Benjamin Feuer
Machine learning at NYU
Hi! I'm currently a PhD student in Machine Learning at New York University, advised by Prof. Chinmay Hegde..
Research: My research focus is data-centric; I try to discover and empirically validate the key factors underpinning good dataset design, and I create methods to improve the performance of machine learning algorithms on data-centric tasks.
Education: I am currently working towards my PhD in Computer Science at New York University, advised by Chinmay Hegde. Previously, I studied at Columbia University and Wesleyan University. My frequent collaborators include Micah Goldblum and Colin White and Juliana Freire.
News
- 2024/2/13 New paper (+ code) benchmarking the performance of tabular algorithms on the largest suite of datasets to date. NeurIPS 2023 (Datasets and Benchmarks).
- 2023/11/07 New paper studying the effects of two important dataset-level constituents: label set design, and class balance. NeurIPS 2023 (1st Workshop on Attributing Model Behavior at Scale) .
- 2023/10/28 New paper investigating sketching and feature-selection methods for prior-fitted networks. NeurIPS 2023 (Second Table Representation Learning Workshop) .
- 2023/10/27 New paper (+ code) introducing ArcheType, a simple, practical method for context sampling, prompt serialization, model querying, and label remapping, which enables large language models to solve CTA problems in a fully zero-shot manner. VLDB 2024.
- 2023/08/01 New paper introducing JANuS (Joint Annotations and Names Set), a collection of four new training datasets with images, labels, and corresponding captions, and conducting controlled investigations of factors contributing to robustness in image classification. TMLR 2023.