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Found 9 papers, 0 papers with code
Influencing Bandits: Arm Selection for Preference Shaping
For the case of binary opinions, two types of opinion dynamics are considered -- decreasing elasticity (modeled as a Polya urn with increasing number of balls) and constant elasticity (using the voter model).
On the Regret of Online Edge Service Hosting
The total cost incurred by the system is modeled as a combination of the rent cost, the service cost incurred due to latency in serving customers, and the fetch cost incurred as a result of the bandwidth used to fetch the code/databases of the service from the cloud servers to host the service at the edge.
Multi-Model Federated Learning with Provable Guarantees
Federated Learning (FL) is a variant of distributed learning where edge devices collaborate to learn a model without sharing their data with the central server or each other.
Multi-Model Federated Learning
Specifically, every client is able to train any one of M models at a time and the server maintains a model for each of the M models which is typically a suitably averaged version of the model computed by the clients.
Online Partial Service Hosting at the Edge
We propose an online policy called $\alpha$-RetroRenting ($\alpha$-RR) which dynamically determines the fraction of the service to be hosted at the edge in any time-slot, based on the history of the request arrivals and the rent cost sequence.
Edge-computing
Networking and Internet Architecture
Greedy k-Center from Noisy Distance Samples
We study a variant of the canonical k-center problem over a set of vertices in a metric space, where the underlying distances are apriori unknown.
Dynamic social learning under graph constraints
We introduce a model of graph-constrained dynamic choice with reinforcement modeled by positively $\alpha$-homogeneous rewards.
Contextual Bandits Evolving Over Finite Time
Contextual bandits have the same exploration-exploitation trade-off as standard multi-armed bandits.
Request Patterns and Caching for VoD Services with Recommendation Systems
We propose a Markovian request model to capture the time-correlation in user requests and show that our model is consistent with the observations of existing empirical studies.
Networking and Internet Architecture
