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Found 8 papers, 1 papers with code
Group Meritocratic Fairness in Linear Contextual Bandits
This is a very strong notion of fairness, since the relative rank is not directly observed by the agent and depends on the underlying reward model and on the distribution of rewards.
Meta Representation Learning with Contextual Linear Bandits
We aim to leverage this information in order to learn a new downstream bandit task, which shares the same representation.
Multi-task Representation Learning with Stochastic Linear Bandits
We study the problem of transfer-learning in the setting of stochastic linear bandit tasks.
Online Model Selection: a Rested Bandit Formulation
Unlike known model selection efforts in the recent bandit literature, our algorithm exploits the specific structure of the problem to learn the unknown parameters of the expected loss function so as to identify the best arm as quickly as possible.
Meta-learning with Stochastic Linear Bandits
The goal is to select a learning algorithm which works well on average over a class of bandits tasks, that are sampled from a task-distribution.
Validity, consonant plausibility measures, and conformal prediction
The standard notion of validity, what we refer to here as Type-1 validity, focuses on coverage probability of prediction regions, while a notion of validity relevant to the other prediction-related tasks performed by predictive distributions is lacking.
Stochastic Bandits with Delay-Dependent Payoffs
Motivated by recommendation problems in music streaming platforms, we propose a nonstationary stochastic bandit model in which the expected reward of an arm depends on the number of rounds that have passed since the arm was last pulled.
Efficient Linear Bandits through Matrix Sketching
More precisely, we show that a sketch of size $m$ allows a $\mathcal{O}(md)$ update time for both algorithms, as opposed to $\Omega(d^2)$ required by their non-sketched versions in general (where $d$ is the dimension of context vectors).
