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Found 5 papers, 0 papers with code
Does Sparsity Help in Learning Misspecified Linear Bandits?
In particular, Du et al. (2020) show that even if a learner is given linear features in $\mathbb{R}^d$ that approximate the rewards in a bandit or RL with a uniform error of $\varepsilon$, searching for an $O(\varepsilon)$-optimal action requires pulling at least $\Omega(\exp(d))$ queries.
Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs
This global cache allows in-GPU importance sampling of mini-batches, which drastically reduces the number of nodes in a mini-batch, especially in the input layer, to reduce data copy between CPU and GPU and mini-batch computation without compromising the training convergence rate or model accuracy.
Faster Activity and Data Detection in Massive Random Access: A Multi-armed Bandit Approach
In this paper, we develop multi-armed bandit approaches for more efficient detection via coordinate descent, which make a delicate trade-off between exploration and exploitation in coordinate selection.
Blind Over-the-Air Computation and Data Fusion via Provable Wirtinger Flow
Over-the-air computation (AirComp) shows great promise to support fast data fusion in Internet-of-Things (IoT) networks.
Nonconvex Demixing From Bilinear Measurements
We consider the problem of demixing a sequence of source signals from the sum of noisy bilinear measurements.
