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Found 11 papers, 0 papers with code
New Classes of the Greedy-Applicable Arm Feature Distributions in the Sparse Linear Bandit Problem
Firstly, we show that a mixture distribution that has a greedy-applicable component is also greedy-applicable.
Reforming an Envy-Free Matching
We consider the problem of reforming an envy-free matching when each agent is assigned a single item.
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.
Approximability of Monotone Submodular Function Maximization under Cardinality and Matroid Constraints in the Streaming Model
The latter one almost matches our lower bound of $\frac{K}{2K-1}$ for a matroid constraint, which almost settles the approximation ratio for a matroid constraint that can be obtained by a streaming algorithm whose space complexity is independent of $n$.
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.
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.
Regret Bounds for Online Portfolio Selection with a Cardinality Constraint
Online portfolio selection is a sequential decision-making problem in which a learner repetitively selects a portfolio over a set of assets, aiming to maximize long-term return.
Causal Bandits with Propagating Inference
In this setting, the arms are identified with interventions on a given causal graph, and the effect of an intervention propagates throughout all over the causal graph.
Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression with Limited Observation
Under these assumptions, we present polynomial-time sublinear-regret algorithms for the online sparse linear regression.
