajectories.
  arXiv  Detail & Related papers  (2026-08-11T06:27:45Z)
• Grounding Generative Policies in Physics: Optimization-Guided Diffusion for Robot Control [41.782471938706344]
  For task-space robot policies, generated grasps, waypoints, or trajectories can be distributionally valid yet infeasible.<n>We propose an inference-time optimization framework that couples the behavior generation to physical feasibility.<n>We evaluate the method on dexterous grasp synthesis with reachability and collision-avoidance constraints, and dynamic manipulation with controller-level trackability constraints.
  arXiv  Detail & Related papers  (2026-06-23T06:49:30Z)
• Balancing Accuracy and Efficiency: Adaptive Dynamics Orchestration for Model Predictive Control [8.262724319342945]
  High-fidelity dynamics models can accurately predict complex vehicle-terrain interactions during trajectory rollouts, but incur significant computational cost.<n> lightweight models enable fast updates and dense sampling, yet may produce erroneous predictions under safety-critical conditions.<n>We propose Adaptive Dynamics Orchestration (ADO), a framework that dynamically selects the most appropriate dynamics model for the current navigation context.
  arXiv  Detail & Related papers  (2026-05-22T19:05:25Z)
• Sampling-Based Control via Entropy-Regularized Optimal Transport [18.278741443124655]
  OT-MPC is a sampling-based algorithm that overcomes limitations through an entropy-regularized optimal transport formulation.<n>We derive closed-form, gradient-free updates via the Sinkhorn algorithm, enabling real-time performance.
  arXiv  Detail & Related papers  (2026-05-04T02:07:15Z)
• Dynamic Momentum Recalibration in Online Gradient Learning [6.6375578955686345]
  Gradient Descent (SGD) and its momentum variants form the backbone of deep learning optimization.<n>In this work, we reinterpret gradient updates through the lens of signal processing and reveal that fixed momentum coefficients inherently distort the balance between bias and variance, leading to skewed or suboptimal parameter updates.<n>We propose SGDF (SGD with Filter), an online, time-varying gain to dynamically refine gradient estimation.
  arXiv  Detail & Related papers  (2026-03-06T10:23:40Z)
• JAWS: Enhancing Long-term Rollout of Neural Operators via Spatially-Adaptive Jacobian Regularization [0.0]
  Jacobian-Adaptive Weighting for Stability (JAWS) is a probabilistic regularization strategy designed to mitigate these limitations.<n>By framing operator learning as Maximum A Posteriori (MAP) estimation with spatially heteroscedastic uncertainty, JAWS dynamically modulates the regularization strength based on local physical complexity.<n> Experiments demonstrate that this spatially-adaptive prior serves as an effective spectral pre-conditioner, which reduces the base operator's burden of handling high-frequency instabilities.
  arXiv  Detail & Related papers  (2026-03-04T06:15:09Z)
• Not All Preferences Are Created Equal: Stability-Aware and Gradient-Efficient Alignment for Reasoning Models [52.48582333951919]
  We propose a dynamic framework designed to enhance alignment reliability by maximizing the Signal-to-Noise Ratio of policy updates.<n>SAGE (Stability-Aware Gradient Efficiency) integrates a coarse-grained curriculum mechanism that refreshes candidate pools based on model competence.<n> Experiments on multiple mathematical reasoning benchmarks demonstrate that SAGE significantly accelerates convergence and outperforms static baselines.
  arXiv  Detail & Related papers  (2026-02-01T12:56:10Z)
• LatentTrack: Sequential Weight Generation via Latent Filtering [0.0]
  LatentTrack (LT) is a sequential neural architecture for online probabilistic prediction under nonstationary dynamics.<n> LT performs causal Bayesian filtering in a low-dimensional latent space and uses a lightweight hypernetwork to generate predictive model parameters at each time step.<n> LT consistently calibrated lower negative log-likelihood and mean squared error than stateful sequential and static uncertainty-aware baselines.
  arXiv  Detail & Related papers  (2026-01-31T02:22:59Z)
• Model-Based Diffusion Sampling for Predictive Control in Offline Decision Making [48.998030470623384]
  offline decision-making requires reliable behaviors from fixed datasets without further interaction.<n>We propose a compositional model-based diffusion framework consisting of: (i) a planner that generates diverse, task-aligned trajectories; (ii) a dynamics model that enforces consistency with the underlying system dynamics; and (iii) a ranker module that selects behaviors aligned with the task objectives.
  arXiv  Detail & Related papers  (2025-12-09T06:26:02Z)
• On the Optimal Construction of Unbiased Gradient Estimators for Zeroth-Order Optimization [57.179679246370114]
  A potential limitation of existing methods is the bias inherent in most perturbation estimators unless a stepsize is proposed.<n>We propose a novel family of unbiased gradient scaling estimators that eliminate bias while maintaining favorable construction.
  arXiv  Detail & Related papers  (2025-10-22T18:25:43Z)
• Control-Augmented Autoregressive Diffusion for Data Assimilation [17.305296093966803]
  We introduce an amortized framework that augments pretrained ARDMs with a lightweight controller.<n>We evaluate this framework in the context of data assimilation (DA) for chaotic partial differential equations (PDEs)<n>Our approach reduces DA inference to a single forward rollout with on-the-fly corrections, avoiding expensive adjoint computations and/or optimizations during inference.
  arXiv  Detail & Related papers  (2025-10-08T04:37:32Z)
• Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior [51.60683890503293]
  We propose a theoretical framework for studying behavior cloning of complex expert demonstrations using generative modeling. We show that pure supervised cloning can generate trajectories matching the per-time step distribution of arbitrary expert trajectories.
  arXiv  Detail & Related papers  (2023-07-27T04:27:26Z)
• Guaranteed Trajectory Tracking under Learned Dynamics with Contraction Metrics and Disturbance Estimation [5.147919654191323]
  This paper presents an approach to trajectory-centric learning control based on contraction metrics and disturbance estimation. The proposed framework is validated on a planar quadrotor example.
  arXiv  Detail & Related papers  (2021-12-15T15:57:33Z)
• Robust Value Iteration for Continuous Control Tasks [99.00362538261972]
  When transferring a control policy from simulation to a physical system, the policy needs to be robust to variations in the dynamics to perform well. We present Robust Fitted Value Iteration, which uses dynamic programming to compute the optimal value function on the compact state domain. We show that robust value is more robust compared to deep reinforcement learning algorithm and the non-robust version of the algorithm.
  arXiv  Detail & Related papers  (2021-05-25T19:48:35Z)
• Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects and Dynamics [90.81186513537777]
  We propose a novel compound kernel that captures the control-affine nature of the problem. We show that this resulting optimization problem is convex, and we call it Gaussian Process-based Control Lyapunov Function Second-Order Cone Program (GP-CLF-SOCP)
  arXiv  Detail & Related papers  (2020-11-14T01:27:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.