The Rao-Blackwell theorem is a fundamental theorem in statistics that offers a powerful method for improving estimators by conditioning on sufficient statistics. It is named after two statisticians, C.R. Rao and David Blackwell, who independently discovered it. The theorem is relevant in many areas of statistics, including machine learning algorithms...
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Simplicity Wins: How Large Language Models Will Revolutionize Software Engineering
Software engineering is on the brink of a revolution with the emergence of large language models (LLMs). LLMs are AI systems that have been trained on large amounts of data, allowing them to generate natural language text and source code.
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Research Idea: Encouraging Ensemble Diversity and Model Disagreement in Active Learning and Beyond
While training of deep ensembles or BNNs, we should be able to maximize the BALD score (model disagreement metric) as a regularizer using unlabeled data to improve model diversity and active learning efficiency (or OOD detection) where it matters: for pool or evaluation set data. Given the limited capacity of...
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Research Idea: Approximating BatchBALD via "k-BALD"
This post introduces a family of much less expensive approximations for BatchBALD that might work well where BatchBALD works. You might have noticed that BatchBALD can be very, very slow. We can approximate BatchBALD using pairwise mutual information terms, leading to a new approximation, we call 2-BALD, or generally, following...
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Paper Review: Bayesian Model Selection, the Marginal Likelihood, and Generalization
The paper, accepted as Long Oral at ICML 2022, discusses the (log) marginal likelihood (LML) in detail: its advantages, use-cases, and potential pitfalls, with an extensive review of related work. It further suggests using the “conditional (log) marginal likelihood (CLML)” instead of the LML and shows that it captures the...
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