arXiv:2508. 16531v3 Announce Type: replace-cross Abstract: Many algorithms are designed to work well on average over inputs.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2509. 03734v3 Announce Type: replace-cross Abstract: In the hypothesis selection problem, we are given sample and query access to finite set of candidate distributions (hypotheses), $\mathcal{H} = \{H_1, \ldots, H_n\}$, and samples from an unknown distribution $P$, both over a domain $\mathcal{X}$.
arXiv:2608. 04686v1 Announce Type: new Abstract: We study distributionally robust PAC learning for the $0$--$1$-loss, where adversarial perturbations of the data distribution are constrained by a Cressie--Read divergence of order $k>1$ and radius $\rho\geq 0$.
We study the problem of \emph{adversarially robust} PAC learning. In this framework, the learner observes independent samples from an unknown distribution over $\mathcal{X} \times \{0,1\}$, as in clas...
The paper introduces the first algorithms for replicable dueling bandits, ensuring that two runs with shared internal randomness and independent comparison outcomes select the same ordered pair in every round with high probability. Under relaxed stochastic transitivity, the authors achieve expected regret ×O(γ^8 K^4 H_p/ρ^2), while under strong stochastic transitivity they improve this to ×O(K^2 H_p/ρ^2). For linear dueling bandits, a regret bound of ×O(d^3√T/(c_μρ)) is presented, and experiments confirm both replicability and sublinear regret.
arXiv:2609.24260v1 Announce Type: cross Abstract: We study the problem of \emph{adversarially robust} PAC learning. In this framework, the learner observes independent samples from an unknown distrib...
arXiv:2608.29308v1 Announce Type: cross Abstract: In metric social choice, each voter ranks a set of $m$ candidates by her distance to them in an unknown metric space. The cost of a candidate is its...