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Found 6 papers, 3 papers with code
Personalized State Anxiety Detection: An Empirical Study with Linguistic Biomarkers and A Machine Learning Pipeline
To address this concern, in Study 1, we collected linguistic data from N=35 high socially anxious participants in a variety of social contexts, finding that digital linguistic biomarkers significantly differ between evaluative vs. non-evaluative social contexts and between individuals having different trait psychological symptoms, suggesting the likely importance of personalized approaches to detect state anxiety.
Infinite Action Contextual Bandits with Reusable Data Exhaust
For infinite action contextual bandits, smoothed regret and reduction to regression results in state-of-the-art online performance with computational cost independent of the action set: unfortunately, the resulting data exhaust does not have well-defined importance-weights.
Personalized Reward Learning with Interaction-Grounded Learning (IGL)
In an era of countless content offerings, recommender systems alleviate information overload by providing users with personalized content suggestions.
Eigen Memory Trees
This work introduces the Eigen Memory Tree (EMT), a novel online memory model for sequential learning scenarios.
Inverse Reinforcement Learning for Strategy Identification
In this paper, the recovered reward are visually displayed, clustered using unsupervised learning, and classified using a supervised learner.
A Framework for Addressing the Risks and Opportunities In AI-Supported Virtual Health Coaches
Virtual coaching has rapidly evolved into a foundational component of modern clinical practice.
