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Found 7 papers, 1 papers with code
Top Two Algorithms Revisited
Top Two algorithms arose as an adaptation of Thompson sampling to best arm identification in multi-armed bandit models (Russo, 2016), for parametric families of arms.
Attribution-based Explanations that Provide Recourse Cannot be Robust
Finally, we strengthen our impossibility result for the restricted case where users are only able to change a single attribute of $x$, by providing an exact characterization of the functions $f$ to which impossibility applies.
Bandits with many optimal arms
We characterize the optimal learning rates both in the cumulative regret setting, and in the best-arm identification setting in terms of the problem parameters $T$ (the budget), $p^*$ and $\Delta$.
Fixed-Confidence Guarantees for Bayesian Best-Arm Identification
We investigate and provide new insights on the sampling rule called Top-Two Thompson Sampling (TTTS).
Safe-Bayesian Generalized Linear Regression
We study generalized Bayesian inference under misspecification, i. e. when the model is 'wrong but useful'.
Safe Testing
We develop the theory of hypothesis testing based on the e-value, a notion of evidence that, unlike the p-value, allows for effortlessly combining results from several studies in the common scenario where the decision to perform a new study may depend on previous outcomes.
Optional Stopping with Bayes Factors: a categorization and extension of folklore results, with an application to invariant situations
It is often claimed that Bayesian methods, in particular Bayes factor methods for hypothesis testing, can deal with optional stopping.
