Multi-Faceted Modeling Approach Questions
Modeling strategies that integrate multiple perspectives, modalities, or objectives to build more robust predictive systems. Covers ensemble methods, multi-task learning, multimodal data fusion, and orchestration of heterogeneous models within production ML pipelines.
MediumTechnical
0 practiced
During multi-task training you observe conflicting gradients between tasks that hurt convergence. Describe algorithmic methods to mitigate gradient conflicts (PCGrad, GradNorm, gradient projection, task grouping) and discuss trade-offs for production (compute, complexity, stability).
MediumTechnical
0 practiced
One component in your heterogeneous ensemble shows disparate impact on a protected group while other components do not. How would you measure and mitigate fairness issues at the ensemble level, balancing overall utility and fairness constraints? Include mitigation techniques and testing approaches.
EasyTechnical
0 practiced
Explain ensemble methods (bagging, boosting, stacking) and why they often improve predictive performance in applied ML. In your answer, relate to the bias-variance trade-off, model diversity, and practical production considerations such as inference cost, maintenance complexity, and debugging of ensemble components.
HardTechnical
0 practiced
An attacker can perturb one modality (e.g., image) at inference time to mislead a multimodal ensemble. Propose detection strategies, robust fusion rules (e.g., modality redundancy, gating resilience), and certification approaches (e.g., randomized smoothing across modalities or ensembles) to improve robustness while preserving model performance.
EasyTechnical
0 practiced
Explain knowledge distillation and why it's useful when deploying ensembles or large multimodal teacher models. Describe which teacher signals you can use (soft logits, intermediate features, attention maps) and what constraints you must consider when training a student model for edge deployment.
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