arXiv AI

Optimizing Explicit Unit-Distance Lower-Bound Certificates

arXiv:2606. 03419v1 Announce Type: cross Abstract: The 2026 disproof of Erd\H{o}s's unit-distance conjecture and Sawin's subsequent explicit quantitative refinement show that the maximum number $u(n)$ of unit distances among $n$ planar points can exceed $n^{1+\varepsilon}$ for a fixed positive $\varepsilon$.

arXiv Machine Learning
Aug 10

Sub-Quadratic Bisimulation Metrics via Approximate Nearest Neighbors: Coverage-Augmented Guarantees and Computable Two-Sided Certificates

arXiv:2608. 06762v1 Announce Type: new Abstract: Bisimulation metrics quantify behavioral similarity in Markov decision processes, but their Wasserstein fixed-point operator updates every state pair and incurs quadratic pairwise work.

By Ibne Farabi Shihab, Joyanta Jyoti Mondal
arXiv Machine Learning
Jun 18

How fast can you find a good hypothesis?

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 $\mathcal{X}$.

By Anders Aamand, Maryam Aliakbarpour, Justin Y. Chen, Sandeep Silwal
arXiv Machine Learning
Jul 20

Testing Distributions Against Bounded Distinguishers

arXiv:2607. 15645v1 Announce Type: cross Abstract: Motivated by the challenge of testing distributions over high-dimensional or continuous domains, we study distribution testing with respect to bounded classes of distinguishers.

By Mark Bun, Rathin Desai, Renato Ferreira Pinto Jr
arXiv Machine Learning
Aug 12

Optimistic Rates for Multiclass PAC Learning

arXiv:2608. 10869v1 Announce Type: new Abstract: Worst-case multiclass bounds do not become smaller when the best classifier is already nearly correct: what is missing is an optimistic rate, a guarantee whose fluctuation scales with the oracle risk itself.

By Xiaoyu Li, Andi Han, Jiaojiao Jiang, Junbin Gao
arXiv AI
1d ago

A Bi-directional Multi-solution Scalable Grover Search Algorithm

arXiv:2404. 15616v2 Announce Type: replace-cross Abstract: Grover's search algorithms, including various Partial Grover Searches (PGS), suffer from scaling issues when multiple solutions are sought, as the number of iterations scales with the number of solutions or marked states, making implementation more computationally expensive.

By Debanjan Konar, Zain Hafeez, Vaneet Aggarwal