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Showing papers from Hokkaido University Show all papers

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NeurIPS 2026HokkaidoHokkaido University A*STARImage recognition

Closed-Form Linear-Probe Dataset Distillation for Pre-trained Vision Models

CLP-DD distills synthetic datasets for frozen-feature linear probing via a closed-form kernel ridge solver, achieving near-state-of-the-art accuracy with roughly 14x faster training and far lower memory.

Bincheng Peng, Miki Haseyama, Guang Li, Ping Liu and 1 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 15 of 20 reviewers recommend it
lenient 3/5
medium 9/10
strict 3/5
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NeurIPS 2026HokkaidoHokkaido University Model-based RL

Predictive but Not Plannable: RC-aux for Latent World Models

RC-aux improves latent world model planning by adding multi-horizon prediction and budget-conditioned reachability supervision to align latent spaces with long-horizon search.

Wenyuan Li, Guang Li, Keisuke Maeda, Takahiro Ogawa and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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11/20 AI panelreviewers recommend it

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AI panel: 11 of 20 reviewers recommend it
lenient 4/5
medium 6/10
strict 1/5