·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Predictive Self-Supervised Learning Provably Identifies Stochastic Signals under Nuisance2h◆Boosting Adversarial Robustness and Generalization with Dictionary Structure2h◆A Dominant Supplier Slows Recursive Drift More Than It Steers It2h◆COMiT: Learning Structured Visual Tokens through Sequential Communication2h◆Compress to Remember: Learning Compact Memory via On-Policy Distillation for Long Video Generation2h◆Data-Free Pruning of Self-Attention Layers in LLMs2h◆Screening Is Enough2h◆Data Unlearning via Inverse Distillation2h◆ReCIRC: Rectified Conformal Risk Control2h◆Replay-buffer engineering for noise-aware quantum circuit optimization2h◆SINO: Scale-Invariant Neural Operator2h◆Preferent Compression Bounds Are Tight2h◆Fundamental Limits of Transferability and Equivariance in Algebraic Signal Models I: Finite Dimensions2h◆How to Tame a Multi-Headed Hydra? Adaptive Multi-Category Safety Steering for Large Language Models2h◆Bregman Consensus2h◆In-Context Learning for Robots: Methods and Applications2h◆Modal Logic Neural Networks2h◆Volatility-Clustering Adaptation for Financial Time Series2h◆Reference-Guided Machine Unlearning2h◆Agentic Federated Learning: Rule-Based Client and Server Agents for Adaptive Training2h◆Predictive Self-Supervised Learning Provably Identifies Stochastic Signals under Nuisance2h◆Boosting Adversarial Robustness and Generalization with Dictionary Structure2h◆A Dominant Supplier Slows Recursive Drift More Than It Steers It2h◆COMiT: Learning Structured Visual Tokens through Sequential Communication2h◆Compress to Remember: Learning Compact Memory via On-Policy Distillation for Long Video Generation2h◆Data-Free Pruning of Self-Attention Layers in LLMs2h◆Screening Is Enough2h◆Data Unlearning via Inverse Distillation2h◆ReCIRC: Rectified Conformal Risk Control2h◆Replay-buffer engineering for noise-aware quantum circuit optimization2h◆SINO: Scale-Invariant Neural Operator2h◆Preferent Compression Bounds Are Tight2h◆Fundamental Limits of Transferability and Equivariance in Algebraic Signal Models I: Finite Dimensions2h◆How to Tame a Multi-Headed Hydra? Adaptive Multi-Category Safety Steering for Large Language Models2h◆Bregman Consensus2h◆In-Context Learning for Robots: Methods and Applications2h◆Modal Logic Neural Networks2h◆Volatility-Clustering Adaptation for Financial Time Series2h◆Reference-Guided Machine Unlearning2h◆Agentic Federated Learning: Rule-Based Client and Server Agents for Adaptive Training2h◆
News/Reference-Guided Machine Unlearning
arxiv
PublishedOctober 1, 2026 at 4:00 AM
—neutral

Reference-Guided Machine Unlearning

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

arXiv:2603.11210v2 Announce Type: replace Abstract: Machine unlearning aims to remove the influence of specific training data from a model while preserving its general utility. In vision, many approximate unlearning methods pursue this goal through degradation-based heuristics, such as loss maximiza

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 →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivPredictive Self-Supervised Learning Provably Identifies Stochastic Signals under Nuisance2harxivBoosting Adversarial Robustness and Generalization with Dictionary Structure2harxivA Dominant Supplier Slows Recursive Drift More Than It Steers It2harxivCOMiT: Learning Structured Visual Tokens through Sequential Communication2h
The Bubble Brief
WEEKLY

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

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

Originally published on arxiv ↗
Built by Marouane Gazouzi
HomeModelsNews