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Found 14 papers, 4 papers with code
Sequential Decision Problems with Weak Feedback
This thesis considers sequential decision problems, where the loss/reward incurred by selecting an action may not be inferred from observed feedback.
Synopsis: Sequential Decision Problems with Weak Feedback
This thesis considers sequential decision problems, where the loss/reward incurred by selecting an action may not be inferred from observed feedback.
Bayesian Optimization under Stochastic Delayed Feedback
The existing BO methods assume that the function evaluation (feedback) is available to the learner immediately or after a fixed delay.
Federated Neural Bandits
To better exploit the federated setting, FN-UCB adopts a weighted combination of two UCBs: $\text{UCB}^{a}$ allows every agent to additionally use the observations from the other agents to accelerate exploration (without sharing raw observations), while $\text{UCB}^{b}$ uses an NN with aggregated parameters for reward prediction in a similar way to federated averaging for supervised learning.
Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction Models
Finally, ML algorithms are used to develop models that predict the network-wide impacts of disruptive events using the cluster-level features.
Stochastic Multi-Armed Bandits with Control Variates
This paper studies a new variant of the stochastic multi-armed bandits problem where auxiliary information about the arm rewards is available in the form of control variates.
Censored Semi-Bandits for Resource Allocation
The loss depends on two hidden parameters, one specific to the arm but independent of the resource allocation, and the other depends on the allocated resource.
Online Algorithm for Unsupervised Sequential Selection with Contextual Information
In this paper, we study Contextual Unsupervised Sequential Selection (USS), a new variant of the stochastic contextual bandits problem where the loss of an arm cannot be inferred from the observed feedback.
Thompson Sampling for Unsupervised Sequential Selection
The total loss is the sum of the cost incurred for selecting the arm and the stochastic loss associated with the selected arm.
Stochastic Network Utility Maximization with Unknown Utilities: Multi-Armed Bandits Approach
We model this problem setting as a bandit setting where feedback obtained in each round depends on the resource allocated to the agents.
Learning and Fairness in Energy Harvesting: A Maximin Multi-Armed Bandits Approach
It is thus important to learn the least amount of energy harvested by nodes so that the source can transmit on a frequency band that maximizes this amount.
Unsupervised Online Feature Selection for Cost-Sensitive Medical Diagnosis
In medical diagnosis, physicians predict the state of a patient by checking measurements (features) obtained from a sequence of tests, e. g., blood test, urine test, followed by invasive tests.
Censored Semi-Bandits: A Framework for Resource Allocation with Censored Feedback
We study this novel setting by establishing its `equivalence' to Multiple-Play Multi-Armed Bandits(MP-MAB) and Combinatorial Semi-Bandits.
Online Algorithm for Unsupervised Sensor Selection
We set up the USS problem as a stochastic partial monitoring problem and develop an algorithm with sub-linear regret under the WD property.
