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Found 6 papers, 0 papers with code
Robustness of Bayesian Pool-based Active Learning Against Prior Misspecification
This suggests we should use a Lipschitz utility for AL if robustness is required.
Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint
We study the worst-case adaptive optimization problem with budget constraint that is useful for modeling various practical applications in artificial intelligence and machine learning.
Learning From Non-iid Data: Fast Rates for the One-vs-All Multiclass Plug-in Classifiers
We prove new fast learning rates for the one-vs-all multiclass plug-in classifiers trained either from exponentially strongly mixing data or from data generated by a converging drifting distribution.
Generalization and Robustness of Batched Weighted Average Algorithm with V-geometrically Ergodic Markov Data
For the generalization of the algorithm, we prove a PAC-style bound on the training sample size for the expected $L_1$-loss to converge to the optimal loss when training data are V-geometrically ergodic Markov chains.
Active Learning for Probabilistic Hypotheses Using the Maximum Gibbs Error Criterion
We introduce a new objective function for pool-based Bayesian active learning with probabilistic hypotheses.
