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Found 7 papers, 3 papers with code
What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement Learning
In sequential decision-making problems involving sensitive attributes like race and gender, reinforcement learning (RL) agents must carefully consider long-term fairness while maximizing returns.
Prefix-Tuning Based Unsupervised Text Style Transfer
Unsupervised text style transfer aims at training a generative model that can alter the style of the input sentence while preserving its content without using any parallel data.
Causal Reinforcement Learning: A Survey
Causality, however, offers a notable advantage as it can formalize knowledge in a systematic manner and leverage invariance for effective knowledge transfer.
Retrieved Sequence Augmentation for Protein Representation Learning
RSA links query protein sequences to a set of sequences with similar structures or properties in the database and combines these sequences for downstream prediction.
Undersampling and Cumulative Class Re-decision Methods to Improve Detection of Agitation in People with Dementia
Agitation is one of the most prevalent symptoms in people with dementia (PwD) that can place themselves and the caregiver's safety at risk.
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning
We show that such OOD sampling and pessimistic bootstrapping yields provable uncertainty quantifier in linear MDPs, thus providing the theoretical underpinning for PBRL.
False Correlation Reduction for Offline Reinforcement Learning
Offline reinforcement learning (RL) harnesses the power of massive datasets for resolving sequential decision problems.
