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New papers explore optimal transport for ML inference

By PulseAugur Editorial · [5 sources] ·

Two new arXiv papers explore advanced inference techniques in machine learning. One paper benchmarks likelihood-free inference methods, evaluating their performance with heavy-tailed and discrete data. The other paper bridges maximum likelihood and optimal transport for efficient inference and model selection in stochastic block models, proposing a regularized formulation for simultaneous parameter recovery and cluster number selection. AI

IMPACT These papers introduce novel statistical methods that could enhance the accuracy and efficiency of machine learning models in complex data scenarios.

RANK_REASON Two new academic papers published on arXiv.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 5 sources. How we write summaries →

COVERAGE [5]

  1. arXiv stat.ML TIER_1 English(EN) · Samira Aka, Marie Kratz, Philippe Naveau ·

    Benchmark of Likelihood-Free Inference Methods based on Neural and Optimal Transport Approaches

    arXiv:2605.30516v1 Announce Type: cross Abstract: Simulation-based inference (SBI) has become an increasingly important framework for parameter estimation in models for which simulation is feasible, including cases where likelihood evaluation is unavailable or costly. While recen…

  2. arXiv stat.ML TIER_1 English(EN) · Philippe Naveau ·

    Benchmark of Likelihood-Free Inference Methods based on Neural and Optimal Transport Approaches

    Simulation-based inference (SBI) has become an increasingly important framework for parameter estimation in models for which simulation is feasible, including cases where likelihood evaluation is unavailable or costly. While recent work has introduced benchmark frameworks to comp…

  3. arXiv stat.ML TIER_1 English(EN) · Simon Queric, C\'edric Vincent-Cuaz, Charles Bouveyron, Marco Corneli ·

    Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models

    arXiv:2605.28488v1 Announce Type: new Abstract: We study inference in stochastic block models (SBMs) through the lens of optimal transport (OT). We first establish that maximum likelihood variational inference (MLVI) can be interpreted as a semi-relaxed Gromov-Wasserstein (srGW) …

  4. arXiv stat.ML TIER_1 English(EN) · Marco Corneli ·

    Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models

    We study inference in stochastic block models (SBMs) through the lens of optimal transport (OT). We first establish that maximum likelihood variational inference (MLVI) can be interpreted as a semi-relaxed Gromov-Wasserstein (srGW) projection with entropic regularization. While t…

  5. arXiv stat.ML TIER_1 English(EN) · Marco Corneli ·

    Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models

    We study inference in stochastic block models (SBMs) through the lens of optimal transport (OT). We first establish that maximum likelihood variational inference (MLVI) can be interpreted as a semi-relaxed Gromov-Wasserstein (srGW) projection with entropic regularization. While t…

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