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This paper introduces a curvature-adaptive Follow-the-Perturbed-Leader (FTPL) algorithm for online optimization that achieves optimal regret bounds for both non-convex Lipschitz losses and strongly convex losses, using a time-varying perturbation scale.
Introduces BoxLitE, a knowledge base embedding model for DL-LiteH that leverages convex optimization to achieve weakly faithful embeddings. The paper shows that for any satisfiable DL-LiteH KB, a BoxLitE embedding exists with desirable faithfulness properties.
This paper introduces CLD, a lightweight convex optimization-based language detection head for ASR that achieves 97-98% accuracy with under 100 training samples while reducing compute costs by 13x, addressing accent and dialect robustness across 5 languages and 24 sub-dialects.