·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
VisionQuest is what happens when AI really goes rogue48m◆Build specialized multimodal models with LFM31h◆Frontier AI labs say their models refuted the Exponential Time Hypothesis. What does that mean?1h◆Anthropic’s elaborate plan to keep Wall Street at bay1h◆Google’s Genie 4 world model adds audio, more realistic worlds, and real-time steering1h◆Firefox’s Smart Window adds AI form filling and webpage monitoring2h◆Can we trust LLMs to judge whether a research idea is novel?2h◆Google’s next Genie AI model generates more realistic experiences2h◆Meta AI director Rohit Patel leaves to take on Microsoft Office and Google Workspace3h◆Apple is adding an AI chatbot to Pages, Keynote, and Numbers4h◆Japan internet giant GMO says hackers posted stolen customer data4h◆General Compute raises $100 million Series A, countering the neocloud disaster narrative5h◆Opera introduces a new web standard to let users deploy AI locally in their browser5h◆Industrial Intelligence: The next growth opportunity for manufacturing6h◆Nvidia backs Indian startup helping companies run AI models with fewer GPUs7h◆Data center backlash is uniting America8h◆OpenAI expands partnership with Oracle to bring its apps to ChatGPT11h◆When Generated Information Returns: Error Persistence in LLM-Mediated Information Ecosystems14h◆Query-Driven Learning for Lifted Reasoning14h◆The Social World Model Evaluation Gap14h◆VisionQuest is what happens when AI really goes rogue48m◆Build specialized multimodal models with LFM31h◆Frontier AI labs say their models refuted the Exponential Time Hypothesis. What does that mean?1h◆Anthropic’s elaborate plan to keep Wall Street at bay1h◆Google’s Genie 4 world model adds audio, more realistic worlds, and real-time steering1h◆Firefox’s Smart Window adds AI form filling and webpage monitoring2h◆Can we trust LLMs to judge whether a research idea is novel?2h◆Google’s next Genie AI model generates more realistic experiences2h◆Meta AI director Rohit Patel leaves to take on Microsoft Office and Google Workspace3h◆Apple is adding an AI chatbot to Pages, Keynote, and Numbers4h◆Japan internet giant GMO says hackers posted stolen customer data4h◆General Compute raises $100 million Series A, countering the neocloud disaster narrative5h◆Opera introduces a new web standard to let users deploy AI locally in their browser5h◆Industrial Intelligence: The next growth opportunity for manufacturing6h◆Nvidia backs Indian startup helping companies run AI models with fewer GPUs7h◆Data center backlash is uniting America8h◆OpenAI expands partnership with Oracle to bring its apps to ChatGPT11h◆When Generated Information Returns: Error Persistence in LLM-Mediated Information Ecosystems14h◆Query-Driven Learning for Lifted Reasoning14h◆The Social World Model Evaluation Gap14h◆
News/Alternate loss functions and regression models that achieve robustness to outliers by modulating the learning rate
arxiv
PublishedJune 25, 2026 at 4:00 AM
—neutral

Alternate loss functions and regression models that achieve robustness to outliers by modulating the learning rate

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

arXiv:2606.22068v2 Announce Type: replace-cross Abstract: Most real-world datasets used for training supervised learning models are contaminated with noisy data and outliers leading to large prediction errors. This paper proposes a new approach for achieving robustness where the learning rate is mod

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
Mentioned models
02
  • 01
    Square Root Loss (SRL)
  • 02
    Smooth Mean Absolute Error (SMAE)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#robustness#outliers#regression#loss-functions

No replies yet. Be first.

Mentioned models
02
  • 01
    Square Root Loss (SRL)
  • 02
    Smooth Mean Absolute Error (SMAE)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#robustness#outliers#regression#loss-functions

Related coverage

More from ARXIV
arxivWhen Generated Information Returns: Error Persistence in LLM-Mediated Information Ecosystems14harxivQuery-Driven Learning for Lifted Reasoning14harxivThe Social World Model Evaluation Gap14h
The Bubble Brief
MON · WED · FRI

Read robustness 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