Key papers

• Multilevel neural simulation-based inference (2025)
  Yuga Hikida et al.
  6.07
• Statistical Inference Leveraging Synthetic Data with Distribution-Free Guarantees (2025)
  Meshi Bashari et al.
  5.93
• Autorelevance function and other feature relevance measures for univariate time series (2026)
  Julian Cardenas et al.
  5.01
• EntroPath: Maximum Entropy Path Ensemble Embedding for Manifold Learning (2026)
  Przemys{\l}aw Rola
  5.01
• Zero-shot protein stability prediction by inverse folding models: a free energy interpretation (2025)
  Jes Frellsen et al.
  4.98
• Geometric mean-based pairwise comparison method with the reference values -- statistical approach (2026)
  Konrad Ku{\l}akowski et al.
  4.39
• Uncertainty quantification in mechanics: A unified Bayesian perspective (2026)
  Sascha Ranftl et al.
  4.39
• Deep learning-based prediction of time-resolved adhesive forces in viscoelastic Hertzian contacts (2026)
  Ali Maghami et al.
  4.39
• Directional Influence Function: Estimating Training Data Influence in Constrained Learning (2026)
  Xin Wang (Jeff) et al.
  4.39
• Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning (2025)
  Amirhossein Mollaali et al.
  4.36
• Goal-driven Bayesian Optimal Experimental Design for Robust Decision-Making Under Model Uncertainty (2026)
  Jinwoo Go et al.
  4.27
• Generative flow-based warm start of the variational quantum eigensolver (2025)
  Hang Zou and Martin Rahm and Anton Frisk Kockum and Simon Olsson
  3.86
• Adversarial Disentanglement by Backpropagation with Physics-Informed Variational Autoencoder (2025)
  Ioannis Christoforos Koune and Alice Cicirello
  3.81
• A kinetic-based regularization method for data science applications (2025)
  Abhisek Ganguly et al.
  3.64
• GraphBU: MILP Instance Generation with Graph-Native Block Units (2026)
  Xiaolei Guo et al.
  3.51
• Finding a stationary point of a stochastic convex problem (2026)
  Felipe Areces et al.
  3.51
• Dynamic Online Processor-Native Inference for State Estimation (2026)
  Orestis Kaparounakis
  3.51
• LatentFlow: A General Framework for Conditioning Stochastic Processes (2026)
  Louis Sharrock et al.
  3.51
• Ensemble Controlled-Flow Filtering for Implicit Data Assimilation (2026)
  Zhuoyuan Li et al.
  3.51
• Lipschitz Continuity in Deep Learning: A Systematic Review of Theoretical Foundations, Estimation Methods, Regularization Approaches, and Certifiable Robustness (2026)
  R\'ois\'in Luo et al.
  3.51
• Mixing-Free and Signal-Optimal Learning of Gaussian Graphical Models from Glauber Dynamics (2026)
  Vignesh Tirukkonda et al.
  3.51
• Algebraic Signatures for Structural Learning in Probability Tensors (2026)
  Akihiro Maeda et al.
  3.51
• The Tractability Landscape of Sampling with Inexact Scores (2026)
  Anming Gu et al.
  3.51
• DiscoverPhysics: Benchmarking LLMs for Out-of-the-Box Scientific Thinking (2026)
  Matt L. Wiemann et al.
  3.39
• Another look at statistical inference with machine learning-imputed data (2024)
  Jessica Gronsbell et al.
  3.20
• BEKAN: Boundary condition-guaranteed evolutionary Kolmogorov-Arnold networks with radial basis functions for solving PDE problems (2025)
  Bongseok Kim et al.
  3.10
• Dynamic SBI: Round-free Sequential Simulation-Based Inference with Adaptive Datasets (2025)
  Huifang Lyu and James Alvey and Noemi Anau Montel and Mauro Pieroni and Christoph Weniger
  3.10
• Fast Linear Solvers via AI-Tuned Markov Chain Monte Carlo-based Matrix Inversion (2025)
  Anton Lebedev and Won Kyung Lee et al.
  3.04
• Consistency of Feature Attribution in Deep Learning Architectures for Multi-Omics (2025)
  Daniel Claborne et al.
  2.93
• Reconstructing Physics-Informed Machine Learning for Traffic Flow Modeling: a Multi-Gradient Descent and Pareto Learning Approach (2025)
  Yuan-Zheng Lei et al.
  2.82
• DUE: A Deep Learning Framework and Library for Modeling Unknown Equations (2025)
  Junfeng Chen et al.
  2.76
• Generative Network-Based Reduced-Order Model for Prediction, Data Assimilation and Uncertainty Quantification (2021)
  Vinicius L. S. Silva et al.
  2.53
• A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms (2025)
  Gil Goldshlager et al.
  2.45
• Latent Guided Sampling for Combinatorial Optimization (2025)
  Sobihan Surendran (LPSM) et al.
  2.34
• 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
• Multiclass Classification without Labels via Posterior Simplex Geometry (2026)
  Rapha\"el Bonnet-Guerrini et al.
  2.00
• Generative Distributionally Robust Optimization (2026)
  Ziwei Zhang et al.
  2.00
• Localized Anomaly Detection via Differentiable D-vine Copulas (2026)
  Nicholas Andrea Pearson 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
• Laplace-PSN-IRT: Uncertainty Quantification for Neural Item Response Theory Models of LLM Benchmarks (2026)
  Juan Francisco et al.
  2.00
• Guiding Posterior Exploration with Optimizer-Derived Geometry (2026)
  Moritz Schlager et al.
  2.00
• Rethinking Likelihood distributions: Student's t Likelihood Boosts Bayesian Neural Network Performance (2026)
  Pei-Hsuan Hsia et al.
  2.00
• PIcsC: Partitioning-Induced Covariate Shift Correction (2026)
  Behraj Khan et al.
  2.00
• Emergent Latent-State Computation under Stochastic Volatility (2026)
  Xiaoyu Huang et al.
  2.00
• Adaptive Measurement Allocation for Learning Kernelized SVMs Under Noisy Observations (2026)
  Artur Miroszewski
  1.89
• Calibrating Scientific Foundation Models with Inference-Time Stochastic Attention (2026)
  Akash Yadav and Taiwo A. Adebiyi and Ruda Zhang
  1.83
• Towards Reliable Simulation-based Inference (2026)
  Arnaud Delaunoy
  1.78
• Learning Deep Hybrid Models with Sharpness-Aware Minimization (2026)
  Naoya Takeishi
  1.72
• Distribution-Free Robust Predict-Then-Optimize in Function Spaces (2026)
  Yash Patel et al.
  1.72
• Amortised and provably-robust simulation-based inference (2026)
  Ayush Bharti et al.
  1.72
• Aggregate Models, Not Explanations: Improving Feature Importance Estimation (2026)
  Joseph Paillard et al.
  1.72
• Active learning for data-driven reduced models of parametric differential systems with Bayesian operator inference (2026)
  Shane A. McQuarrie et al.
  1.67
• Physics-informed Gaussian Process Regression in Solving Eigenvalue Problem of Linear Operators (2026)
  Tianming Bai et al.
  1.67
• Dual-Level Models for Physics-Informed Multi-Step Time Series Forecasting (2026)
  Mahdi Nasiri et al.
  1.67
• VSE: Variational state estimation of complex model-free process (2026)
  Gustav Nor\'en et al.
  1.67
• Non-Negative Stiefel Approximating Flow: Orthogonalish Matrix Optimization for Interpretable Embeddings (2025)
  Brian B. Avants et al.
  1.56
• Algorithms and Scientific Software for Quasi-Monte Carlo, Fast Gaussian Process Regression, and Scientific Machine Learning (2025)
  Aleksei G. Sorokin
  1.56
• Parameter Identification for Partial Differential Equation with Jump Discontinuities in Coefficients by Markov Switching Model and Physics-Informed Machine Learning (2025)
  Zhikun Zhang et al.
  1.50
• Realizing LLMs' Causal Potential Requires Science-Grounded, Novel Benchmarks (2025)
  Ashutosh Srivastava et al.
  1.50
• Extreme Event Aware ($\eta$-) Learning (2025)
  Kai Chang et al.
  1.50