中文

Machine Unlearningedit

Research topic on removing or correcting data influence from trained models.

Machine Unlearning studies how to remove, reduce, or correct the effect of selected training data after a model has already been trained. In this wiki it is treated as both a privacy topic and a data-centric systems topic: an unlearning method must say what it removes, how faithfully it approximates retraining, and how much computation or latency is saved.1

Introductionedit

The topic covers post-training data operations: certified deletion, exact removal in tree ensembles, and continuous reweighting for fairness or robustness correction. The shared question is whether a trained system can be revised after deployment without simply retraining from scratch each time the data record changes.

Role in this wikiedit

This page organizes Qiao's publication line on post-training data operations. The line includes certified deletion, weighted correction, and tree-ensemble updates. It is closely connected to Data Centric ML because the central object is not a new model architecture, but a data operation that changes model behavior. It also connects to Trustworthy AI, since deletion requests, fairness corrections, and robustness interventions are forms of governance over a trained system.

Publicationsedit

PaperVenue/status
Hessian-Free Online Certified UnlearningICLR 2025, 24-28 April 2025, Singapore.
DynFrs: An Efficient Framework for Machine Unlearning in Random ForestICLR 2025, 24-28 April 2025, Singapore.
Beyond Binary Erasure: Soft-Weighted Unlearning for Fairness and RobustnessAAAI 2026, 20-27 January 2026, Singapore.

Connection to Qiao's workedit

Qiao's unlearning papers cover complementary settings. Hessian-Free Online Certified Unlearning targets certified updates for convex objectives without explicit Hessian inversion. Beyond Binary Erasure generalizes deletion from binary remove-or-keep actions to continuous weights for fairness and robustness. DynFrs: An Efficient Framework for Machine Unlearning in Random Forest studies exact and efficient update mechanisms for random forests. Together they define an arc from mathematical certification to practical low-latency model maintenance.

See alsoedit

Footnotesedit

  1. A widely cited formulation is Bourtoule et al., "Machine Unlearning", IEEE Symposium on Security and Privacy 2021, which introduced SISA-style sharded, isolated, sliced, and aggregated training as a practical route to deletion. ↩