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- llmClassificR: Text Classification with Large Language Models, Network Psychometrics, and Calibrated Uncertainty in R
- Etude comparative des répercussions émotionnelles chez les primo-intervenants (pompiers, SMUR) et les psychologues
- Benchmark Prediction with Sparse and Unpaired Cached Responses
- Penalized Cox Regression with Graph-Structured Distribution-Valued Predictors
- Unconfoundedness as an Information Constraint
- Causal Inference from Self-controlled Case Series
- ST-GFM: Spectral-Temporal Graph Forecasting with Measured Perturbation Bounds and Masked Spectral Reconstruction
- Robustness against lying of the confidence interval for a fraction of population with a sensitive characteristic
- Fast nonparametric estimation of sparse precision matrices with a priori sparsity structure
- Error Accumulation in Second-Order Sequential Machine Unlearning
- bacontrees: An extensible framework for Bayesian Context Trees in R
- Data integration using propensity score weighting under missing at random
- Neural Actor--Critic Learning with Minimax Critics and Reflected Langevin Sampling
- Evaluation of clinical prediction models: Decomposing performance uncertainty into model development and validation components
- Methods for Assessing Distributional Assumptions in Individual Participant Data Meta-Analysis of Diagnostic Tests
- Leveraging Continuous Trade Data for Intraday Imbalance Price Forecasting
- LLMs Depend on Specialist Tools for Out-of-Distribution Generalization in MS/MS Structural Elucidation
- EventGPT: Next-Event Pretraining on Banking Customer Histories
- Nested MCMC for Uncertainty Propagation in Spatial Modeling of Temporal Long-Memory in Resting-State fMRI
- Tail persistence of extremes at finite and asymptotic levels with Bayesian hierarchical inference
- Strictly proper scoring rules for node selection tasks on graphs
- Poisson Super Learning For Survival And Competing Risks On A Discretized Time Scale
- Bagel: A Fast Bayesian Online Changepoint Detection Algorithm for Linear Models via Posterior Merging
- Reducing SNAP Payment Errors at Lower Cost With AI: A Simulation Study
- Causal discovery needs explicit epistemic standards