Abstract
We introduce p-adic manifold learning, propose an algorithm to solve it, and propose benchmark tasks from impartial games.
p-adic Manifold Learning and Benchmark Tasks from Impartial Games
Related papers
Ranked by semantic similarity — how closely each paper's abstract matches this one (100% = near-identical topic).
- The game theoretic p-Laplacian and semi-supervised learning with few labels (2017)54% match
- PAC-Bayesian Lifelong Learning For Multi-Armed Bandits (2022)53% match
- Learning to sample fibers for goodness-of-fit testing (2024)53% match
- Privately Learning Decision Lists and a Differentially Private Winnow (2026)52% match
- PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting (2026)52% match
- Learning Pseudo-Backdoors for Mixed Integer Programs (2021)52% match
- Internal State-Based Policy Gradient Methods for Partially Observable Markov Potential Games (2026)52% match
- Aspiration-based Perturbed Learning Automata in Games with Noisy Utility Measurements. Part A: Stochastic Stability in Non-zero-Sum Games (2025)51% match