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OptimalBest-ArmIdentificationMethodsfor Tail-RiskMeasures
The algorithm requires solving a non-convex optimization problem inthespace ofprobability measures, thatrequires delicate analysis.
Cascading Contextual Assortment Bandits
These selected items are sequentially presented one at a time to a user. If the user clicks on a presented item, the cascading round ends.
Cascading Contextual Assortment Bandits
These selected items are sequentially presented one at a time to a user. If the user clicks on a presented item, the cascading round ends.
Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals
Surbhi Goel, Sushrut Karmalkar, Adam Klivans
Here we consider the more realistic scenario of empirical risk minimization or learning a ReLU with noise (often referred to as agnostically learning a ReLU). We assume that a learner has access to a training set from a joint distribution D on Rd R where the marginal distribution on Rd is Gaussian but the distribution on the labels can be arbitrary within [0,1].
Meta-Surrogate Benchmarking for Hyperparameter Optimization
Aaron Klein, Zhenwen Dai, Frank Hutter, Neil Lawrence, Javier Gonzalez
The model combines aprobabilistic encoder withamulti-task model suchthatitcan generate inexpensive and realistic tasks of the class of problems of interest.
