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Found 8 papers, 2 papers with code
Trading-off price for data quality to achieve fair online allocation
We consider the problem of online allocation subject to a long-term fairness penalty.
Decentralized model-free reinforcement learning in stochastic games with average-reward objective
We propose the first model-free algorithm that achieves low regret performance for decentralized learning in two-player zero-sum tabular stochastic games with infinite-horizon average-reward objective.
On Fair Selection in the Presence of Implicit and Differential Variance
In the second setting (with known variances), imposing the $\gamma$-rule decreases the utility but we prove a bound on the utility loss due to the fairness mechanism.
Reinforcement Learning for Markovian Bandits: Is Posterior Sampling more Scalable than Optimism?
While the regret bound and runtime of vanilla implementations of PSRL and UCRL2 are exponential in the number of bandits, we show that the episodic regret of MB-PSRL and MB-UCRL2 is $\tilde{O}(S\sqrt{nK})$ where $K$ is the number of episodes, $n$ is the number of bandits and $S$ is the number of states of each bandit (the exact bound in S, n and K is given in the paper).
Exponential Convergence Rate for the Asymptotic Optimality of Whittle Index Policy
In this paper we show that, under the same conditions, the convergence rate is exponential in the number of bandits, unless the fixed point is singular (to be defined later).
Performance Optimization and Control Probability
On Fair Selection in the Presence of Implicit Variance
We then compare the utility obtained by imposing a fairness mechanism that we term $\gamma$-rule (it includes demographic parity and the four-fifths rule as special cases), to that of a group-oblivious selection algorithm that picks the candidates with the highest estimated quality independently of their group.
The Price of Local Fairness in Multistage Selection
In particular, our experiments show that the price of local fairness is generally smaller when the sensitive attribute is observed at the first stage; but globally fair selections are more locally fair when the sensitive attribute is observed at the second stage---hence in both cases it is often possible to have a selection that has a small price of local fairness and is close to locally fair.
Linear Regression from Strategic Data Sources
In this paper, we study a setting in which features are public but individuals choose the precision of the outputs they reveal to an analyst.
