Search Results for author:
Found 7 papers, 6 papers with code
Large Language Models to Enhance Bayesian Optimization
Bayesian optimization (BO) is a powerful approach for optimizing complex and expensive-to-evaluate black-box functions.
GOGGLE: Generative Modelling for Tabular Data by Learning Relational Structure
Specifically, we introduce GOGGLE, an end-to-end message passing scheme that jointly learns the relational structure and corresponding functional relationships as the basis of generating synthetic samples.
Learning Representations without Compositional Assumptions
This paper addresses unsupervised representation learning on tabular data containing multiple views generated by distinct sources of measurement.
TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization
In this work, we introduce Tabular Neural Gradient Orthogonalization and Specialization (TANGOS), a novel framework for regularization in the tabular setting built on latent unit attributions.
Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes
It is important to guarantee that machine learning algorithms deployed in the real world do not result in unfairness or unintended social consequences.
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection
Consider the problem of imputing missing values in a dataset.
Epileptic Seizure Classification with Symmetric and Hybrid Bilinear Models
Epilepsy affects nearly 1% of the global population, of which two thirds can be treated by anti-epileptic drugs and a much lower percentage by surgery.
