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Found 4 papers, 2 papers with code
Probabilistic Actor-Critic: Learning to Explore with PAC-Bayes Uncertainty
We introduce Probabilistic Actor-Critic (PAC), a novel reinforcement learning algorithm with improved continuous control performance thanks to its ability to mitigate the exploration-exploitation trade-off.
If there is no underfitting, there is no Cold Posterior Effect
The cold posterior effect (CPE) (Wenzel et al., 2020) in Bayesian deep learning shows that, for posteriors with a temperature $T<1$, the resulting posterior predictive could have better performances than the Bayesian posterior ($T=1$).
Split-kl and PAC-Bayes-split-kl Inequalities for Ternary Random Variables
We present a new concentration of measure inequality for sums of independent bounded random variables, which we name a split-kl inequality.
Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote
The bound is based on a novel parametric form of the Chebyshev- Cantelli inequality (a. k. a.
