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Found 10 papers, 1 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.
Strategic Representation
In our main setting of interest, the system represents attributes of an item to the user, who then decides whether or not to consume.
ADVISER: AI-Driven Vaccination Intervention Optimiser for Increasing Vaccine Uptake in Nigeria
More than 5 million children under five years die from largely preventable or treatable medical conditions every year, with an overwhelmingly large proportion of deaths occurring in under-developed countries with low vaccination uptake.
A Causal Bandit Approach to Learning Good Atomic Interventions in Presence of Unobserved Confounders
First, we propose a simple regret minimization algorithm that takes as input a semi-Markovian causal graph with atomic interventions and possibly unobservable variables, and achieves $\tilde{O}(\sqrt{M/T})$ expected simple regret, where $M$ is dependent on the input CBN and could be very small compared to the number of arms.
Strategic Classification in the Dark
Strategic classification studies the interaction between a classification rule and the strategic agents it governs.
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.
Sparse Linear Networks with a Fixed Butterfly Structure: Theory and Practice
Motivated by these facts, we propose to replace a dense linear layer in any neural network by an architecture based on the butterfly network.
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.
