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Found 5 papers, 2 papers with code
Proxy-informed Bayesian transfer learning with unknown sources
PROMPT uses the same proxy information for two purposes: (i) estimation of effects specific to the target task, and (ii) construction of a robust reweighting of the source data for estimation of effects that transfer between tasks.
One system for learning and remembering episodes and rules
Humans can learn individual episodes and generalizable rules and also successfully retain both kinds of acquired knowledge over time.
Bayesian Active Learning in the Presence of Nuisance Parameters
However, the introduction of nuisance parameters can lead to bias in the Bayesian learner's estimate of the target parameters, a phenomenon we refer to as negative interference.
Learning Relevant Contextual Variables Within Bayesian Optimization
Contextual Bayesian Optimization (CBO) efficiently optimizes black-box functions with respect to design variables, while simultaneously integrating contextual information regarding the environment, such as experimental conditions.
Characterizing the robustness of Bayesian adaptive experimental designs to active learning bias
We show that active learning bias can also afflict Bayesian adaptive experimental design, depending on model misspecification.
