performance guarantee
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FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression Y ouguang Chen George Biros Oden Institute for Computational Engineering and Sciences The University of Texas at Austin
Based on our theoretical analysis, we propose an active learning algorithm that employs regret minimization to minimize the FIR.
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Piecewise-Stationary Bandits with Knapsacks
Bwk work Liu et al. (2022), we do not require a bounded global variation.
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Improving Online Algorithms via ML Predictions
Manish Purohit, Zoya Svitkina, Ravi Kumar
There aretwointeresting andwell-studied computational paradigms aimed attackling uncertainty.
ApproximateValueEquivalence
This gives rise to a rich collection oftopological relationships and conditions under which VE models are optimal for planning. Despite this effort, relatively little is known about the planning performance of models that fail to satisfy these conditions.
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