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Towards Enhanced Local Explainability of Random Forests: a Proximity-Based Approach
We initiate a novel approach to explain the out of sample performance of random forest (RF) models by exploiting the fact that any RF can be formulated as an adaptive weighted K nearest-neighbors model.
Towards reducing hallucination in extracting information from financial reports using Large Language Models
For a financial analyst, the question and answer (Q\&A) segment of the company financial report is a crucial piece of information for various analysis and investment decisions.
Learning Embedded Representation of the Stock Correlation Matrix using Graph Machine Learning
In particular, the algorithm compresses the network into a lower dimensional continuous space, called an embedding, where pairs of nodes that are identified as similar by the algorithm are placed closer to each other.
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