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Found 6 papers, 1 papers with code
Adaptive Step-Size Methods for Compressed SGD
Compressed Stochastic Gradient Descent (SGD) algorithms have been recently proposed to address the communication bottleneck in distributed and decentralized optimization problems, such as those that arise in federated machine learning.
Multiple Testing Framework for Out-of-Distribution Detection
While a number of tests for OOD detection have been proposed in prior work, a formal framework for studying this problem is lacking.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Autoequivariant Network Search via Group Decomposition
We address these problems by proving a new group-theoretic result in the context of equivariant neural networks that shows that a network is equivariant to a large group if and only if it is equivariant to smaller groups from which it is constructed.
Dynamic Spectrum Access using Stochastic Multi-User Bandits
In contrast to prior work, it is assumed that rewards can be non-zero even under collisions, thus allowing for the number of users to be greater than the number of channels.
Multi-User MABs with User Dependent Rewards for Uncoordinated Spectrum Access
Multi-user multi-armed bandits have emerged as a good model for uncoordinated spectrum access problems.
Decentralized Heterogeneous Multi-Player Multi-Armed Bandits with Non-Zero Rewards on Collisions
Within this setup, where the number of players is allowed to be greater than the number of arms, we present a policy that achieves near order-optimal expected regret of order $O(\log^{1 + \delta} T)$ for some $0 < \delta < 1$ over a time-horizon of duration $T$.
