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Cost-Efficient Online Decision Making: A Combinatorial Multi-Armed Bandit Approach
In this paper, we provide a novel formulation of the online decision making problem based on combinatorial multi-armed bandits and take the (possibly stochastic) cost of performing tests into account.
Efficient Online Decision Tree Learning with Active Feature Acquisition
Specifically, we employ a surrogate information acquisition function based on adaptive submodularity to actively query feature values with a minimal cost, while using a posterior sampling scheme to maintain a low regret for online prediction.
Analysis of Knowledge Transfer in Kernel Regime
Knowledge transfer is shown to be a very successful technique for training neural classifiers: together with the ground truth data, it uses the "privileged information" (PI) obtained by a "teacher" network to train a "student" network.
Do Kernel and Neural Embeddings Help in Training and Generalization?
The approximated representations induced by these kernels are fed to the neural network and the optimization and generalization properties of the final model are evaluated and compared.
Recovery Bounds on Class-Based Optimal Transport: A Sum-of-Norms Regularization Framework
We develop a novel theoretical framework for understating OT schemes respecting a class structure.
