← Search

Roman Levin

3 accepted papers

2023

A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning

NeurIPS 2023poster

Academic tabular benchmarks often contain small sets of curated features. In contrast, data scientists typically collect as many features as possible into their datasets, and even engineer new features from existing ones. To prevent over-fitting in subsequent downstream modeling, practitioners commo…

2023

Transfer Learning with Deep Tabular Models

ICLR 2023poster

Recent work on deep learning for tabular data demonstrates the strong performance of deep tabular models, often bridging the gap between gradient boosted decision trees and neural networks. Accuracy aside, a major advantage of neural models is that they are easily fine-tuned in new domains and learn…

2022

Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability

NeurIPS 2022accept

Conventional saliency maps highlight input features to which neural network predictions are highly sensitive. We take a different approach to saliency, in which we identify and analyze the network parameters, rather than inputs, which are responsible for erroneous decisions. We first verify that ide…