ac-bayesian
Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and Learning
In online advertising, for instance, the user's profile is the context, the
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Learning via Wasserstein-Based High Probability Generalisation Bounds
The authors contributed equally to this work 37th Conference on Neural Information Processing Systems (NeurIPS 2023). Developing upper bounds on the generalisation gap, i.e., generalisation bounds has been a longstanding topic in statistical learning.
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A Derivation details under Dirichlet assumptions
We finally report the detailed comparison on real benchmarks in Figure 14 and Tables 1 and 2. 21 Figure 13: Comparison voter strength (1st column), training error (2nd column), test error (3rd