Pervasive AI for IoT applications: A survey on resource-efficient distributed artificial intelligence
Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of
Things (IoT) applications and services, spanning from recommendation systems and speech …
Things (IoT) applications and services, spanning from recommendation systems and speech …
Methods for robot behavior adaptation for cognitive neurorehabilitation
An estimated 11% of adults report experiencing some form of cognitive decline, which may
be associated with conditions such as stroke or dementia and can impact their memory …
be associated with conditions such as stroke or dementia and can impact their memory …
Hierarchical bayesian bandits
Abstract Meta-, multi-task, and federated learning can be all viewed as solving similar tasks,
drawn from a distribution that reflects task similarities. We provide a unified view of all these …
drawn from a distribution that reflects task similarities. We provide a unified view of all these …
Online clustering of bandits with misspecified user models
The contextual linear bandit is an important online learning problem where given arm
features, a learning agent selects an arm at each round to maximize the cumulative rewards …
features, a learning agent selects an arm at each round to maximize the cumulative rewards …
Conversational recommendation with online learning and clustering on misspecified users
In the domain of conversational recommendation systems (CRSs), the development of
recommenders capable of eliciting user preferences through conversation has marked a …
recommenders capable of eliciting user preferences through conversation has marked a …
Metadata-based multi-task bandits with bayesian hierarchical models
How to explore efficiently is a central problem in multi-armed bandits. In this paper, we
introduce the metadata-based multi-task bandit problem, where the agent needs to solve a …
introduce the metadata-based multi-task bandit problem, where the agent needs to solve a …
Provably efficient multi-task reinforcement learning with model transfer
We study multi-task reinforcement learning (RL) in tabular episodic Markov decision
processes (MDPs). We formulate a heterogeneous multi-player RL problem, in which a …
processes (MDPs). We formulate a heterogeneous multi-player RL problem, in which a …
A gang of adversarial bandits
We consider running multiple instances of multi-armed bandit (MAB) problems in parallel. A
main motivation for this study are online recommendation systems, in which each of $ N …
main motivation for this study are online recommendation systems, in which each of $ N …
Decentralized randomly distributed multi-agent multi-armed bandit with heterogeneous rewards
We study a decentralized multi-agent multi-armed bandit problem in which multiple clients
are connected by time dependent random graphs provided by an environment. The reward …
are connected by time dependent random graphs provided by an environment. The reward …
Multi-agent learning with heterogeneous linear contextual bandits
As trained intelligent systems become increasingly pervasive, multiagent learning has
emerged as a popular framework for studying complex interactions between autonomous …
emerged as a popular framework for studying complex interactions between autonomous …