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Found 7 papers, 4 papers with code
BanditPAM: Almost Linear Time $k$-Medoids Clustering via Multi-Armed Bandits
Current state-of-the-art $k$-medoids clustering algorithms, such as Partitioning Around Medoids (PAM), are iterative and are quadratic in the dataset size $n$ for each iteration, being prohibitively expensive for large datasets.
BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits
In these experiments, we observe that BanditPAM returns the same results as state-of-the-art PAM-like algorithms up to 4x faster while performing up to 200x fewer distance computations.
MABSplit: Faster Forest Training Using Multi-Armed Bandits
We present an algorithm that accelerates the training of random forests and other popular tree-based learning methods.
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.
Private DNA Sequencing: Hiding Information in Discrete Noise
When an individual's DNA is sequenced, sensitive medical information becomes available to the sequencing laboratory.
Information Theory Cryptography and Security Information Theory
Faster Maximum Inner Product Search in High Dimensions
We provide theoretical guarantees that BanditMIPS returns the correct answer with high probability, while improving the complexity in $d$ from $O(\sqrt{d})$ to $O(1)$.
Utilizing Free Clients in Federated Learning for Focused Model Enhancement
Federated Learning (FL) is a distributed machine learning approach to learn models on decentralized heterogeneous data, without the need for clients to share their data.
