Sujay Khandagale — accepted AI-conference papers

2023

When Do Neural Nets Outperform Boosted Trees on Tabular Data?

Duncan C. McElfresh, Sujay Khandagale, Jonathan Valverde, Vishak Prasad C, Ganesh Ramakrishnan, Micah Goldblum +1

NeurIPS 2023poster

Tabular data is one of the most commonly used types of data in machine learning. Despite recent advances in neural nets (NNs) for tabular data, there is still an active discussion on whether or not NNs generally outperform gradient-boosted decision trees (GBDTs) on tabular data, with several recent…

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2022

On the Generalizability and Predictability of Recommender Systems

Duncan C. McElfresh, Sujay Khandagale, Jonathan Valverde, John P Dickerson, Colin White

NeurIPS 2022accept

While other areas of machine learning have seen more and more automation, designing a high-performing recommender system still requires a high level of human effort. Furthermore, recent work has shown that modern recommender system algorithms do not always improve over well-tuned baselines. A natura…

Cited by 11 Source ↗PDF ↗code ↗Scholar ↗

2022

Unsupervised Stem-based Cross-lingual Part-of-Speech Tagging for Morphologically Rich Low-Resource Languages

Ramy Eskander, Cass Lowry, Sujay Khandagale, Judith Klavans, Maria Polinsky, Smaranda Muresan

NAACL 2022long

Unsupervised cross-lingual projection for part-of-speech (POS) tagging relies on the use of parallel data to project POS tags from a source language for which a POS tagger is available onto a target language across word-level alignments. The projected tags then form the basis for learning a POS mode…

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2021

Synthetic Benchmarks for Scientific Research in Explainable Machine Learning

Yang Liu, Sujay Khandagale, Colin White, Willie Neiswanger

NeurIPS 2021poster

As machine learning models grow more complex and their applications become more high-stakes, tools for explaining model predictions have become increasingly important. This has spurred a flurry of research in model explainability and has given rise to feature attribution methods such as LIME and SHA…

Cited by 86 Source ↗code ↗Scholar ↗