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Found 7 papers, 2 papers with code
On Interpolating Experts and Multi-Armed Bandits
For a vector $\mathbf{m}=(m_1,\dots, m_K)\in \mathbb{N}^K$, an instance of $\mathbf{m}$-MAB indicates that the arms are partitioned into $K$ groups and the $i$-th group contains $m_i$ arms.
Improved Algorithms for Bandit with Graph Feedback via Regret Decomposition
The problem of bandit with graph feedback generalizes both the multi-armed bandit (MAB) problem and the learning with expert advice problem by encoding in a directed graph how the loss vector can be observed in each round of the game.
Information-theoretic Classification Accuracy: A Criterion that Guides Data-driven Combination of Ambiguous Outcome Labels in Multi-class Classification
While practitioners commonly combine ambiguous outcome labels for all data points (instances) in an ad hoc way to improve the accuracy of multi-class classification, there lacks a principled approach to guide the label combination for all data points by any optimality criterion.
Understanding Bandits with Graph Feedback
We propose the notions of the fractional weak domination number $\delta^*$ and the $k$-packing independence number capturing upper bound and lower bound for the regret respectively.
Distributed Bayesian Matrix Decomposition for Big Data Mining and Clustering
Such a method should scale up well, model the heterogeneous noise, and address the communication issue in a distributed system.
Matrix Normal PCA for Interpretable Dimension Reduction and Graphical Noise Modeling
However, most of the existing methods only assume that the noise is correlated in the feature space while there may exist two-way structured noise.
Bayesian Joint Matrix Decomposition for Data Integration with Heterogeneous Noise
A few of matrix decomposition methods have been extended for such multi-view data integration and pattern discovery.
