TDS uses per-tree prediction trajectories to derive instance difficulty scores that rank errors better than prior hardness measures and improve active learning, selective prediction, and Mondrian conformal prediction on tabular data.
Relative location of CT slices on axial axis
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DP-GIBO is a differentially private local Bayesian optimization algorithm that converges to locally optimal hyperparameters with polynomial (rather than exponential) dependence on dimension under suitable conditions.
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Trajectory-Based Difficulty Scoring for Reliable Learning on Tabular Data
TDS uses per-tree prediction trajectories to derive instance difficulty scores that rank errors better than prior hardness measures and improve active learning, selective prediction, and Mondrian conformal prediction on tabular data.
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Differentially Private Hyperparameter Tuning using Local Bayesian Optimization
DP-GIBO is a differentially private local Bayesian optimization algorithm that converges to locally optimal hyperparameters with polynomial (rather than exponential) dependence on dimension under suitable conditions.