3 papers
cs.CR2026
Attestream: Usage-Aware Intermittent Data Distribution with Verifiable Lifecycle Provenance for Machine-Learning Data Streams
Kentaro Oda
Providers of continuously produced, commercially valuable data -- sensor streams, telemetry, and other feeds sold as machine-learning training material -- cannot observe whether de…
cs.LG2026
Separating Covariate Shift from Mechanism Change with Two Discriminators: CJSD, a Conditional Discrepancy with an Exact Covariate-Concept Decomposition
Kentaro Oda
After the inputs X are known, how much additional information does the label Y carry about which dataset a sample came from? That single quantity -- estimable as the difference of…
cs.LG2026
Evidence Before Expansion: Reuse, Spawn, or Defer in Lifelong Expert Pools
Kentaro Oda
Streaming systems that maintain a pool of expert models must repeatedly decide whether to reuse an existing expert for arriving data, spawn a new one, or defer. We present a decisi…