arXiv:2508. 16531v3 Announce Type: replace-cross Abstract: Many algorithms are designed to work well on average over inputs.
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...
arXiv:2607. 17469v1 Announce Type: cross Abstract: A randomized algorithm may terminate almost surely even though exceptional random tapes make it run forever.
arXiv:2610. 26944v1 Announce Type: cross Abstract: Under the quantifier $\exists A\,\forall D$, where the realizable distribution and its marginal are unknown, we determine the finite-class sample-complexity exponent for strict replicability: two independent samples and one shared seed must produce identical functions.
arXiv:2610. 26944v2 Announce Type: replace Abstract: Strict replicability requires two independent training samples, processed with the same random seed, to produce exactly the same hypothesis.
The paper investigates TCP acknowledgment strategies that aim to minimize both acknowledgment and waiting costs. It establishes that no deterministic online algorithm can achieve a competitive ratio better than 2, and shows that achieving sublinear regret requires at least $k$ randomized online experts.
arXiv:2609.14170v2 Announce Type: replace-cross Abstract: Distributed multiple testing asks $N$ sites, each holding p-values for its own hypotheses, to control the false discovery rate (FDR) of the d...
arXiv:2604. 11026v4 Announce Type: replace-cross Abstract: We establish a stability bound for the Kullback-Leibler (KL) divergence when its Gaussian reference distribution is perturbed.