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.
By Tung Tran, Viet Bao Mai, Hoang Ta, Tuan Dam
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.
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:2607. 05359v1 Announce Type: new Abstract: Planning under uncertainty in continuous domains is essential for autonomous systems, yet computationally demanding.
By Idan Lev-Yehudi, Vadim Indelman
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.
By Krzysztof Choromanski, Avinava Dubey, Arijit Sehanobish, Isaac Reid
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.
By Hoyun Choi, Junghyo Jo, Deok-Sun Lee
arXiv:2503. 14549v3 Announce Type: replace-cross Abstract: How can a cheap but biased sequential, finite-horizon sampler over a discrete space be corrected so that its terminal output follows a prescribed Gibbs distribution?
By Michael Chertkov, Sungsoo Ahn, Hamidreza Behjoo
The paper introduces Pref‑MH, an exact Metropolis‑Hastings sampler that uses only stochastic binary pairwise comparisons from judges to sample from distributions conditioned on desired semantic properties. By linking the MH density ratio to the preference odds of the Bradley‑Terry model, the authors devise an accept/reject rule that guarantees convergence to the target distribution. Experiments on text, molecular, and image generation with large‑language‑model and vision‑language‑model judges show Pref‑MH as a practical, flexible method for conditional sampling when comparative feedback is readily available.
By Ariel Smogorghevski, Nir Rosenfeld, Yaniv Romano
arXiv:2609.08172v1 Announce Type: cross
Abstract: Slice sampling is a Markov chain Monte Carlo algorithm that draws its next state uniformly from a "slice"---a super-level set of the target density f...
By Trevor Campbell
RheoSampling tackles the one‑hot dilemma in stochastic dynamic‑tree speculative decoding by decoupling token sampling from tree construction and verification. It injects a sampled token into deterministic top‑K slots, assigning distinct proxy probabilities for expansion and pruning while preserving the true sampling probability for verification, thereby achieving lossless, context‑aware top‑K construction with stochastic sampling. Experiments on various LLMs show higher acceptance rates and speedups compared to existing dynamic‑tree methods.
By Qiao Hu, Yepeng Weng, Bo Zhang, Takehisa Yairi
arXiv:2609.06489v1 Announce Type: cross
Abstract: Monte Carlo Tree Search (MCTS) has demonstrated success in online planning for deterministic environments, yet significant challenges remain in adapt...
By Tuan Dam
arXiv:2607. 00586v1 Announce Type: cross Abstract: We present a simple, yet general approach to study the scaling properties as the dimensionality of Metropolised MCMC sampling algorithms increases.
By P. Dobson, J. M. Sanz-Serna, K. C. Zygalakis