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New MASEM method improves sampling from disconnected distributions

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method called Manifold Sampling via Entropy Maximization (MASEM) to address the challenge of sampling from complex, disconnected feasible sets. This technique uses a resampling scheme to maximize the entropy of the empirical distribution, effectively improving mixing across different components of the feasible set. MASEM demonstrates significant improvements in efficiency and scalability, outperforming existing methods by an order of magnitude in Sinkhorn distance on various benchmarks. AI

IMPACT Introduces a novel sampling technique that could enhance performance in AI applications like Bayesian optimization and robotics.

RANK_REASON Academic paper detailing a new sampling method. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New MASEM method improves sampling from disconnected distributions

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Academic paper detailing a new sampling method. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Marc Toussaint ·

    Manifold Sampling via Entropy Maximization

    Sampling from constrained distributions has a wide range of applications, including in Bayesian optimization and robotics. Prior work establishes convergence and feasibility guarantees for constrained sampling, but assumes that the feasible set is connected. However, in practice,…

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