aiqbench analysis
This arXiv paper proposes a post-hoc method for removing nuisance information from representations that live on curved spaces (hyperspherical, hyperbolic, and SE(3)), rather than assuming Euclidean geometry. Its results are reported as trade-offs: nuisance recoverability drops while utility stays "competitive," with the KLIFS kinase case as the strongest joint result. Readers working on representation learning for structured scientific data should weigh whether the gains hold beyond these benchmarks.
IsoAct: Geometry-Aware Post-hoc Debiasing via Isometric Actions
arXiv:2610.34690v1 Announce Type: new Abstract: Learning representations that are robust to nuisance factors is essential for reliable deployment, but remains challenging when the representations lie on non-Euclidean manifolds. Existing debiasing methods typically assume Euclidean representations or apply linear interventions, which can distort the geometry of structured latent spaces. We propose IsoAct, a post-hoc framework that accounts for geometry in both nuisance identification and representation editing. It identifies nuisance-predictive directions using Fisher discriminant analysis (FDA) in tangent coordinates and applies corrections through manifold-native actions that preserve manifold membership. Controlled and real-world experiments on hyperspherical, hyperbolic, and $\mathrm{SE}(3)$ representations evaluate nuisance reduction, utility preservation, and manifold validity jointly. IsoAct reduces nuisance recoverability while retaining competitive utility. Its clearest joint result is on KLIFS, combining reduced kinase-group recoverability, strong structural prediction, and valid rigid poses.
Abstract:Learning representations that are robust to nuisance factors is essential for reliable deployment, but remains challenging when the representations lie on non-Euclidean manifolds. Existing debiasing methods typically assume Euclidean representations or apply linear interventions, which can distort the geometry of structured latent spaces. We propose IsoAct, a post-hoc framework that accounts for geometry in both nuisance identification and representation editing. It identifies nuisance-predictive directions using Fisher discriminant analysis (FDA) in tangent coordinates and applies corrections through manifold-native actions that preserve manifold membership. Controlled and real-world experiments on hyperspherical, hyperbolic, and $\mathrm{SE}(3)$ representations evaluate nuisance reduction, utility preservation, and manifold validity jointly. IsoAct reduces nuisance recoverability while retaining competitive utility. Its clearest joint result is on KLIFS, combining reduced kinase-group recoverability, strong structural prediction, and valid rigid poses.
Machine Learning (cs.LG)
arXiv:2610.34690 [cs.LG]
(or arXiv:2610.34690v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.34690 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 221 words