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researcher

Christopher Lee-Messer

Stanford University

5 papers hereh-index 7626 citations16 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author4

Across the 4 of 5 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.CV1
affiliations
  • Stanford University
ORCID 0000-0002-2938-6184

identity via Semantic Scholar / OpenAlex

activity
20192023
most citedDomino: Discovering Systematic Errors with Cross-Modal Embeddings

39 citations · 40 across the 4 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2023★ 1 cited

Towards trustworthy seizure onset detection using workflow notes

Khaled Saab, Siyi Tang, Mohamed Taha +3

A major barrier to deploying healthcare AI models is their trustworthiness. One form of trustworthiness is a model's robustness across different subgroups: while existing models ma…

cs.LG2022★ 39 cited

Domino: Discovering Systematic Errors with Cross-Modal Embeddings

Sabri Eyuboglu, Maya Varma, Khaled Saab +5

Machine learning models that achieve high overall accuracy often make systematic errors on important subsets (or slices) of data. Identifying underperforming slices is particularly…

cs.LG2021

Double Descent Optimization Pattern and Aliasing: Caveats of Noisy Labels

Florian Dubost, Erin Hong, Max Pike +5

Optimization plays a key role in the training of deep neural networks. Deciding when to stop training can have a substantial impact on the performance of the network during inferen…

cs.LG2019

Cross-Modal Data Programming Enables Rapid Medical Machine Learning

Jared Dunnmon, Alexander Ratner, Nishith Khandwala +8

Labeling training datasets has become a key barrier to building medical machine learning models. One strategy is to generate training labels programmatically, for example by applyi…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.