arXiv Machine Learning
The Marked Edge Walk: A Novel MCMC Algorithm for Sampling of Graph Partitions
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arXiv Machine Learning
Sep 18
Graph-Based Stochastic Power-UCT: Monte-Carlo Graph Search with Power Mean Estimation
Graph-Based Stochastic-Power-UCT (GS-Power-UCT) is a Monte‑Carlo graph search algorithm that shares states reached at the same planning depth while keeping separate values for different depths, thereby reducing duplicate simulations in stochastic MDPs. The method guarantees that, for a fixed horizon, the root estimate converges to the finite‑horizon value at an $O(n^{-1/2})$ rate, matching tree‑based Stochastic‑Power‑UCT but with improved sample reuse. Two full‑state variants—GS‑Power‑UCT‑F and GS‑Power‑UCT‑F$^+$—extend the approach to single‑node per physical state and adaptive horizons, respectively, with the latter converging to the optimal infinite‑horizon discounted value when cross‑depth bias vanishes. Experiments on stochastic planning benchmarks demonstrate that GS‑Power‑UCT outperforms both tree‑based and other graph‑based baselines in sample efficiency.
Hugging Face Trending Papers
Sep 17
Graph-Based Stochastic Power-UCT: Monte-Carlo Graph Search with Power Mean Estimation
The paper introduces Graph-Based Stochastic-Power-UCT (GS-Power-UCT), a Monte‑Carlo graph search algorithm that shares states reached at the same planning depth while maintaining separate values for different depths. It achieves an $O(n^{-1/2})$ convergence rate for the root estimate in finite‑horizon stochastic MDPs, matching tree‑based methods but with better sample reuse. Two full‑state variants, GS-Power-UCT‑F and GS-Power-UCT‑F$^+$, further explore sample sharing and bias control, with GS-Power-UCT‑F$^+$ converging to the optimal infinite‑horizon discounted value when the cross‑depth gap vanishes. Experiments on stochastic planning benchmarks demonstrate improved sample efficiency over existing tree‑based and graph‑based baselines.
Hugging Face Trending Papers
Jul 6
Graph Sparse Sampling: Breaking the Curse of the Horizon in Continuous MDP Planning
Planning under uncertainty in continuous domains is essential for autonomous systems, yet computationally demanding. Tree-based search methods such as Monte Carlo Tree Search (MCTS) remain popular, but their branching structure can require sampling budgets that grow exponentially with lookahead depth in the worst case.
arXiv AI
Jul 7
Graph Sparse Sampling: Breaking the Curse of the Horizon in Continuous MDP Planning
arXiv:2607. 05359v1 Announce Type: new Abstract: Planning under uncertainty in continuous domains is essential for autonomous systems, yet computationally demanding.
arXiv Machine Learning
Jun 26
Computationally-efficient Graph Modeling with Refined Graph Random Features
arXiv:2510. 07716v2 Announce Type: replace Abstract: We propose refined GRFs (GRFs++), a new class of Graph Random Features (GRFs) for efficient and accurate computations involving kernels defined on the nodes of a graph.
arXiv AI
Sep 18
Reinforcement Learning for Graph Generation under a Hard Assortativity Constraint
The paper presents a reinforcement learning approach to generate graph ensembles that satisfy a hard assortativity constraint, a measure of degree–degree correlation between adjacent nodes. Unlike traditional soft-constraint methods, the learned policy performs degree-preserving rewiring to meet the exact target, reducing generation cost by at least an order of magnitude while preserving over 98% of configurational diversity. Trained on small graphs, the method generalizes to larger sizes and different topologies, allowing precise control over secondary observables such as the clustering coefficient.