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Found 8 papers, 2 papers with code
Near-Optimal Pure Exploration in Matrix Games: A Generalization of Stochastic Bandits & Dueling Bandits
We design a near-optimal algorithm whose sample complexity matches the lower bound, up to log factors.
Logarithmic Regret for Matrix Games against an Adversary with Noisy Bandit Feedback
If the row player uses the EXP3 strategy, an algorithm known for obtaining $\sqrt{T}$ regret against an arbitrary sequence of rewards, it is immediate that the row player also achieves $\sqrt{T}$ regret relative to the Nash equilibrium in this game setting.
Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games
We study the sample complexity of identifying an approximate equilibrium for two-player zero-sum $n\times 2$ matrix games.
Fairness and Welfare Quantification for Regret in Multi-Armed Bandits
Since NSW is known to satisfy fairness axioms, our approach complements the utilitarian considerations of average (cumulative) regret, wherein the algorithm is evaluated via the arithmetic mean of its expected rewards.
Multi-Armed Bandits with Bounded Arm-Memory: Near-Optimal Guarantees for Best-Arm Identification and Regret Minimization
In this setting, the arms arrive in a stream, and the number of arms that can be stored in the memory at any time, is bounded.
Streaming Algorithms for Stochastic Multi-armed Bandits
In this setting, the arms arrive in a stream, and the number of arms that can be stored in the memory at any time, is bounded.
On Parameterized Complexity of Binary Networked Public Goods Game
In the Binary Networked Public Goods game, every player needs to decide if she participates in a public project whose utility is shared equally by the community.
Computer Science and Game Theory Computational Complexity Data Structures and Algorithms 68Q27
Query Complexity of Tournament Solutions
We, in this paper, precisely study this problem for commonly used tournament solutions: given an oracle access to the edges of a tournament T, find $f(T)$ by querying as few edges as possible, for a tournament solution f. We first show that the set of Condorcet non-losers in a tournament can be found by querying $2n-\lfloor \log n \rfloor -2$ edges only and this is tight in the sense that every algorithm for finding the set of Condorcet non-losers needs to query at least $2n-\lfloor \log n \rfloor -2$ edges in the worst case, where $n$ is the number of vertices in the input tournament.
