REMEDI is a new benchmark for evaluating machine unlearning in multi-label clinical disease inference on MIMIC-III data that reveals trade-offs in existing methods.
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Introduces interference-aware multi-task unlearning with task-aware gradient projection and instance-level gradient orthogonalization, reducing interference scores by 30.3% and 52.9% on vision benchmarks.
Class-level unlearning shortcuts via bias suppression in the classification head; new bias-aware training mechanisms and bias-specific metrics are introduced to diagnose and reduce this dependence.
citing papers explorer
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REMEDI: A Benchmark for Retention and Unlearning Evaluation in Multi-label Clinical Disease Inference
REMEDI is a new benchmark for evaluating machine unlearning in multi-label clinical disease inference on MIMIC-III data that reveals trade-offs in existing methods.
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Interference-Aware Multi-Task Unlearning
Introduces interference-aware multi-task unlearning with task-aware gradient projection and instance-level gradient orthogonalization, reducing interference scores by 30.3% and 52.9% on vision benchmarks.
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Classification-Head Bias in Class-Level Machine Unlearning: Diagnosis, Mitigation, and Evaluation
Class-level unlearning shortcuts via bias suppression in the classification head; new bias-aware training mechanisms and bias-specific metrics are introduced to diagnose and reduce this dependence.