CAPS provides an iterative differentially private synthesis method that outperforms one-shot baselines on authentic educational real-world data.
A Comprehensive Survey of Continual Learning: Theory, Method and Application.IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 5362–5383
3 Pith papers cite this work. Polarity classification is still indexing.
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Survey identifying technical and supply-chain barriers to GDPR data subject rights in ML, with new framing of 'models in the dark' for downstream opacity.
MoLEM achieves a 10.40% average accuracy improvement in continual learning tasks across math, science, and code by using dynamic latent memory experts with a frozen base model and stage-specific autoencoders for routing.
citing papers explorer
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Cyclic Adaptive Private Synthesis for Sharing Real-World Data in Education
CAPS provides an iterative differentially private synthesis method that outperforms one-shot baselines on authentic educational real-world data.
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Short paper: Models in the dark -- Rectification and erasure under GDPR in ML supply chains
Survey identifying technical and supply-chain barriers to GDPR data subject rights in ML, with new framing of 'models in the dark' for downstream opacity.
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Dynamic Mixture of Latent Memories for Self-Evolving Agents
MoLEM achieves a 10.40% average accuracy improvement in continual learning tasks across math, science, and code by using dynamic latent memory experts with a frozen base model and stage-specific autoencoders for routing.