·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Retrieval Augmented Generation Compositionality3h◆VeriEvolve: Evaluator-First Evolution for Tool-Using Agents3h◆TNODEV: Toolbox for Neural ODE Verification3h◆Learning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding3h◆On the Expressive Power and Limitations of Multi-Layer SSMs3h◆From Weather to Enhanced Weathering: Weather Foundation Models for Carbon Dioxide Removal at Operational Scale3h◆Predictor-Guided Latent Space Codon Optimization for Maximizing Protein Expression3h◆A Generalisation Signal Need Not Be a Model-Selection Signal3h◆Mitigating Reward Hacking in Realistic Scenarios Through Representation Engineering3h◆Aggregation of Published Non-Uniform Axial Power Data for Phase II of the OECD/NEA AI/ML Critical Heat Flux Benchmark3h◆Sparse Equation Matching: A Derivative-Free Learning for General-Order Dynamical Systems3h◆EgoHand: Monocular 3D Hand Reconstruction from Egocentric Video3h◆Learning to Incentivise: Using Reinforcement Learning for Sustainable Urban Mobility3h◆Energy Estimation for Virtual Servers Using Resource Utilization and Machine Learning3h◆Robust Tangent Space Estimation via Laplacian Eigenvector Gradient Orthogonalization3h◆ChemBART: Pre-training a Reaction Language Model for Comprehensive Synthesis Planning3h◆To Distill or Decide? Understanding the Algorithmic Trade-off in Partially Observable Reinforcement Learning3h◆FlowCritic: Bridging Value Estimation with Flow Matching in Reinforcement Learning3h◆Revisiting GAN with Bayes-Optimal Discrimination3h◆Specter: Fast MoE Inference via Quantization-Enabled Multi-Step Speculative Offloading3h◆Retrieval Augmented Generation Compositionality3h◆VeriEvolve: Evaluator-First Evolution for Tool-Using Agents3h◆TNODEV: Toolbox for Neural ODE Verification3h◆Learning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding3h◆On the Expressive Power and Limitations of Multi-Layer SSMs3h◆From Weather to Enhanced Weathering: Weather Foundation Models for Carbon Dioxide Removal at Operational Scale3h◆Predictor-Guided Latent Space Codon Optimization for Maximizing Protein Expression3h◆A Generalisation Signal Need Not Be a Model-Selection Signal3h◆Mitigating Reward Hacking in Realistic Scenarios Through Representation Engineering3h◆Aggregation of Published Non-Uniform Axial Power Data for Phase II of the OECD/NEA AI/ML Critical Heat Flux Benchmark3h◆Sparse Equation Matching: A Derivative-Free Learning for General-Order Dynamical Systems3h◆EgoHand: Monocular 3D Hand Reconstruction from Egocentric Video3h◆Learning to Incentivise: Using Reinforcement Learning for Sustainable Urban Mobility3h◆Energy Estimation for Virtual Servers Using Resource Utilization and Machine Learning3h◆Robust Tangent Space Estimation via Laplacian Eigenvector Gradient Orthogonalization3h◆ChemBART: Pre-training a Reaction Language Model for Comprehensive Synthesis Planning3h◆To Distill or Decide? Understanding the Algorithmic Trade-off in Partially Observable Reinforcement Learning3h◆FlowCritic: Bridging Value Estimation with Flow Matching in Reinforcement Learning3h◆Revisiting GAN with Bayes-Optimal Discrimination3h◆Specter: Fast MoE Inference via Quantization-Enabled Multi-Step Speculative Offloading3h◆
News/To discretize continually: Mean shift interacting particle systems for Bayesian inference
arxiv
PublishedMay 15, 2026 at 4:00 AM
—neutral

To discretize continually: Mean shift interacting particle systems for Bayesian inference

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2605.14142v1 Announce Type: cross Abstract: Integration against a probability distribution given its unnormalized density is a central task in Bayesian inference and other fields. We introduce new methods for approximating such expectations with a small set of weighted samples -- i.e., a quadr

Stay posted· Newsletter

Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#machine-learning#bayesian-inference#sampling#optimization

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#machine-learning#bayesian-inference#sampling#optimization

Related coverage

More from ARXIV
arxivRetrieval Augmented Generation Compositionality3harxivVeriEvolve: Evaluator-First Evolution for Tool-Using Agents3harxivTNODEV: Toolbox for Neural ODE Verification3harxivLearning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding3h
The Bubble Brief
MON · WED · FRI

Read machine-learning insights every Monday, Wednesday and Friday — top movers, new models, story of the week.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Originally published on arxiv ↗
PrivacyTermsRefundsLegal notice|AboutMethodologyContact

© 2026 Databubble

Built by Marouane Gazouzi · Readyo, AI interview coach

HomeModelsNews