Bonferroni
PulseAugur coverage of Bonferroni — every cluster mentioning Bonferroni across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
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New research explores multivariate conformal methods for atomistic simulation uncertainty
A new research paper explores multivariate conformal methods for quantifying uncertainty in atomistic simulations, a crucial step for developing accurate interatomic potentials in machine learning. The study, authored b…
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Small LLMs like Qwen2.5 and Llama 3.2 exhibit significant capitulation to user pushback
A new research paper investigates the tendency of small language models, specifically Qwen2.5-1.5B and Llama-3.2-1B, to abandon correct answers when challenged by users. The study found that these models frequently swit…
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New research tackles Shapley value estimation challenges in ML · 2 papers
Two new research papers published on arXiv propose novel methods for estimating Shapley values, a key technique in machine learning for feature attribution. The first paper introduces FUSHAP, designed to handle multi-si…
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New SASST method improves AI agent stress testing rigor
Researchers have developed a new method called Selection-Aware Semantic Stress Testing (SASST) to more rigorously evaluate interactive AI agents. SASST addresses a common issue where benchmarks select workflows and then…
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Statistical paper clarifies best-arm identification and FWER control
This paper explores the theoretical underpinnings of best-arm identification in statistics, specifically addressing the use of union bounds and their relationship to familywise error rate (FWER) control. It clarifies ho…
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New HG-CRC framework enhances LLM risk control across subgroups
Researchers have developed a new framework called Hierarchical Group-Conditional Conformal Risk Control (HG-CRC) to improve the reliability of large language models. This method ensures that risk guarantees are met not …
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Conditional Inference Forests show strong performance in feature selection
Researchers have developed Conditional Inference Trees (CIT) and Conditional Inference Forests (CIF) as methods for feature selection in machine learning. While these methods can be computationally intensive due to repe…