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Found 5 papers, 2 papers with code
Joint Repetition Suppression and Content Moderation of Large Language Models
Natural language generation (NLG) is one of the most impactful fields in NLP, and recent years have witnessed its evolution brought about by large language models (LLMs).
Interactive Text Generation
Unfortunately, this means most of the research on text, code, and image generation has focused on non-interactive settings, whereby the model is expected to get everything right without accounting for any input from a user who may be willing to help.
Targeting Learning: Robust Statistics for Reproducible Research
Targeted Learning is a subfield of statistics that unifies advances in causal inference, machine learning and statistical theory to help answer scientifically impactful questions with statistical confidence.
Nonparametric Bootstrap Inference for the Targeted Highly Adaptive LASSO Estimator
In order to optimize the finite sample coverage of the nonparametric bootstrap confidence intervals, we propose a selection method for this sectional variation norm that is based on running the nonparametric bootstrap for all values of the sectional variation norm larger than the one selected by cross-validation, and subsequently determining a value at which the width of the resulting confidence intervals reaches a plateau.
Statistics Theory Methodology Statistics Theory
Data-adaptive statistics for multiple hypothesis testing in high-dimensional settings
Current statistical inference problems in areas like astronomy, genomics, and marketing routinely involve the simultaneous testing of thousands -- even millions -- of null hypotheses.
