Active Learning-Based Assignment Strategies

• Active Learning-Based Assignment Strategies are approaches that assign tasks based on uncertainty metrics to maximize model performance under practical labeling constraints.
• They integrate noise-aware, min–max, and joint sampling models which efficiently match queries with labelers while controlling for annotation noise and capacity limitations.
• Empirical evaluations demonstrate significant gains in F1 scores and annotation efficiency, highlighting their impact in machine learning, crowdsourcing, and educational applications.

Active learning-based assignment strategies constitute a set of methodologies for efficiently assigning query points or learning activities to annotators, students, or system agents so as to optimize objectives such as model performance, annotation efficiency, or engagement—all under significant practical constraints. These strategies are foundational in machine learning, crowdsourcing, and education, accommodating imperfections in annotators, multi-user demands, and real-world constraints on cost, assignment accuracy, and user experience.

1. Formal Problem Setting and Key Parameters

Active learning-based assignment divides into several core scenarios: annotation allocation in the presence of heterogeneous or noisy labelers, adaptive task assignment in crowdsourcing, and the design of active, personalized learning activities.

Annotation and Labeler Assignment

Let $U_t = \{x_1, \ldots, x_{u_t}\}$ denote the unlabeled pool in active learning cycle $t$, and $M$ the set of available oracles (labelers), each with known or estimated accuracy $a_i \in [0,1]$ and per-cycle capacity $c_i$. The estimated entropy $e_j$ of point $x_j$ (under the current classifier) quantifies query informativeness but also correlates with labeler confusion. The objective is to minimize model error or maximize F1 given a fixed querying budget while constraining noisy assignments.

Assignment Variables and Constraints

Assignment variables $z_{ij}\in\{0,1\}$ indicate if data point $x_j$ is assigned to labeler $i$. Constraints govern:

• Single labeling: $\sum_{i=1}^M z_{ij} = 1,\;\forall j$
• Capacity: $\sum_{j=1}^{N_t} z_{ij} \leq c_i,\;\forall i$
• Noise-acceptance: $\epsilon(a_i, e_j)z_{ij} \leq \beta$, bounding label error via a function $\epsilon$ decreasing in $a$ and increasing in $e$ and a user-specified $\beta$ (Ahadi et al., 14 Dec 2025).

Task Selection in Crowdsourcing

For binary tasks with label aggregation via weighted voting, assignment strategies select both the next task and the most suitable worker under a contextual bandit model, optimizing for accuracy given individual context-dependent reliabilities (Zhang et al., 2015). Task selection leverages uncertainty (least confidence, margin, or information density) metrics over provisional label posteriors.

2. Sampling and Assignment Methodologies

Min–Max Assignment Model

The central optimization model for robust assignment in the presence of label noise is

$$\min_{z} \max_{j \in [N_t]} \sum_{i=1}^M \epsilon(a_i, e_j) z_{ij}$$

subject to labeling and capacity constraints (Ahadi et al., 14 Dec 2025).

The min–max form penalizes high-noise outliers in any assignment round. The optimal solution, by exchange arguments, is a sorted assignment (“highest entropy to highest accuracy labelers”) matching labelers and queries in order of quality and informativeness.

Joint Sampling and Assignment with Noise Control

When sampling is integrated, the following constrained maximization applies:

$\max_{y} \sum_{j=1}^{u_t} \sum_{i=1}^M e_j y_{ij}$

subject to per-labeler capacity, one-to-one assignments, and the noise constraint $\epsilon(a_i, e_j) y_{ij} \leq \beta$ (Ahadi et al., 14 Dec 2025).

Efficient two-pointer algorithms yield the provably-optimal sampling plus assignment configuration in $O(\sum c_i + u_t)$ time per cycle, ensuring no assigned pair violates the maximum tolerable noise.

Active Task Selection in Crowdsourcing

Weighted-vote confidence scores $\overline{y}_i^{+}, \overline{y}_i^{-}$ guide selection:

• Least confidence: select task with lowest $\max(\overline{y}_i^{+}, \overline{y}_i^{-})$
• Margin: select smallest $|\overline{y}_i^{+} - \overline{y}_i^{-}|$
• Information density: modulate based on task context prevalence

Empirical benchmarks confirm least-confidence as the most consistent, robust rule (Zhang et al., 2015).

Hybrid and Learned Query Policies

Meta-learning and imitation learning approaches learn to rank batches or single samples using neural scoring functions trained on synthetic expert rollouts (e.g., via behavioral cloning where the expert is the “oracle” that always picks the greatest local gain in model accuracy) (Gonsior et al., 2021). Reinforcement learning (MDP/Q-learning) formulations allow transfer of non-myopic active learning policies across domains (Konyushkova et al., 2018).

3. Empirical and Theoretical Comparisons

Benchmarking of optimal noise-aware assignment (random sampling + random assignment, entropy sampling + random vs. optimal assignment) demonstrates that noise-minimizing joint assignment (OLAS) yields sharply higher and more stable F1, e.g., from $\simeq0.33 \rightarrow 0.78$ on UCI datasets, with rapid convergence to full-data upper bounds in industrial datasets (Ahadi et al., 14 Dec 2025). Theorem 1 guarantees assignment minimizes worst-case label-noise per cycle, extended to the joint sampling + assignment scenario by Theorem 2.

Task selection integrated with contextual bandit assignment yields superior accuracy to margin-based or random choice (Zhang et al., 2015). Empirically, least confidence outperforms margin, with accuracy gains of 3–5 percentage points at saturation.

Learning-to-rank and RL-based policies outperform handcrafted heuristics and random assignment across diverse settings, with single-sample neural policies (e.g., ImitAL) ranking best or second-best on 12–14/15 datasets, and delivering up to $10\times$ speedups over classic margin sampling (Gonsior et al., 2021, Konyushkova et al., 2018). RL strategies consistently save 30–35% of annotation budget over standard active learners (Konyushkova et al., 2018).

4. Application Domains and Context-Specific Constraints

Crowdsourcing

Heterogeneous reliability and limited budget dictate a trade-off between exploring worker reliability, refining models, and maximizing per-budget classification accuracy (Zhang et al., 2015).

Noisy Oracles

Assignment under imperfect labelers is critical in scientific and industrial settings with expensive or error-prone annotators. Noise modeling via $\epsilon(a,e)$ and enforcing $\epsilon \leq \beta$ as part of the assignment constraint ensures that learning proceeds only with tolerable label noise (Ahadi et al., 14 Dec 2025).

Multi-label and Complex Outputs

In domains such as ICD-9 code assignment for clinical texts, instance selection is complicated by multilabel predictions. Aggregation of uncertainty (mean/mode of least confidence or entropy) and clustering-informed correlations are empirically superior. Clustering-based assignment (e.g., $k$-means++) outperforms uncertainty-only assignment under typical multi-label settings, enabling >90% reduction in manual annotation requirements while preserving full-data performance (Ferreira et al., 2021).

5. Extensions: Beyond Labeling to Educational and User-Centered Assignment

Adaptive assignment in educational settings targets dual objectives: efficient model learning and appropriate user experience. Joint-utility sampling combines system and user utilities, $U_\mathrm{sys}$ and $U_\mathrm{usr}$, via trade-off or multiplicative combination to select exercises that both maximize model uncertainty (for learning) and match the learner's proficiency (for engagement and appropriateness) (Lee et al., 2020). Tuning of trade-off weights ($\lambda$) can be annealed to bias initial sampling toward rapid model training, then shift focus to user satisfaction.

Active-learning-based assignment is also foundational in group work, collaborative problem-solving, and active learning classrooms. Synchronous, role-structured group assignments significantly improve compliance, reduce missing work, and mitigate unauthorized resource usage compared to individual assignments (Hettiarachchilage et al., 2024).

6. Best Practices and Limitations

Recommended operational practices for assignment strategies include:

• Use sorted/noise-aware assignment to pair most challenging samples with most accurate labelers (Ahadi et al., 14 Dec 2025).
• Prefer least-confidence over margin for task selection under worker reliability uncertainty (Zhang et al., 2015).
• For large-scale annotation with diverse data and annotation costs, deploy meta-learned or imitation-learned query policies for plug-and-play active learner deployment (Gonsior et al., 2021, Konyushkova et al., 2018).
• In multilabel or feature-rich settings, support instance selection with clustering- or core-set methods to maximize label efficiency (Ferreira et al., 2021).

Limitations involve computational cost (all-pool inference for very large $U$), need for accurate labeler/reliability estimates, and the off-policy generalization gap in learned policies. In educational adaptation, purely light-guilded simulation activities showed limited learning gains over text-alone assignments but improved engagement, suggesting that careful alignment of assignment scaffolding with learning objectives is crucial (Stang et al., 2016, Jacobson et al., 2022).

7. Future Directions and Open Problems

Active learning-based assignment research directions include multi-step lookahead using NTK for computationally-efficient expected-model-change acquisition (Mohamadi et al., 2022), joint optimization of user and system objectives with dynamic trade-offs (Lee et al., 2020), plug-and-play RL/metapolicy transfer (Konyushkova et al., 2018), and integration of uncertainty-aware assignment in core-set selection, clustering, and hybrid acquisition pipelines.

Integration of active assignment into collaborative and group learning, personalized research skill development, and multimodal peer feedback represents active areas of application, particularly given the strong empirical evidence for gains in engagement, compliance, and conceptual mastery under structured assignment protocols (Ariza, 2024, Hettiarachchilage et al., 2024, Jacobson et al., 2022).