arXiv:2607. 09909v1 Announce Type: cross Abstract: We study nearest neighbor search from the perspective of data-driven algorithm design: given a dataset $P \subset \mathbb{R}^d$ of size $n$ and sample access to a query distribution over $\mathbb{R}^d$, the goal is to learn a data structure optimized for queries drawn from that specific distribution.
By Sanjeev Khanna, Ashwin Padaki, Erik Waingarten
arXiv:2608. 15472v1 Announce Type: cross Abstract: The problem of networked information aggregation, studied in Kearns et al.
By Ambar Pal
arXiv:2608. 06337v1 Announce Type: cross Abstract: A monotone adversary observes an i.
By Anay Mehrotra
arXiv:2608. 03294v1 Announce Type: new Abstract: We study the problem of learning multi-head softmax attention from black-box input-output access.
By Sunyeop Kim, Insung Kim, Jian Guo
arXiv:2606. 11149v1 Announce Type: new Abstract: We study the problem of learning a drifting concept in the presence of Massart noise.
By Mingchen Ma, Guyang Cao, Jelena Diakonikolas, Ilias Diakonikolas
arXiv:2607. 22889v1 Announce Type: new Abstract: Learning the natural parameters $z \in \mathbb{R}^n$ of discrete distributions $\mu_z$ from independent samples constrained to a subset $S \subseteq \{0,1\}^n$ is a foundational challenge in high-dimensional statistics.
By Rohan Chauhan, Ioannis Panageas