output
20132026
most citedTowards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools

271 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

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Showing cs.LGShow all

8 papers · 1 filter

cs.LG2026

DP-Rec: Towards Dynamic Patching for Efficient Long-Sequence Recommendation

Dwipam Katariya, Thomas Caputo, Akshat Shreemali +6

Transformers have redefined sequential recommendation by effectively modeling dynamic user behaviors and long-range dependencies. However, they remain inherently inefficient: stand…

cs.LG2024★ 2 cited

T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients

Evandro S. Ortigossa, Fábio F. Dias, Brian Barr +2

The development of machine learning applications has increased significantly in recent years, motivated by the remarkable ability of learning-powered systems to discover and genera…

cs.LG2022★ 8 cited

Making Intelligence: Ethical Values in IQ and ML Benchmarks

Borhane Blili-Hamelin, Leif Hancox-Li

In recent years, ML researchers have wrestled with defining and improving machine learning (ML) benchmarks and datasets. In parallel, some have trained a critical lens on the ethic…

cs.LG2022★ 7 cited

Should attention be all we need? The epistemic and ethical implications of unification in machine learning

Nic Fishman, Leif Hancox-Li

"Attention is all you need" has become a fundamental precept in machine learning research. Originally designed for machine translation, transformers and the attention mechanisms th…

cs.LG2021

Counterfactual Explanations via Latent Space Projection and Interpolation

Brian Barr, Matthew R. Harrington, Samuel Sharpe +1

Counterfactual explanations represent the minimal change to a data sample that alters its predicted classification, typically from an unfavorable initial class to a desired target…

cs.LG2020

Quantifying Challenges in the Application of Graph Representation Learning

Antonia Gogoglou, C. Bayan Bruss, Brian Nguyen +2

Graph Representation Learning (GRL) has experienced significant progress as a means to extract structural information in a meaningful way for subsequent learning tasks. Current app…

cs.LG2019★ 271 cited

Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools

Anh Truong, Austin Walters, Jeremy Goodsitt +3

There has been considerable growth and interest in industrial applications of machine learning (ML) in recent years. ML engineers, as a consequence, are in high demand across the i…

cs.LG2019★ 20 cited

Global Explanations of Neural Networks: Mapping the Landscape of Predictions

Mark Ibrahim, Melissa Louie, Ceena Modarres +1

A barrier to the wider adoption of neural networks is their lack of interpretability. While local explanation methods exist for one prediction, most global attributions still reduc…