#submodular-optimization News & Analysis

4 articles tagged with #submodular-optimization. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AIBullisharXiv – CS AI · Apr 107/10

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SPICE: Submodular Penalized Information-Conflict Selection for Efficient Large Language Model Training

Researchers introduce SPICE, a data selection algorithm that reduces large language model training data requirements by 90% while maintaining performance by identifying and minimizing gradient conflicts between training samples. The method combines information-theoretic principles with practical efficiency improvements, enabling effective model tuning on just 10% of typical datasets across multiple benchmarks.

AINeutralarXiv – CS AI · May 126/10

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Efficient Ensemble Selection from Binary and Pairwise Feedback

Researchers present new algorithms for efficiently selecting small, high-performing ensembles of AI systems using minimal model evaluations. The work addresses both binary feedback (correct/incorrect outcomes) and pairwise feedback (preference comparisons), providing theoretical guarantees and practical query-saving methods validated through LLM experiments.

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AINeutralarXiv – CS AI · May 116/10

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A Resilience Framework for Bi-Criteria Combinatorial Optimization with Bandit Feedback

Researchers introduce a resilience framework for bi-criteria combinatorial optimization under noisy conditions, extending bandit feedback algorithms from single-objective to multi-objective settings. The framework achieves sublinear regret bounds without requiring structural assumptions like linearity or submodularity, with potential applications to constrained optimization problems in machine learning and algorithmic decision-making.

AINeutralarXiv – CS AI · May 116/10

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$\gamma$-weakly $\theta$-up-concavity: A Unified Framework for Non-Convex Optimization Beyond DR-Submodular and OSS Functions

Researchers introduce γ-weakly θ-up-concavity, a mathematical framework that unifies optimization approaches for non-convex functions by generalizing DR-submodular and One-Sided Smooth functions. The framework proves these functions are upper-linearizable, enabling improved approximation guarantees for both offline and online optimization problems across various constraint structures.