Public articles linked to the same research event.
arXiv The work presents RDTU, a Residual Diffusion framework for time-series unlearning: it first uses a retained-set neural tangent kernel predictor to obtain a deletion-compatible base forecast, then quantifies the global and local structural support of each affected window via the volume contribution of the retained-reference data, and finally has a diffusion model generate a residual correction that estimates the counterfactual forecast, yielding a pseudo-label field that guides a lightweight model update; experiments show RDTU consistently produces unlearned models that most closely match exact retraining.
The work presents RDTU, a Residual Diffusion framework for time-series unlearning: it first uses a retained-set neural tangent kernel predictor to obtain a deletion-compatible base forecast, then quantifies the global and local structural support of each affected window via the volume contribution of the retained-reference data, and finally has a diffusion model generate a residual correction that estimates the counterfactual forecast, yielding a pseudo-label field that guides a lightweight model update; experiments show RDTU consistently produces unlearned models that most closely match exact retraining.
The work presents RDTU, a Residual Diffusion framework for time-series unlearning: it first uses a retained-set neural tangent kernel predictor to obtain a deletion-compatible base forecast, then quantifies the global and local structural support of each affected window via the volume contribution of the retained-reference data, and finally has a diffusion model generate a residual correction that estimates the counterfactual forecast, yielding a pseudo-label field that guides a lightweight model update; experiments show RDTU consistently produces unlearned models that most closely match exact retraining.
The work presents RDTU, a Residual Diffusion framework for time-series unlearning: it first uses a retained-set neural tangent kernel predictor to obtain a deletion-compatible base forecast, then quantifies the global and local structural support of each affected window via the volume contribution of the retained-reference data, and finally has a diffusion model generate a residual correction that estimates the counterfactual forecast, yielding a pseudo-label field that guides a lightweight model update; experiments show RDTU consistently produces unlearned models that most closely match exact retraining.