·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Mathematicians want proof OpenAI didn’t use their work3h◆Powering AI is an architecture problem3h◆Planning and Scheduling Business Processes under Control-Flow Uncertainty10h◆A Hierarchical Consistency Framework for Auditing Retrieval-Augmented Generation Systems10h◆Risk Is Not Review Value: Wrong-Answer Exposure Under Bounded Review Budgets10h◆From Simulated Citizens to Simulated Deliberation: Challenges in Representation and Interaction10h◆FinCUABuild: Can Agents Build Reliable Benchmarks for Dynamic Financial Computer Use?10h◆AgentIdeaBench: Benchmarking Scientific Ideation in the Agent Era10h◆Norms at a Price: Why RL-Based Alignment Can Promise Conditional Compliance at Best10h◆A radiographic world model for clinical reasoning and evidence generation10h◆The Profit Alignment Problem: How Profit Mandates Induce Alignment Failures in LLMs10h◆When Intelligence Becomes Agency: A Theory of Governed, Proactive Agency for Symbiotic AI Systems10h◆Do Large Language Models Know What They Don't Know II? A Fully Behavioral, Non-Cognitive Measure of Epistemic Honesty10h◆Explainable Temporal Attention-based Defect Detection For Fillet Joints in Real-Time Gas Metal Arc Welding Based on Multi-modal Data10h◆Quantization Amplifies Determinism, Not Bias: Scale-Dependent Behavioral Effects of Serving-Time Weight Compression10h◆PRIMUS: Identity, Governance, and Verification for Multi-Agent Federations10h◆Co-Evolving Harnesses and Models: On-Policy Correction Helps Weaker Models Catch Up Where Imitation Fails10h◆A Data-Driven Framework for Identifying and Prioritizing RPA Opportunities in Healthcare Processes10h◆Robots Influencing Humans to Reveal their Goals during Collaboration and Competition10h◆An Agent Model Abstraction for Human-AI Teaming Cognitive Coupling10h◆Mathematicians want proof OpenAI didn’t use their work3h◆Powering AI is an architecture problem3h◆Planning and Scheduling Business Processes under Control-Flow Uncertainty10h◆A Hierarchical Consistency Framework for Auditing Retrieval-Augmented Generation Systems10h◆Risk Is Not Review Value: Wrong-Answer Exposure Under Bounded Review Budgets10h◆From Simulated Citizens to Simulated Deliberation: Challenges in Representation and Interaction10h◆FinCUABuild: Can Agents Build Reliable Benchmarks for Dynamic Financial Computer Use?10h◆AgentIdeaBench: Benchmarking Scientific Ideation in the Agent Era10h◆Norms at a Price: Why RL-Based Alignment Can Promise Conditional Compliance at Best10h◆A radiographic world model for clinical reasoning and evidence generation10h◆The Profit Alignment Problem: How Profit Mandates Induce Alignment Failures in LLMs10h◆When Intelligence Becomes Agency: A Theory of Governed, Proactive Agency for Symbiotic AI Systems10h◆Do Large Language Models Know What They Don't Know II? A Fully Behavioral, Non-Cognitive Measure of Epistemic Honesty10h◆Explainable Temporal Attention-based Defect Detection For Fillet Joints in Real-Time Gas Metal Arc Welding Based on Multi-modal Data10h◆Quantization Amplifies Determinism, Not Bias: Scale-Dependent Behavioral Effects of Serving-Time Weight Compression10h◆PRIMUS: Identity, Governance, and Verification for Multi-Agent Federations10h◆Co-Evolving Harnesses and Models: On-Policy Correction Helps Weaker Models Catch Up Where Imitation Fails10h◆A Data-Driven Framework for Identifying and Prioritizing RPA Opportunities in Healthcare Processes10h◆Robots Influencing Humans to Reveal their Goals during Collaboration and Competition10h◆An Agent Model Abstraction for Human-AI Teaming Cognitive Coupling10h◆
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

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

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

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
arxivPlanning and Scheduling Business Processes under Control-Flow Uncertainty10harxivA Hierarchical Consistency Framework for Auditing Retrieval-Augmented Generation Systems10harxivRisk Is Not Review Value: Wrong-Answer Exposure Under Bounded Review Budgets10harxivFrom Simulated Citizens to Simulated Deliberation: Challenges in Representation and Interaction10h
The Bubble Brief
WEEKLY

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

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

Originally published on arxiv ↗
HomeModelsNews