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Found 6 papers, 4 papers with code
Adaptive Data Depth via Multi-Armed Bandits
For example, we may want to find the most central point in a data set (a generalized median), or to identify and remove all outliers (points on the fringe of the data set with low depth).
Approximate Function Evaluation via Multi-Armed Bandits
We design an instance-adaptive algorithm that learns to sample according to the importance of each coordinate, and with probability at least $1-\delta$ returns an $\epsilon$ accurate estimate of $f(\boldsymbol{\mu})$.
Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification
For such applications, a common approach is to organize these labels into a tree, enabling training and inference times that are logarithmic in the number of labels.
Adaptive Learning of Rank-One Models for Efficient Pairwise Sequence Alignment
The second ingredient is to utilise a multi-armed bandit algorithm to adaptively refine this spectral estimator only for read pairs that are likely to have large alignments.
Ultra Fast Medoid Identification via Correlated Sequential Halving
Four to five orders of magnitude gains over exact computation are obtained on real data, in terms of both number of distance computations needed and wall clock time.
Bandit-Based Monte Carlo Optimization for Nearest Neighbors
The celebrated Monte Carlo method estimates an expensive-to-compute quantity by random sampling.
