Our algorithm can improve safety and performance by flagging regretful predictions for abstention or data cleaning.

For example, we demonstrate that, by abstaining from prediction using our algorithm, we can reduce mistakes compared to standard approaches:

We develop a method that trains models over plausible clean datasets to anticipate regretful predictions, helping us spot when a model is unreliable at the individual-level.

We capture this effect with a simple measure: regret.

Regret is inevitable with label noise, but it can tell us where models silently fail, and how we can guide safer predictions

We can frame this problem as learning from noisy labels.

Plenty of algorithms have been designed to handle label noise by predicting well on average, but we show how they still fail on specific individuals.

Many ML models predict labels that don’t reflect what we care about, e.g.:
– Diagnoses from unreliable tests
– Outcomes from noisy electronic health records

In a new paper w/@berkustun, we study how this subjects individuals to a lottery of mistakes.
Paper: bit.ly/3Y673uZ
🧵👇

Our algorithm can improve safety and performance by flagging regretful predictions for abstention or for data cleaning. For example, we demonstrate how abstaining from prediction on these instances can reduce mistakes compared to standard approaches:

We develop a method to anticipate regretful predictions by training models over plausible clean datasets.

This helps us spot when a model is unreliable at the individual-level.

We capture this effect with a simple measure: regret.

Regret is inevitable with label noise -- it tells us where models silently fail, and how we can guide safer predictions.

We can frame this as learning from noisy labels.

Plenty of algorithms have been designed to handle label noise by predicting well on average —
But we show how they can still fail on specific individuals.