Papers

• Multilevel neural simulation-based inference (2025)
  Yuga Hikida et al.
  6.07
• Dynamic Online Processor-Native Inference for State Estimation (2026)
  Orestis Kaparounakis
  3.51
• A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms (2025)
  Gil Goldshlager et al.
  2.45
• A Hierarchical Validity-Audit Framework for Neural Mass Models in Simulation-Based Inference: From Observational Coverage to Mechanistic Interpretation (2026)
  Tianming Cai et al.
  2.00
• Simulation-based parameter estimation via a combination of embedded normalizing flows and implied empirical probabilities under moment restrictions (2026)
  Getachew K. Befekadu
  2.00
• Towards Reliable Simulation-based Inference (2026)
  Arnaud Delaunoy
  1.78
• Amortised and provably-robust simulation-based inference (2026)
  Ayush Bharti et al.
  1.72
• Scalable Batch Correction for Cell Painting via Batch-Dependent Kernels and Adaptive Sampling (2026)
  Aditya Narayan Ravi et al.
  1.67
• Gaussian process surrogate with physical law-corrected prior for multi-coupled PDEs defined on irregular geometry (2025)
  Pucheng Tang et al.
  1.44
• Efficient optimization of expensive black-box simulators via marginal means, with application to neutrino detector design (2025)
  Hwanwoo Kim et al.
  1.39
• A Score-based Diffusion Model Approach for Adaptive Learning of Stochastic Partial Differential Equation Solutions (2025)
  Toan Huynh et al.
  1.39
• Are Statistical Methods Obsolete in the Era of Deep Learning? A Study of ODE Inverse Problems (2025)
  Skyler Wu et al.
  1.22
• Annealing Flow Generative Models Towards Sampling High-Dimensional and Multi-Modal Distributions (2024)
  Dongze Wu et al.
  0.83
• Self-Tuning Hamiltonian Monte Carlo for Accelerated Sampling (2023)
  Henrik Christiansen and Federico Errica and Francesco Alesiani
  0.17
• BINOCULARS for Efficient, Nonmyopic Sequential Experimental Design (2019)
  Shali Jiang et al.
  —
• Stochastic Gradient Bayesian Optimal Experimental Designs for Simulation-based Inference (2023)
  Vincent D. Zaballa and Elliot E. Hui
  —
• Diffusive Gibbs Sampling (2024)
  Wenlin Chen et al.
  —
• Extracting Signal out of Chaos: Advancements on MAGI for Bayesian Analysis of Dynamical Systems (2024)
  Skyler Wu
  —
• Denoising Fisher Training For Neural Implicit Samplers (2024)
  Weijian Luo and Wei Deng
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