Denied a loan, an interview, or an insurance claim by machine learning models? You may be entitled to a list of reasons. In our latest w @anniewernerfelt.bsky.social @berkustun.bsky.social @friedler.net, we show how existing explanation frameworks fail and present an alternative for recourse

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 🧵👇

🚨 Excited to announce a new paper accepted at #ICLR2025 in Singapore! “Learning Under Temporal Label Noise” We tackle a new challenge in time series ML: label noise that changes over time 🧵👇 arxiv.org/abs/2402.04398

Learning under Temporal Label Noise

Many time series classification tasks, where labels vary over time, are affected by label noise that also varies over time. Such noise can cause label quality to improve, worsen, or periodically chang...

arxiv.org