Oco
PulseAugur coverage of Oco — every cluster mentioning Oco across labs, papers, and developer communities, ranked by signal.
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Minimax Alternating Regret for Experts Problem and OCO Resolved · 2 sources tracked
Researchers have resolved the minimax regret rate for the Experts Problem and Online Convex Optimization (OCO). For the d-expert problem, the minimax alternating regret is shown to be $\Theta(\log d)$, independent of th…
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New architecture unifies materials ontologies for regulatory compliance
Researchers have proposed a novel multi-level architecture for reusable materials ontologies, addressing fragmentation in the field. This architecture features independent classification axes for abstraction level and c…
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New arXiv Papers Detail Advances in Convex Optimization Techniques
Two new research papers on arXiv explore advancements in convex optimization. The first paper introduces a unified probing model for Online Convex Optimization (OCO) that can improve worst-case regret even with a sublin…
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New OOD detection method uses object co-occurrence for improved reliability
Researchers have developed a new framework called Object Co-occurrence (OCO) to improve out-of-distribution (OOD) detection in deep learning models. This method leverages the natural tendency for objects to appear toget…