Discover and read the best of Twitter Threads about #reliableml
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Understanding data quality is crucial for reliable ML. In our #ICML2022 paper, @NabeelSeedat01, @JonathanICrabbe & @MihaelaVDS present a Data-Centric framework for the understudied problem of identifying incongruous examples of in-distribution data.
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TLDR.
*Do you want to know which examples will be reliably predicted, independent of the downstream predictive model?
* Do you want to get insights into your data to understand possible limitations?
If so, Data-SUITE our new #DataCentricAI framework is for you!
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*Do you want to know which examples will be reliably predicted, independent of the downstream predictive model?
* Do you want to get insights into your data to understand possible limitations?
If so, Data-SUITE our new #DataCentricAI framework is for you!
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There has been a significant focus on out-of-distribution data (OOD) for reliable ML.
However, in Data-SUITE we tackle an equally important yet understudied problem.
How do we assess In-Distribution data, with feature space heterogeneity?
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However, in Data-SUITE we tackle an equally important yet understudied problem.
How do we assess In-Distribution data, with feature space heterogeneity?
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