We have 3 of 57 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
The proposed AdaST is an adaptive ST forecasting framework that tackles challenges through a decompose-recompose paradigm, and significantly outperforms state-of-the-art baselines, validating the necessity of an adaptive approach.
Teacher Alignment is proposed, which directly adapts the teacher toward the student's distribution without discarding data or degrading reasoning quality, and which significantly outperforms baselines across diverse reasoning benchmarks and distillation methods.
This work presents the first model extraction attack specifically designed for graph classification under strict black-box constraints, which uses model explanation outputs to guide Monte Carlo edge sensitivity estimation toward decision boundaries, with Hoeffding concentration guarantees on estimation accuracy.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
