Fast and Robust Simulation-Based Inference With Optimization Monte Carlo

• URL: http://arxiv.org/abs/2511.13394v1
• Date: Mon, 17 Nov 2025 14:07:36 GMT
• Title: Fast and Robust Simulation-Based Inference With Optimization Monte Carlo
• Authors: Vasilis Gkolemis, Christos Diou, Michael Gutmann,
• Abstract summary: We propose a new method for differentiable simulators that delivers accurate posterior inference with substantially reduced runtimes.<n>Our method consistently matches, and often exceeds, the accuracy of state-of-the-art approaches, while reducing the runtime by a substantial margin.
• Score: 5.023891066282675
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bayesian parameter inference for complex stochastic simulators is challenging due to intractable likelihood functions. Existing simulation-based inference methods often require large number of simulations and become costly to use in high-dimensional parameter spaces or in problems with partially uninformative outputs. We propose a new method for differentiable simulators that delivers accurate posterior inference with substantially reduced runtimes. Building on the Optimization Monte Carlo framework, our approach reformulates stochastic simulation as deterministic optimization problems. Gradient-based methods are then applied to efficiently navigate toward high-density posterior regions and avoid wasteful simulations in low-probability areas. A JAX-based implementation further enhances the performance through vectorization of key method components. Extensive experiments, including high-dimensional parameter spaces, uninformative outputs, multiple observations and multimodal posteriors show that our method consistently matches, and often exceeds, the accuracy of state-of-the-art approaches, while reducing the runtime by a substantial margin.

Related papers

• Parallel Simulation for Log-concave Sampling and Score-based Diffusion Models [55.07411490538404]
  We propose a novel parallel sampling method that improves adaptive complexity dependence on dimension $d$.<n>Our approach builds on parallel simulation techniques from scientific computing.
  arXiv  Detail & Related papers  (2024-12-10T11:50:46Z)
• Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference [12.019504660711231]
  We introduce sequential neural posterior estimation (ASNPE)<n>ASNPE brings an active learning scheme into the inference loop to estimate the utility of simulation parameter candidates to the underlying probabilistic model.<n>Our method outperforms well-tuned benchmarks and state-of-the-art posterior estimation methods on a large-scale real-world traffic network.
  arXiv  Detail & Related papers  (2024-12-07T08:57:26Z)
• Accelerate Neural Subspace-Based Reduced-Order Solver of Deformable Simulation by Lipschitz Optimization [9.364019847856714]
  Reduced-order simulation is an emerging method for accelerating physical simulations with high DOFs. We propose a method for finding optimized subspace mappings, enabling further acceleration of neural reduced-order simulations. We demonstrate the effectiveness of our approach through general cases in both quasi-static and dynamics simulations.
  arXiv  Detail & Related papers  (2024-09-05T12:56:03Z)
• Diffusion posterior sampling for simulation-based inference in tall data settings [53.17563688225137]
  Simulation-based inference ( SBI) is capable of approximating the posterior distribution that relates input parameters to a given observation. In this work, we consider a tall data extension in which multiple observations are available to better infer the parameters of the model. We compare our method to recently proposed competing approaches on various numerical experiments and demonstrate its superiority in terms of numerical stability and computational cost.
  arXiv  Detail & Related papers  (2024-04-11T09:23:36Z)
• Multi-fidelity Constrained Optimization for Stochastic Black Box Simulators [1.6385815610837167]
  We introduce the algorithm Scout-Nd (Stochastic Constrained Optimization for N dimensions) to tackle the issues mentioned earlier. Scout-Nd efficiently estimates the gradient, reduces the noise of the estimator gradient, and applies multi-fidelity schemes to further reduce computational effort. We validate our approach on standard benchmarks, demonstrating its effectiveness in optimizing parameters highlighting better performance compared to existing methods.
  arXiv  Detail & Related papers  (2023-11-25T23:36:38Z)
• Learning Unnormalized Statistical Models via Compositional Optimization [73.30514599338407]
  Noise-contrastive estimation(NCE) has been proposed by formulating the objective as the logistic loss of the real data and the artificial noise. In this paper, we study it a direct approach for optimizing the negative log-likelihood of unnormalized models.
  arXiv  Detail & Related papers  (2023-06-13T01:18:16Z)
• Numerical Methods for Convex Multistage Stochastic Optimization [86.45244607927732]
  We focus on optimisation programming (SP), Optimal Control (SOC) and Decision Processes (MDP) Recent progress in solving convex multistage Markov problems is based on cutting planes approximations of the cost-to-go functions of dynamic programming equations. Cutting plane type methods can handle multistage problems with a large number of stages, but a relatively smaller number of state (decision) variables.
  arXiv  Detail & Related papers  (2023-03-28T01:30:40Z)
• Sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm [62.997667081978825]
  We propose a computationally efficient and powerful Bayesian approach for sparse high-dimensional linear regression. Minimal prior assumptions on the parameters are used through the use of plug-in empirical Bayes estimates. The proposed approach is implemented in the R package probe.
  arXiv  Detail & Related papers  (2022-09-16T19:15:50Z)
• Truncated Marginal Neural Ratio Estimation [5.438798591410838]
  We present a neural simulator-based inference algorithm which simultaneously offers simulation efficiency and fast empirical posterior testability. Our approach is simulation efficient by simultaneously estimating low-dimensional marginal posteriors instead of the joint posterior. By estimating a locally amortized posterior our algorithm enables efficient empirical tests of the robustness of the inference results.
  arXiv  Detail & Related papers  (2021-07-02T18:00:03Z)
• Global Optimization of Gaussian processes [52.77024349608834]
  We propose a reduced-space formulation with trained Gaussian processes trained on few data points. The approach also leads to significantly smaller and computationally cheaper sub solver for lower bounding. In total, we reduce time convergence by orders of orders of the proposed method.
  arXiv  Detail & Related papers  (2020-05-21T20:59:11Z)
• Learning to Guide Random Search [111.71167792453473]
  We consider derivative-free optimization of a high-dimensional function that lies on a latent low-dimensional manifold. We develop an online learning approach that learns this manifold while performing the optimization. We empirically evaluate the method on continuous optimization benchmarks and high-dimensional continuous control problems.
  arXiv  Detail & Related papers  (2020-04-25T19:21:14Z)
• Black-Box Optimization with Local Generative Surrogates [6.04055755718349]
  In fields such as physics and engineering, many processes are modeled with non-differentiable simulators with intractable likelihoods. We introduce the use of deep generative models to approximate the simulator in local neighborhoods of the parameter space. In cases where the dependence of the simulator on the parameter space is constrained to a low dimensional submanifold, we observe that our method attains minima faster than baseline methods.
  arXiv  Detail & Related papers  (2020-02-11T19:02:57Z)

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.