arXiv:2602.07530v2 Announce Type: replace
Abstract: Conformal prediction provides rigorous, distribution-free uncertainty guarantees, but often yields prohibitively large prediction sets in structured domains such as routing, planning, or sequential r…
arXiv:2603.26852v1 Announce Type: cross
Abstract: We propose novel algorithms for sequence prediction based on ideas from stringology. These algorithms are time and space efficient and satisfy mistake bounds related to particular stringological comple…
arXiv:2603.26893v1 Announce Type: cross
Abstract: Allocation of dynamically-arriving (i.e., online) divisible resources among a set of offline agents is a fundamental problem, with applications to online marketplaces, scheduling, portfolio selection, …
arXiv:2603.04689v2 Announce Type: replace-cross
Abstract: Fair top-$k$ selection, which ensures appropriate proportional representation of members from minority or historically disadvantaged groups among the top-$k$ selected candidates, has drawn sign…
arXiv:2603.25622v1 Announce Type: cross
Abstract: We present an efficient algorithm for uniformly sampling from an arbitrary compact body $\mathcal{X} \subset \mathbb{R}^n$ from a warm start under isoperimetry and a natural volume growth condition. Ou…
arXiv:2603.18582v2 Announce Type: replace-cross
Abstract: DRESS is a deterministic, parameter-free framework for structural graph refinement that iteratively refines the structural similarity of edges in a graph to produce a canonical fingerprint: a r…
arXiv:2502.02861v4 Announce Type: replace-cross
Abstract: The field of algorithms with predictions incorporates machine learning advice in the design of online algorithms to improve real-world performance. A central consideration is the extent to whic…
arXiv:2603.24545v1 Announce Type: cross
Abstract: We study the problem of detecting local geometry in random graphs. We introduce a model $\mathcal{G}(n, p, d, k)$, where a hidden community of average size $k$ has edges drawn as a random geometric gra…