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Found 7 papers, 2 papers with code
Multi-objective Asynchronous Successive Halving
Hyperparameter optimization (HPO) is increasingly used to automatically tune the predictive performance (e. g., accuracy) of machine learning models.
Assessing the Performance of Online Students -- New Data, New Approaches, Improved Accuracy
This student performance (SP) modeling problem is a critical step for building adaptive online teaching systems.
Fair Bayesian Optimization
Moreover, our method can be used in synergy with such specialized fairness techniques to tune their hyperparameters.
Bandit Linear Optimization for Sequential Decision Making and Extensive-Form Games
Tree-form sequential decision making (TFSDM) extends classical one-shot decision making by modeling tree-form interactions between an agent and a potentially adversarial environment.
Transferable Student Performance Modeling for Intelligent Tutoring Systems
(ii) In the inductive transfer setting, we tune pre-trained course-agnostic performance models to new courses using small-scale target course data (e. g., collected during a pilot study).
NEOLAF, an LLM-powered neural-symbolic cognitive architecture
This paper presents the Never Ending Open Learning Adaptive Framework (NEOLAF), an integrated neural-symbolic cognitive architecture that models and constructs intelligent agents.
Ruffle&Riley: Towards the Automated Induction of Conversational Tutoring Systems
Conversational tutoring systems (CTSs) offer learning experiences driven by natural language interaction.
