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Found 7 papers, 0 papers with code
Mitigating Disparity while Maximizing Reward: Tight Anytime Guarantee for Improving Bandits
We study the tension that arises between two seemingly conflicting objectives in the horizon-unaware setting: a) maximizing the cumulative reward at any time based on current rewards of the arms, and b) ensuring that arms with better long-term rewards get sufficient opportunities even if they initially have low 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.
Long-Term Resource Allocation Fairness in Average Markov Decision Process (AMDP) Environment
Fairness has emerged as an important concern in automated decision-making in recent years, especially when these decisions affect human welfare.
Budgeted and Non-budgeted Causal Bandits
If there are no backdoor paths from an intervenable node to the reward node then we propose an algorithm to minimize simple regret that optimally trades-off observations and interventions based on the cost of intervention.
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.
Achieving Fairness in the Stochastic Multi-armed Bandit Problem
Finally, we evaluate the cost of fairness in terms of the conventional notion of regret.
Achieving Fairness in Stochastic Multi-armed Bandit Problem
Finally, we evaluate the cost of fairness in terms of the conventional notion of regret.
