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Ilsang Ohn

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Preprint Aug 2026

Fast rates in Bayesian online learning with approximate posteriors

Exact Bayes prediction enjoys fast predictive regret guarantees, but exact posterior updating or representation may be too costly for online use. We study when these statistical guarantees are preserved by computational approximations. We show that the cumulative price of posterior approximation can be governed by the...

Ilsang Ohn · 0 citations
Jul 2026

Adaptive Bayesian Online Learning via Expert Aggregation

It is proved that the resulting aggregate competes with the best expert in hindsight at an aggregation cost determined by how each expert's per-round performance is evaluated, and shows that the aggregate tracks strong experts without oracle expert selection.

Jungbin Jun, Ilsang Ohn · 1 citation

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