We develop a derivative-free temporal-difference (TD) framework for calibrating black-box simulators using one-step transitions from a target system and noisy simulator-generated continuation values....
arXiv:2606. 16923v1 Announce Type: new Abstract: Simulation-based inference (SBI) of latent parameters is often hindered by simulator misspecification, the mismatch between simulated and real-world observations caused by inherent modeling simplifications.
arXiv:2608. 10896v1 Announce Type: cross Abstract: Constant-stepsize temporal-difference (TD) learning is attractive for policy evaluation, but inference from a single Markov trajectory must account for serial dependence and a stepsize-dependent stationary target.
arXiv:2609.33543v2 Announce Type: replace Abstract: Coupled rollouts can reduce the noise of counterfactual action comparisons, but two issues prevent standard common-random-number constructions from...
The paper investigates how to collect data for recalibrating a misspecified simulator that models a partially observed real-world system as a fully observed Markovian process. It compares four data‑collection strategies: never recalibrating, passive on‑policy recalibration, continuous exploration across all units, and randomized experimentation on a sample of units. Theoretical analysis and a vending‑machine simulation show that experimentation can outperform passive adaptation when drift is invisible to on‑policy data or when recalibration lags behind ongoing drift, while exploration is preferable when trying worse actions is costly and dynamics may not have changed.
arXiv:2606. 18186v1 Announce Type: cross Abstract: Finite-dimensional (FD) diffusion policies exhibit temporal drift owing to discretization artifacts that degrade long-horizon performance (when deployed on physical systems).
The paper introduces a theoretical framework to study how perturbations introduced during diffusion model sampling affect the generated output. It defines a path cost based on the Kullback–Leibler divergence between perturbed and reference trajectories, showing that this cost bounds but does not fully determine output changes. Using a response identity, the authors link local perturbation propagation to changes in feature means, revealing that first‑order responses can miss significant output variations. Experiments on pretrained diffusion models demonstrate varying output sensitivity across sampling stages and spatial frequencies, and the framework successfully identifies problematic sampling intervals in cache‑based acceleration.
arXiv:2406.04261v2 Announce Type: replace Abstract: Simulators are vital in science and engineering, as they faithfully model the influence of design parameters on real-world observations. Optimizing...
arXiv:2607. 12928v1 Announce Type: new Abstract: We study the online binary sequential calibration problem.
arXiv:2306. 02704v2 Announce Type: replace-cross Abstract: We introduce \emph{Calibrated Stackelberg Games (CSGs)}, a generalization of the standard Stackelberg Games (SGs) framework.
arXiv:2608. 13096v1 Announce Type: new Abstract: Limit order book (LOB) simulators are most useful to practitioners when they combine realistic market dynamics, computationally efficient sampling, controllable scenario generation, and the ability to generalize beyond the instruments seen during training---properties that existing agent-based and deep generative simulators provide only partially.
arXiv:2606. 22062v2 Announce Type: replace-cross Abstract: Simulation-to-reality transfer, often called sim-to-real transfer, is a central challenge in robot learning.