quantile regression
PulseAugur coverage of quantile regression — every cluster mentioning quantile regression across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
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FlowCritic uses generative modeling for reinforcement learning value estimation
Researchers have introduced FlowCritic, a novel generative approach for value estimation in reinforcement learning. This method utilizes flow matching, a technique successful in generative modeling, to model and sample …
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New Loscar framework enhances causal discovery with quantile regression
Researchers have introduced Loscar, a new framework for causal discovery that extends the RESIT method to location-scale noise models (LSNMs). Unlike previous LSNM methods that rely on parametric assumptions for residua…
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New GBTSVR and GB-TSVQR models enhance regression by using granular balls
Researchers have introduced Granular Ball Twin Support Vector Regression (GBTSVR) and its quantile extension, GB-TSVQR, to improve regression models. These new models replace individual data points with compact "granula…
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New two-step Metropolis-Hastings algorithm enhances Bayesian empirical likelihood methods
Researchers have developed a novel two-step Metropolis-Hastings algorithm designed to improve the efficiency of Markov chain Monte Carlo (MCMC) sampling for Bayesian empirical likelihood (BayesEL) methods. This new appr…
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New methods advance uncertainty quantification in machine learning · 5 sources tracked
Researchers have introduced new methods for evaluating uncertainty quantification (UQ) in machine learning models. One approach, termed "decision-alignment," aims to ensure that UQ metrics meaningfully correlate with do…
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New SVM framework enhances quantile regression for heavy-tailed data
Researchers have developed a new Support Vector Machine (SVM) framework to improve quantile regression for datasets with heavy-tailed inputs. This approach focuses on the angular components of extreme observations to en…