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Tag

#algorithms

5 articles tagged #algorithms

arxivMay 16

Adapting Dijkstra for Buffers and Unlimited Transfers

arXiv:2603.11729v3 Announce Type: replace-cross Abstract: In recent years, RAPTOR based algorithms have been considered the state-of-the-art for path-finding with unlimited transfers without preprocessing. However, this status largely stems from the evolution of routing research, where Dijkstra-base

#routing#algorithms#optimizationRead on arxiv →
arxivMay 12

Mistake-Bounded Language Generation

arXiv:2605.10809v1 Announce Type: new Abstract: We investigate the learning task of language generation in the limit, but shift focus from the traditional time-of-last-mistake metric of a generator's success to a new notion of "mistake-bounded generation." While existing results for language generat

#language-generation#machine-learning#algorithmsRead on arxiv →
arxivMay 11bullish

Simple KNN-Based Outlier Detection Achieves Robust Clustering

arXiv:2605.07130v1 Announce Type: new Abstract: Being robust to the presence of outliers is crucial for applying clustering algorithms in practice. In the $\textit{robust $k$-Means}$ problem (i.e., $k$-Means with outliers), the goal is to remove $z$ outliers and minimize the $k$-Means cost on the re

#clustering#outlier-detection#machine-learningRead on arxiv →
arxivApr 18

Tight Bounds for Learning Polyhedra with a Margin

arXiv:2604.14614v1 Announce Type: cross Abstract: We give an algorithm for PAC learning intersections of $k$ halfspaces with a $\rho$ margin to within error $\varepsilon$ that runs in time $\textsf{poly}(k, \varepsilon^{-1}, \rho^{-1}) \cdot \exp \left(O(\sqrt{n \log(1/\rho) \log k})\right)$. Notabl

#machine-learning#algorithms#data-structuresRead on arxiv →
arxivApr 2bullish

Learning to Shuffle: Block Reshuffling and Reversal Schemes for Stochastic Optimization

arXiv:2604.00260v1 Announce Type: new Abstract: Shuffling strategies for stochastic gradient descent (SGD), including incremental gradient, shuffle-once, and random reshuffling, are supported by rigorous convergence analyses for arbitrary within-epoch permutations. In particular, random reshuffling

LA1 model#optimization#machine-learning#researchRead on arxiv →
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