Gradient Boosting Decision Trees
PulseAugur coverage of Gradient Boosting Decision Trees — every cluster mentioning Gradient Boosting Decision Trees across labs, papers, and developer communities, ranked by signal.
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New 'split-candidate scaling' parameter reveals double descent in GBDTs
Researchers have identified a new capacity parameter for gradient boosting decision trees (GBDTs) called split-candidate scaling, which can lead to a phenomenon known as double descent. Unlike neural networks, GBDTs hav…
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LLM steerability predicted from early internal states
Researchers have developed a method to predict the success of controlling large language models (LLMs) through activation steering. By analyzing a model's internal states early in the generation process, they can foreca…
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New method predicts LLM steerability from early decoding states
Researchers have developed a method to predict the steerability of large language models (LLMs) using early decoding states. This approach employs a Gradient Boosting Decision Trees (GBDT) classifier trained on features…
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New GBDT Training Method Hides Record IDs for Enhanced Privacy
Researchers have developed a new method for training Gradient Boosting Decision Trees (GBDTs) on vertically partitioned data while preserving the anonymity of record identifiers. This approach addresses the security vul…