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Found 18 papers, 3 papers with code
Investigating Gender Fairness in Machine Learning-driven Personalized Care for Chronic Pain
In this article, we study gender fairness in personalized pain care recommendations using a real-world application of reinforcement learning (Piette et al., 2022a).
Provably Efficient Exploration in Constrained Reinforcement Learning:Posterior Sampling Is All You Need
We present a new algorithm based on posterior sampling for learning in constrained Markov decision processes (CMDP) in the infinite-horizon undiscounted setting.
Multi-Armed Bandits with Generalized Temporally-Partitioned Rewards
In some real-world applications, feedback about a decision is delayed and may arrive via partial rewards that are observed with different delays.
Curiosity-driven Exploration in Sparse-reward Multi-agent Reinforcement Learning
Sparsity of rewards while applying a deep reinforcement learning method negatively affects its sample-efficiency.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Local Differential Privacy for Sequential Decision Making in a Changing Environment
We study the problem of preserving privacy while still providing high utility in sequential decision making scenarios in a changing environment.
Generalizing distribution of partial rewards for multi-armed bandits with temporally-partitioned rewards
In this paper, we introduce a general formulation of how an arm's cumulative reward is distributed across several rounds, called Beta-spread property.
An Empirical Evaluation of Posterior Sampling for Constrained Reinforcement Learning
We study a posterior sampling approach to efficient exploration in constrained reinforcement learning.
Survey on Fair Reinforcement Learning: Theory and Practice
In this article, we provide an extensive overview of fairness approaches that have been implemented via a reinforcement learning (RL) framework.
The Impact of Batch Learning in Stochastic Linear Bandits
Our main theoretical results show that the impact of batch learning is a multiplicative factor of batch size relative to the regret of online behavior.
The Impact of Batch Learning in Stochastic Bandits
We consider a special case of bandit problems, namely batched bandits.
Autonomous exploration for navigating in non-stationary CMPs
We consider a setting in which the objective is to learn to navigate in a controlled Markov process (CMP) where transition probabilities may abruptly change.
Variational Regret Bounds for Reinforcement Learning
This is the first variational regret bound for the general reinforcement learning setting.
A Sliding-Window Algorithm for Markov Decision Processes with Arbitrarily Changing Rewards and Transitions
We consider reinforcement learning in changing Markov Decision Processes where both the state-transition probabilities and the reward functions may vary over time.
Counterfactual Learning for Machine Translation: Degeneracies and Solutions
Counterfactual learning is a natural scenario to improve web-based machine translation services by offline learning from feedback logged during user interactions.
On Formalizing Fairness in Prediction with Machine Learning
Machine learning algorithms for prediction are increasingly being used in critical decisions affecting human lives.
Corrupt Bandits for Preserving Local Privacy
In this framework, motivated by privacy preservation in online recommender systems, the goal is to maximize the sum of the (unobserved) rewards, based on the observation of transformation of these rewards through a stochastic corruption process with known parameters.
A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits
We study the K-armed dueling bandit problem which is a variation of the classical Multi-Armed Bandit (MAB) problem in which the learner receives only relative feedback about the selected pairs of arms.
Utility-based Dueling Bandits as a Partial Monitoring Game
Partial monitoring is a generic framework for sequential decision-making with incomplete feedback.
