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Found 4 papers, 1 papers with code
An Improved Algorithm for Learning Drifting Discrete Distributions
To use more samples, we must resort to samples further in the past, and we incur a drift error due to the bias introduced by the change in distribution.
An Adaptive Method for Weak Supervision with Drifting Data
In particular, at each step, our algorithm guarantees an estimation of the current accuracies of the weak supervision sources over a window of past observations that minimizes a trade-off between the error due to the variance of the estimation and the error due to the drift.
Nonparametric Density Estimation under Distribution Drift
We study nonparametric density estimation in non-stationary drift settings.
Tight Lower Bounds on Worst-Case Guarantees for Zero-Shot Learning with Attributes
We develop the first non-trivial lower bound on the worst-case error of the best map from attributes to classes for this setting, even with perfect attribute detectors.
