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Found 6 papers, 1 papers with code
Incorporating LLM Priors into Tabular Learners
We present a method to integrate Large Language Models (LLMs) and traditional tabular data classification techniques, addressing LLMs challenges like data serialization sensitivity and biases.
Tabular Few-Shot Generalization Across Heterogeneous Feature Spaces
Despite the prevalence of tabular datasets, few-shot learning remains under-explored within this domain.
Interpretable Medical Diagnostics with Structured Data Extraction by Large Language Models
Drawing upon the reasoning capabilities of LLMs, TEMED-LLM goes beyond traditional extraction techniques, accurately inferring tabular features, even when their names are not explicitly mentioned in the text.
MoET: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees
We propose MoET, a more expressive, yet still interpretable model based on Mixture of Experts, consisting of a gating function that partitions the state space, and multiple decision tree experts that specialize on different partitions.
Gaussian Conditional Random Fields for Classification
The extended method of local variational approximation of sigmoid function is used for solving empirical Bayes in GCRFBCb variant, whereas MAP value of latent variables is the basis for learning and inference in the GCRFBCnb variant.
MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning
By training Mo\"ET models using an imitation learning procedure on deep RL agents we outperform the previous state-of-the-art technique based on decision trees while preserving the verifiability of the models.
