@conference {cBonet23, title = {HyperFast: Instant Classification for Tabular Data}, booktitle = {38th Annual AAAI Conference on Artificial Intelligence (AAAI)}, year = {2024}, month = {12/2024}, abstract = {

Training deep learning models and performing hyperparameter tuning can be computationally demanding and time-consuming. Meanwhile, traditional machine learning methods like gradient-boosting algorithms remain the preferred choice for most tabular data applications, while neural network alternatives require extensive hyperparameter tuning or work only in toy datasets under limited settings. In this paper, we introduce HyperFast, a meta-trained hypernetwork designed for instant classification of tabular data in a single forward pass. HyperFast generates a task-specific neural network tailored to an unseen dataset that can be directly used for classification inference, removing the need for training a model. We report extensive experiments with OpenML and genomic data, comparing HyperFast to competing tabular data neural networks, traditional ML methods, AutoML systems, and boosting machines. HyperFast shows highly competitive results, while being significantly faster. Additionally, our approach demonstrates robust adaptability across a variety of classification tasks with little to no fine-tuning, positioning HyperFast as a strong solution for numerous applications and rapid model deployment. HyperFast introduces a promising paradigm for fast classification, with the potential to substantially decrease the computational burden of deep learning.

}, author = {Bonet, David and Mas-Montserrat, Daniel and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @unpublished {xBonet21, title = {Improved Neural Network Generalization using Channel-Wise NNK Graph Constructions}, year = {2021}, publisher = {Final Year Project, UPC}, abstract = {

State-of-the-art neural network architectures continue to scale in size and deliver impressive results on unseen data points at the expense of poor interpretability. In the deep layers of these models we often encounter very high dimensional feature spaces, where constructing graphs from intermediate data representations can lead to the well-known curse of dimensionality. We propose a channel-wise graph construction method that works on lower dimensional subspaces and provides a new channel-based perspective that leads to better interpretability of the data and relationship between channels. In addition, we introduce a novel generalization estimate based on the proposed graph construction method with which we perform local polytope interpolation. We show its potential to replace the standard generalization estimate based on validation set performance to perform progressive channel-wise early stopping without requiring a validation set.

}, author = {Bonet, David}, editor = {Ortega, Antonio and Ruiz-Hidalgo, J. and Shekkizhar, Sarath} }