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Found 6 papers, 1 papers with code
Efficient Algorithms for Device Placement of DNN Graph Operators
However, for such settings (large models and multiple heterogeneous devices), we require automated algorithms and toolchains that can partition the ML workload across devices.
Multi-scale Online Learning and its Applications to Online Auctions
For the online posted pricing problem, we show regret bounds that scale with the best fixed price, rather than the range of the values.
Linear Contextual Bandits with Knapsacks
We consider the linear contextual bandit problem with resource consumption, in addition to reward generation.
An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives
This problem was introduced by Badanidiyuru et al. (2014), who gave a computationally inefficient algorithm with near-optimal regret bounds for it.
Fast Algorithms for Online Stochastic Convex Programming
We introduce the online stochastic Convex Programming (CP) problem, a very general version of stochastic online problems which allows arbitrary concave objectives and convex feasibility constraints.
Bandits with concave rewards and convex knapsacks
In this paper, we consider a very general model for exploration-exploitation tradeoff which allows arbitrary concave rewards and convex constraints on the decisions across time, in addition to the customary limitation on the time horizon.
