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This paper proposes separating generation from selection in explainable-recommendation systems to reduce serving costs, using a frozen candidate pool of explanations and a small CPU-resident selector. It benchmarks offline-pool selectors and finds that pairwise learning-to-rank outperforms single-action RL formulations like PPO, GRPO, and DPO in terms of F1 scores.
This paper introduces DonorRank, a learning-to-rank framework for selecting effective donor languages in low-resource cross-lingual speech recognition, evaluated on Indic and African language corpora. It demonstrates improved donor selection over genetic-similarity and high-resource heuristics, and provides insights into transfer patterns for multilingual ASR.
A research paper presenting a learning-to-rank framework for selecting efficient tensor-network contraction plans for GPU-accelerated quantum circuit simulation, using gradient-boosted rankers trained from GPU measurements.
This paper proposes Representation Curriculum (RC), a training-time intervention that stages feature utilization to reduce over-reliance on exposure-confounded historical signals and improve cold-start generalization in ranking systems. The method is theoretically analyzed and validated on public benchmarks and large-scale eBay search experiments.
PEARL introduces a contrastive percentile approximation framework to mitigate behavioral intensity imbalance in recommender systems, achieving significant gains in engagement metrics in a production livestream platform serving billions of users.
This paper proposes an Edge-AI-driven decentralized task allocation framework for circular smart manufacturing that uses learning-to-rank to align with the ordering-based nature of winner selection. Simulation results show improved delay, deadline adherence, and energy efficiency under high-load and tight-deadline scenarios.