Key papers

• 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
• 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
• Scaling Laws for Classical Machine Learning on Tabular Data: A Benchmark Study (2026)
  Kaihua Ding
  4.39
• Directional Influence Function: Estimating Training Data Influence in Constrained Learning (2026)
  Xin Wang (Jeff) et al.
  4.39
• Structuring Scientific Innovation: A Framework for Modeling and Discovering Impactful Knowledge Combinations (2025)
  Junlan Chen et al.
  3.64
• Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation (2024)
  Diantong Li et al.
  3.47
• Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data (2026)
  Vin\'icius Gabriel Angelozzi et al.
  2.00
• 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
• Data Quality Profiling at Scale with Progressive Sampling: A Benchmark for Data-Centric AI Pipelines (2026)
  Laure Berti-Equille
  2.00
• PIcsC: Partitioning-Induced Covariate Shift Correction (2026)
  Behraj Khan et al.
  2.00
• From Training to Deployment: Post-Hoc Causal Feature Identification via Sensitivity Ratios (2026)
  Athanasios Vlontzos et al.
  2.00
• A Unified Framework for Uncertainty-Aware Explainable Artificial Intelligence: A Case Study in Power Quality Disturbance Classification (2026)
  Yinsong Chen et al.
  1.89
• InferenceEvolve: Towards Automated Causal Effect Estimators through Self-Evolving AI (2026)
  Can Wang et al.
  1.83
• An Axiomatic Benchmark for Evaluation of Scientific Novelty Metrics (2026)
  Miri Liu et al.
  1.83
• Towards Reliable Simulation-based Inference (2026)
  Arnaud Delaunoy
  1.78
• Making Multi-Axis Models Robust to Multiplicative Noise: How, and Why? (2026)
  Bailey Andrew et al.
  1.78
• Importance inversion transfer identifies shared principles for cross-domain learning (2026)
  Daniele Caligiore
  1.72
• Amortised and provably-robust simulation-based inference (2026)
  Ayush Bharti et al.
  1.72
• cc-Shapley: Measuring Multivariate Feature Importance Needs Causal Context (2026)
  J\"org Martin and Stefan Haufe
  1.72
• Non-Negative Stiefel Approximating Flow: Orthogonalish Matrix Optimization for Interpretable Embeddings (2025)
  Brian B. Avants et al.
  1.56
• Fusion of classical and quantum kernels enables accurate and robust two-sample tests (2025)
  Yu Terada et al.
  1.56
• EngiBench: A Framework for Data-Driven Engineering Design Research (2025)
  Florian Felten et al.
  1.39
• Efficient optimization of expensive black-box simulators via marginal means, with application to neutrino detector design (2025)
  Hwanwoo Kim et al.
  1.39
• From Observations to Causations: A GNN-based Probabilistic Prediction Framework for Causal Discovery (2025)
  Rezaur Rashid et al.
  1.33
• Data-Driven Soil Organic Carbon Sampling: Integrating Spectral Clustering with Conditioned Latin Hypercube Optimization (2025)
  Weiying Zhao et al.
  1.28
• Bayesian Epistemology with Weighted Authority: A Formal Architecture for Truth-Promoting Autonomous Scientific Reasoning (2025)
  Craig S. Wright
  1.28
• Cohort-attention Evaluation Metrics for Tied Data (2025)
  Dongjing Jiang et al.
  1.11
• Verification and Validation for Trustworthy Scientific Machine Learning (2025)
  John D. Jakeman et al.
  1.06
• BINOCULARS for Efficient, Nonmyopic Sequential Experimental Design (2019)
  Shali Jiang et al.
  —
• BaCaDI: Bayesian Causal Discovery with Unknown Interventions (2022)
  Alexander H\"agele et al.
  —
• Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena (2022)
  Timo Freiesleben et al.
  —
• Inference of Regulatory Networks Through Temporally Sparse Data (2022)
  Mohammad Alali and Mahdi Imani
  —
• A Robust Scientific Machine Learning for Optimization: A Novel Robustness Theorem (2022)
  Luana P. Queiroz et al.
  —
• A Rigorous Uncertainty-Aware Quantification Framework Is Essential for Reproducible and Replicable Machine Learning Workflows (2023)
  Line Pouchard et al.
  —
• A Novel Fuzzy Bi-Clustering Algorithm with AFS for Identification of Co-Regulated Genes (2023)
  Kaijie Xu
  —
• Stochastic Gradient Bayesian Optimal Experimental Designs for Simulation-based Inference (2023)
  Vincent D. Zaballa and Elliot E. Hui
  —
• Ab initio uncertainty quantification in scattering analysis of microscopy (2023)
  Mengyang Gu et al.
  —
• Assumption-Lean and Data-Adaptive Post-Prediction Inference (2023)
  Jiacheng Miao et al.
  —
• DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design (2023)
  Clare Lyle et al.
  —
• Causal hybrid modeling with double machine learning (2024)
  Kai-Hendrik Cohrs et al.
  —