Author

A. Korotin

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#machine learning Preprint Sep 2026

Data Unlearning via Inverse Distillation

Multi-step matching models, including flow and diffusion models, produce high-quality outputs but incur substantial inference costs and may reproduce unwanted components of their training datasets. We introduce Inverse Distillation Unlearning (IDU), a unified framework that simultaneously distills a teacher multi-step...

Aleksei Leonov, N. Kornilov, Zhen-He Zhang et al. · 0 citations

#machine learning Preprint Sep 2026

Tilted Schr\"odinger Bridge Matching

Tilted Schrodinger Bridge Matching (TSBM) is introduced, a post-training method for fine-tuning a learned bridge between source and target toward a reward-tilted target while preserving source, and theoretical justification and practical algorithm are derived.

S. Kholkin, Evgeny Burnaev, A. Korotin · 1 citation