Key papers

• Distributional Determinantal Point Process for Repulsive Clustering of Distributions (2026)
  Khai Nguyen et al.
  5.49
• Automatic knot selection in smooth additive models (2026)
  Nicol\'as Carrizosa et al.
  5.01
• Optimal use of a black-box learner in semiparametric estimation (2026)
  Yihong Gu
  5.01
• Smart predict-then-robustly-optimize (2026)
  Aakil Caunhye et al.
  5.01
• Natural Invariant Measures for Chaotic Game Dynamics: Finding Order in Chaos (2026)
  Jakub Bielawski et al.
  5.01
• Simulation-Based Empirical Bayes (2026)
  Xinwei Shen et al.
  5.01
• Interventional Score Geometry for Causal Inference (2026)
  Mojtaba Eslami
  5.01
• Learning Bidirectional Causal Interactions with Heteroscedastic Neural Networks (2026)
  Masahiro Tanaka
  5.01
• Learning Ergodic Dynamical Systems from a Finite Trajectory (2026)
  Oleksii Kachaiev et al.
  5.01
• Graph-Based Correlation Matrix Generation: A Convex Optimization Approach (2026)
  Ali Fakhar (UGA) et al.
  5.01
• The V-fold jackknife for semiparametric inference: variance estimation, confidence intervals, and simultaneous confidence bands (2026)
  Yi Li et al.
  5.01
• Sample Complexities of Estimating Gumbel--Max Watermark Proportions with and without Reduction to Pivotal Statistics (2026)
  Shuwen Chai et al.
  4.39
• Causal Discovery on Irregular Time Series (2026)
  Martim Penim et al.
  4.39
• Uncertainty quantification in mechanics: A unified Bayesian perspective (2026)
  Sascha Ranftl et al.
  4.39
• On the sensitivity of machine-learned probabilistic weather forecast models to scale-aware scoring rules (2026)
  Simon Lang et al.
  4.39
• Data-Poisoning Audits for Causal Effect Estimation (2026)
  Kwangho Kim
  4.39
• A Structure-Adaptive Random Feature Method for High-Dimensional Elliptic PDEs (2026)
  Jiale Linghu et al.
  4.39
• Nonlinear Bias-Compensated Adaptive Filter and Its Application for Time-Series Prediction (2026)
  Yi Peng et al.
  4.39
• Instance Hardness-Based Relevance for Imbalanced Regression (2026)
  Vitor M. Leitao et al.
  4.39
• Interpretable Fuzzy Rule-Based Regression Extension for Ex-Fuzzy Library (2026)
  Cayan Deniz Kucuktopana et al.
  4.39
• Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference (2026)
  Haoran Li et al.
  4.39
• Inducing Comparability of Factorised Probability Distributions (2026)
  Jan Speller et al.
  4.39
• Generative Bayesian Filtering for State Estimation (2026)
  Lei Cao et al.
  4.39
• Grounding Investor Views: Neural Predicates in the Black-Litterman Model (2026)
  Marcos Florencio
  4.39
• Measuring the Dependency Gap: Diagnosing Inter-Column Fidelity in Tabular Generative Models (2026)
  Jie Zhang
  4.39
• Scaling Laws for Classical Machine Learning on Tabular Data: A Benchmark Study (2026)
  Kaihua Ding
  4.39
• MissHyper: Restoring Clinical Synchronicity in Missingness-Guided Hypergraph Forecasting (2026)
  Mingyi Ma et al.
  4.39
• Multiplicity of Stable Attractors in Disordered Neural Models (2026)
  Raffaele Marino et al.
  4.39
• From Score Approximation to Distribution Approximation in Score-Based Diffusion Models (2026)
  Lan V. Truong
  4.39
• Class-Balanced Softmax: A Bayes Theory-Based Method for Long-Tailed Recognition (2026)
  Yi-Hang Zhu et al.
  4.39
• Evolution-Aware MSA Reasoning for Subsampling via Factor Graphs (2026)
  Zhangzhi Xiong et al.
  4.39
• Algebraic Signatures for Structural Learning in Probability Tensors (2026)
  Akihiro Maeda et al.
  3.51
• Equilibrium Causal Games: Separation, Identification, and the Identifiability of Cyclic Latent States (2026)
  Faraz Dadgostari et al.
  3.51
• Online Pricing and Allocation with Demand Learning and Fulfillment Cost (2025)
  Jianyu Xu et al.
  2.06
• Is Randomness Necessary for Adaptive Data Analysis? (2026)
  Edith Cohen et al.
  2.00
• Causal Graphs, Markov Properties and Do-calculus for Stochastic Differential Equations (2026)
  Philip Boeken et al.
  2.00
• Interpretable Anomaly and Drift Detection with Gaussian Mixture Models (2026)
  Behnam Asadi
  2.00
• Simpson's Paradox in Behavioral Curves: How Aggregation Distorts Parametric Models of User Dynamics (2026)
  Chao Zhou
  1.89
• Conformal Anomaly Detection in Python: Moving Beyond Heuristic Thresholds with nonconform (2026)
  Oliver Hennh\"ofer et al.
  1.89
• Heavy-Tailed Principal Component Analysis (2026)
  Mario Sayde et al.
  1.78
• Gaussian Mixture Model with unknown diagonal covariances via continuous sparse regularization (2025)
  Romane Giard (ECL et al.
  1.44
• Participatory Budgeting with Project Groups (2020)
  Pallavi Jain et al.
  —
• Ask for More Than Bayes Optimal: A Theory of Indecisions for Selective Hypothesis Testing (2024)
  Mohamed Ndaoud et al.
  —