stat.ME

Papers

• Causal Machine Learning: A Survey and Open Problems (2026)
  Jean Kaddour et al.
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• Certified Causal Defense with Generalizable Robustness (2026)
  Yiran Qiao et al.
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• Isometry pursuit (2026)
  Samson Koelle et al.
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• Re-examining Granger Causality with Causal Bayesian Networks and Reichenbachs Principles (2026)
  S. A. Adedayo
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• Causal Additive Models with Unobserved Causal Paths and Backdoor Paths (2026)
  Thong Pham et al.
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• LiDDA: Data Driven Attribution at LinkedIn (2026)
  John Bencina et al.
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• Position: Stop Chasing the C-index when Evaluating Survival Analysis Models (2026)
  Christian Marius Lillelund et al.
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• Local MDI+: Local Feature Importances for Tree-Based Models (2026)
  Zhongyuan Liang et al.
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• Vecchia-Inducing-Points Full-Scale Approximations for Gaussian Processes (2026)
  Tim Gyger et al.
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• SADA: Safe and Adaptive Aggregation of Multiple Black-Box Predictions in Semi-Supervised Learning (2026)
  Jiawei Shan et al.
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• SpeedCP: Fast Kernel-based Conditional Conformal Prediction (2026)
  Yating Liu et al.
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• One-shot Conditional Sampling: MMD meets Nearest Neighbors (2026)
  Anirban Chatterjee et al.
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• Beyond Accuracy: Are Time Series Foundation Models Well-Calibrated? (2026)
  Coen Adler et al.
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• Bayesian model selection and misspecification testing in imaging inverse problems only from noisy and partial measurements (2026)
  Tom Sprunck et al.
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• Selection-Induced Contraction of Innovation Statistics in Gated Kalman Filters (2026)
  Barak Or
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• Online monotone density estimation and log-optimal calibration (2026)
  Rohan Hore et al.
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• The Well-Tempered Classifier: Some Elementary Properties of Temperature Scaling (2026)
  Pierre-Alexandre Mattei et al.
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• Probabilistic Multilabel Graphical Modelling of Motif Transformations in Symbolic Music (2026)
  Ron Taieb et al.
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• Estimating Continuous Treatment Effects with Two-Stage Kernel Ridge Regression (2026)
  Seok-Jin Kim et al.
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• Variance-Reduced Manifold Sampling via Polynomial-Maximization Density Estimation (2026)
  Serhii Zabolotnii
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• Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation (2026)
  Benedikt L\"utke Schwienhorst et al.
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• LLM Sparsity Prior for Robust Feature Selection (2026)
  Caleb Skinner et al.
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• Operationalizing Individual Fairness via Gradient Descent and Bradley-Terry Models (2026)
  Conlan Olson et al.
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• Spiking the training data to correct for test set contamination (2026)
  Johnny Tian-Zheng Wei et al.
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• Learning Treatment Effects during Resource Allocation via Priority-Queue Randomization (2026)
  JungHo Lee et al.
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• Different Statistical Perspectives for Understanding Generalisation in Graph Neural Networks (2026)
  Nil Ayday et al.
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• Geometry Adaptive Counterfactual Distribution Learning with Diffusion-Guided Smoothing (2026)
  Kwangho Kim
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• Statistical Inference for Stochastic Gradient Descent Beyond Finite Variance (2026)
  Jose Blanchet et al.
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• Beyond Differences: Doubly Robust Meta-Learners for Ratio-Based Treatment Effects (2026)
  Michael Fuchs et al.
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• Function-Valued Causal Influence in Nonlinear Time Series (2026)
  Valentina V. Kuskova et al.
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• Confounder Detection via Treatment Intent: A New Observational Study Design (2026)
  Drago Plecko et al.
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• Structure-Adaptive Conformal Inference for Large-Scale Out-of-Distribution Testing (2026)
  Rongyi Sun et al.
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• Causal Representation Learning for Generalisable Recommendation (2026)
  Yorgos Felekis et al.
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• When prompt perturbations break your A/B test: A valid statistical test for generative surveying (2026)
  Hayden Helm et al.
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• Robust Moment-Based Estimation via Spectral Gradient Reweighting (2026)
  Liu Zhang et al.
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• Learning to target with network interference (2026)
  Xiaomeng Wang et al.
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• Multi-Teacher Knowledge Distillation via Teacher-Informed Mixture Priors (2026)
  Luyang Fang et al.
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• Dynamic Topic Modeling with a Higher-Order Hypergraphical Representation (2026)
  Hanjia Gao et al.
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• Towards Continuous-time Causal Foundation Models (2026)
  Dennis Thumm et al.
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• The Good, the Bad, and the Ugly of Markov Boundary for Tabular Prediction (2026)
  Shu Wan et al.
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• Leave a Window Out: Modifying the Jackknife for Predictive Inference in Time Series (2026)
  Hanyang Jiang et al.
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