Search Results for author:
Found 7 papers, 0 papers with code
Multi-Agent Bandit Learning through Heterogeneous Action Erasure Channels
To our knowledge, these are the first algorithms capable of effectively learning through heterogeneous action erasure channels.
Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms
When the context distribution is unknown, we establish an algorithm that reduces the stochastic contextual instance to a sequence of linear bandit instances with small misspecifications and achieves nearly the same worst-case regret bound as the algorithm that solves the misspecified linear bandit instances.
Differentially Private Stochastic Linear Bandits: (Almost) for Free
In the shuffled model, we also achieve regret of $\tilde{O}(\sqrt{T}+\frac{1}{\epsilon})$ %for small $\epsilon$ as in the central case, while the best previously known algorithm suffers a regret of $\tilde{O}(\frac{1}{\epsilon}{T^{3/5}})$.
Learning in Distributed Contextual Linear Bandits Without Sharing the Context
Contextual linear bandits is a rich and theoretically important model that has many practical applications.
Solving Multi-Arm Bandit Using a Few Bits of Communication
Existing works usually fail to address this issue and can become infeasible in certain applications.
Quantizing data for distributed learning
In this paper, we propose an alternate approach to learn from distributed data that quantizes data instead of gradients, and can support learning over applications where the size of gradient updates is prohibitive.
On Distributed Quantization for Classification
We consider the problem of distributed feature quantization, where the goal is to enable a pretrained classifier at a central node to carry out its classification on features that are gathered from distributed nodes through communication constrained channels.
