arXiv Machine Learning By Christoph Koller, Johannes F\"urnkranz, Timo Bertram

RePAIR: Predictive Self-Supervised Representation Learning in Chess

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arXiv:2606. 11860v1 Announce Type: new Abstract: In this paper, we introduce Representation Prediction via Autoencoding using Iterative Refinement (RePAIR) - a novel self-supervised representation learning architecture that synthesizes Masked Autoencoders (MAE), Joint Embedding Predictive Architectures (JEPA), and Bidirectional Encoder Representations from Transformers (BERT).

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arXiv AI
Aug 26

Disentangled Skill Representations for Predictive Human Modeling

The paper introduces Skill Abstraction with Interpretable Latents (SAIL), a method that models human skill as a persistent, multi‑dimensional construct inferred from naturalistic behavior over time. SAIL produces a robust skill embedding that blends expert and novice bases, learns transferable subskills through counterfactual subskill swaps, and supports skill‑informed behavior prediction across various in‑domain contexts. Experiments on racing and baseball demonstrate that SAIL achieves strong predictive performance, improves behaviorally grounded disentanglement compared to baselines, and enhances downstream AI coaching outcomes.

By Mariah Schrum, Deepak Gopinath, Srijan Srivatsa, Guy Rosman, Tiffany Chen