Loss Functions, Behaviors & Selection Questions
Loss function design, evaluation, and selection in machine learning. Includes common loss functions (MSE, cross-entropy, hinge, focal loss), how loss properties affect optimization and gradient flow, issues like class imbalance and label noise, calibration, and practical guidance for choosing the most appropriate loss for a given task and model.
HardTechnical
0 practiced
Design a composite loss for multi-object tracking that includes (1) detection classification loss, (2) bounding-box regression loss (L1/GIoU), (3) identity consistency loss (e.g., cross-entropy over track IDs or triplet loss), and (4) motion smoothness regularizer. Explain weighting strategy, staged vs joint training, and how you would validate each component in offline and online evaluation.
MediumSystem Design
0 practiced
Design a monitoring system that tracks per-class loss, loss decomposition (e.g., classification vs regression terms), gradient norms, and concept drift signals in production. Specify what to log (granularity), retention policies, alerting thresholds, sample rates, dashboards, and how to tie observed loss changes to upstream data or code changes.
HardTechnical
0 practiced
Discuss trade-offs between probabilistic (NLL) losses that produce calibrated probabilities and discriminative losses that target classification accuracy. In a cost-sensitive scenario, explain how to use predicted probabilities to make optimal decisions and whether training directly for expected cost (cost-weighted loss) can yield better operational outcomes.
HardTechnical
0 practiced
How can choice of loss amplify or mitigate demographic bias? Describe loss-based mitigation strategies such as group-reweighted loss, adversarial de-biasing (minimizing predictive power of group attributes), and constrained optimization (e.g., equalized odds constraints). Discuss theoretical limitations and how you'd monitor and communicate residual risks to stakeholders.
MediumTechnical
0 practiced
Design a loss-weighting strategy for a multi-task model where tasks have different units and importance (for example: object detection classification loss, bounding-box regression loss, and semantic segmentation loss). Describe manual and automatic approaches (uncertainty weighting, GradNorm), show the formulas for at least one automatic method, and explain how you'd validate and implement this in a training loop.
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