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Found 7 papers, 0 papers with code
Best-of-Three-Worlds Linear Bandit Algorithm with Variance-Adaptive Regret Bounds
At the higher level, the proposed algorithm adapts to a variety of types of environments.
Online Task Assignment Problems with Reusable Resources
The key features of our problem are (1) an agent is reusable, i. e., an agent comes back to the market after completing the assigned task, (2) an agent may reject the assigned task to stay the market, and (3) a task may accommodate multiple agents.
Near-Optimal Regret Bounds for Contextual Combinatorial Semi-Bandits with Linear Payoff Functions
However, there is a gap of $\tilde{O}(\max(\sqrt{d}, \sqrt{k}))$ between the current best upper and lower bounds, where $d$ is the dimension of the feature vectors, $k$ is the number of the chosen arms in a round, and $\tilde{O}(\cdot)$ ignores the logarithmic factors.
Delay and Cooperation in Nonstochastic Linear Bandits
This paper offers a nearly optimal algorithm for online linear optimization with delayed bandit feedback.
Improved Regret Bounds for Bandit Combinatorial Optimization
\textit{Bandit combinatorial optimization} is a bandit framework in which a player chooses an action within a given finite set $\mathcal{A} \subseteq \{ 0, 1 \}^d$ and incurs a loss that is the inner product of the chosen action and an unobservable loss vector in $\mathbb{R} ^ d$ in each round.
Oracle-Efficient Algorithms for Online Linear Optimization with Bandit Feedback
Our algorithm for non-stochastic settings has an oracle complexity of $\tilde{O}( T )$ and is the first algorithm that achieves both a regret bound of $\tilde{O}( \sqrt{T} )$ and an oracle complexity of $\tilde{O} ( \mathrm{poly} ( T ) )$, given only linear optimization oracles.
An Arm-Wise Randomization Approach to Combinatorial Linear Semi-Bandits
Our empirical evaluation with artificial and real-world datasets demonstrates that the proposed algorithms with the arm-wise randomization technique outperform the existing algorithms without this technique, especially for the clustered case.
