Research Archive

We formulate machine unlearning for online L-BFGS as a counterfactual state-alignment problem. Given an actual event stream and a deletion-edited counterfactual stream, the target of unlearning is the optimizer state that would have arisen had the deleted samples never been processed. We introduce state-aware metrics that separately measure parameter error, memory-operator error, combined state error, and update-direction error. The memory metric compares the inverse-Hessian actions induced by the o-L-BFGS memory, rather than treating curvature pairs as of finite influence. Under convexity assumptions, we derive a recursive bound on counterfactual state deviation. We then evaluate a state-aware benchmark of deletion interventions, including memory-only and parameter-only corrections, against an counterfactual oracle model. These results show that unlearning for online L-BFGS is not merely a parameter-correction problem: it requires alignment with a realizable counterfactual optimizer state.

Individuals have the right to be forgotten, but does AI has the right to forget?

The Lab investigates the math behind a machine's right to forget, and the human's right to be forgotten as presented by the seminal Sekhari paper.

Unlearning is more than performance. It's a matter of model state.

We review Sinha and Mahajan's take on the sufficiency of an information state. We're such fans, we decide to take it even further.

A primer on entropy, mutual information, and variational inference.