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Temporal-Difference Calibration of Black-Box Simulators
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arXiv Statistics ML
Oct 27
Temporal-Difference Calibration of Black-Box Simulators
arXiv:2610.35311v1 Announce Type: new Abstract: We develop a derivative-free temporal-difference (TD) framework for calibrating black-box simulators using one-step transitions from a target system an...
arXiv Machine Learning
Aug 12
Self-Normalized Inference for Constant-Stepsize Temporal-Difference Learning under Markovian Sampling
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 AI
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OSCC: Certified Observation-Safe Coupling Optimization for Gradient-Noise Control in Imperfect-Information Learning
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arXiv AI
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MA-SBI: Misspecification-Aware Simulation-Based Inference via Side-Channel Guidance
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 AI
Oct 9
Equal Path Cost, Unequal Output Effects: Understanding Perturbation Propagation in Diffusion Models
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 AI
Jun 17
Kolmogorov Regression for Robust Diffusion Policies
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).