·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Anthropic’s Dario Amodei responds: doesn’t oppose open-weight models, but fears Chinese AI1h◆Satya Nadella says companies that trust one AI for everything may not survive4h◆PSA: Your Claude shared chats and Artifacts may have ended up on Google5h◆Microsoft launches its first cybersecurity model, plus a new agentic cybersecurity system7h◆OpenAI called the Hugging Face attack unprecedented. But we’ve been here before.7h◆OpenAI’s Hugging Face breach has reignited the debate over alignment and control8h◆Why China is giving away its best AI models8h◆Threads users can now chat with Meta AI in their DMs8h◆Google’s AI search is rapidly becoming the default, new data shows9h◆Power up your AI infrastructure! A first look at the Smart Systems Stage agenda at TechCrunch Disrupt 202610h◆This $9 key physically locks your most addictive apps10h◆Ilya Sutskever’s Safe Superintelligence partners with Nvidia to scale its AI research10h◆Enigma raises $71M to make controlling a robot as easy as adjusting the volume12h◆Nvidia, Microsoft launch open AI security alliance — without OpenAI, Google, or Anthropic13h◆The path to artificial superintelligence13h◆Closing the data loop in AI-driven drug discovery13h◆Building the enterprise environment for agentic AI14h◆NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics16h◆A Consensus-Based Framework for Relative Preference Evaluation of Large Language Models21h◆Probing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders21h◆Anthropic’s Dario Amodei responds: doesn’t oppose open-weight models, but fears Chinese AI1h◆Satya Nadella says companies that trust one AI for everything may not survive4h◆PSA: Your Claude shared chats and Artifacts may have ended up on Google5h◆Microsoft launches its first cybersecurity model, plus a new agentic cybersecurity system7h◆OpenAI called the Hugging Face attack unprecedented. But we’ve been here before.7h◆OpenAI’s Hugging Face breach has reignited the debate over alignment and control8h◆Why China is giving away its best AI models8h◆Threads users can now chat with Meta AI in their DMs8h◆Google’s AI search is rapidly becoming the default, new data shows9h◆Power up your AI infrastructure! A first look at the Smart Systems Stage agenda at TechCrunch Disrupt 202610h◆This $9 key physically locks your most addictive apps10h◆Ilya Sutskever’s Safe Superintelligence partners with Nvidia to scale its AI research10h◆Enigma raises $71M to make controlling a robot as easy as adjusting the volume12h◆Nvidia, Microsoft launch open AI security alliance — without OpenAI, Google, or Anthropic13h◆The path to artificial superintelligence13h◆Closing the data loop in AI-driven drug discovery13h◆Building the enterprise environment for agentic AI14h◆NVIDIA Cosmos-H-Dreams: Bringing Real-Time Generative Simulation to Surgical Robotics16h◆A Consensus-Based Framework for Relative Preference Evaluation of Large Language Models21h◆Probing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders21h◆
News/Generative Bayesian Optimization: Generative Models as Acquisition Functions
arxiv
PublishedMay 15, 2026 at 4:00 AM
—neutral

Generative Bayesian Optimization: Generative Models as Acquisition Functions

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

arXiv:2510.25240v3 Announce Type: replace-cross Abstract: We present a general strategy for turning generative models into candidate solution samplers for batch Bayesian optimization (BO). The use of generative models for BO enables large batch scaling as generative sampling, optimization of non-con

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

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

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#optimization#machine-learning#research

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#optimization#machine-learning#research

Related coverage

More from ARXIV
arxivA Consensus-Based Framework for Relative Preference Evaluation of Large Language Models21harxivProbing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders21h
The Bubble Brief
WEEKLY

Read optimization insights every Tuesday — top movers, new releases, story of the week.

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

Originally published on arxiv ↗
HomeModelsNews