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New methods enhance uncertainty quantification in ML models

By PulseAugur Editorial · [5 sources] ·

Researchers have developed new theoretical frameworks and practical methods for improving uncertainty quantification in machine learning models. One paper introduces discrete-time approximations for stochastic gradient methods to accurately estimate covariance and autocorrelation times, offering better tuning guidance for large batch sizes or misspecified models. Another study proposes a statistical scaling limit theory for SGLD-Gibbs to provide principled hyperparameter tuning for latent variable models, leading to more meaningful uncertainty estimates. A third paper presents a Gaussian process-based approach for causal uncertainty quantification of interventional functions, improving reliability in high-stakes applications. AI

IMPACT These advancements in uncertainty quantification could lead to more reliable and trustworthy AI systems, particularly in critical applications where understanding model confidence is paramount.

RANK_REASON The cluster consists of multiple academic papers published on arXiv detailing new theoretical and methodological advancements in machine learning, specifically concerning uncertainty quantification.

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) · Yu Wang, Jie Ding, Jonathan H. Huggins ·

    Accurate Large-sample Uncertainty Quantification using Stochastic Gradient Markov Chain Monte Carlo

    arXiv:2606.00293v1 Announce Type: cross Abstract: Tuning algorithms such as stochastic gradient descent (SGD) and stochastic gradient Langevin dynamics (SGLD) for approximate sampling and uncertainty quantification remains challenging, particularly in the practically relevant set…

  2. arXiv stat.ML TIER_1 English(EN) · Xiaoyu Wang, Jonathan H. Huggins ·

    Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo

    arXiv:2606.00309v1 Announce Type: cross Abstract: Stochastic gradient Langevin dynamics combined with Gibbs updates (SGLD--Gibbs) provides a highly scalable approach to approximate Bayesian inference in latent variable models. However, it remains unclear how to tune the algorithm…

  3. arXiv stat.ML TIER_1 English(EN) · Hugh Dance, Peter Orbanz, Arthur Gretton ·

    Interventional Processes for Causal Uncertainty Quantification

    arXiv:2410.14483v3 Announce Type: replace Abstract: Reliable uncertainty quantification for causal effects is crucial in high-stakes applications, but remains challenging when the target is an entire function rather than a scalar estimand. In this work, we introduce a GP-based ap…

  4. arXiv stat.ML TIER_1 English(EN) · Jonathan H. Huggins ·

    Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo

    Stochastic gradient Langevin dynamics combined with Gibbs updates (SGLD--Gibbs) provides a highly scalable approach to approximate Bayesian inference in latent variable models. However, it remains unclear how to tune the algorithm's hyperparameters in a principled manner to ensur…

  5. arXiv stat.ML TIER_1 English(EN) · Jonathan H. Huggins ·

    Accurate Large-sample Uncertainty Quantification using Stochastic Gradient Markov Chain Monte Carlo

    Tuning algorithms such as stochastic gradient descent (SGD) and stochastic gradient Langevin dynamics (SGLD) for approximate sampling and uncertainty quantification remains challenging, particularly in the practically relevant settings when the batch size is large or the model is…

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