10 citations · 14 across the 7 of their papers we have counts for
4 papers · 1 filter
Sparse Tuning Enhances Plasticity in PTM-based Continual Learning
Huan Zhang, Shenghua Fan, Shuyu Dong +3
Continual Learning with Pre-trained Models holds great promise for efficient adaptation across sequential tasks. However, most existing approaches freeze PTMs and rely on auxiliary…
DCILP: A Distributed Approach for Large-Scale Causal Structure Learning
Shuyu Dong, Michèle Sebag, Kento Uemura +4
Causal learning tackles the computationally demanding task of estimating causal graphs. This paper introduces a new divide-and-conquer approach for causal graph learning, called DC…
Learning Large Causal Structures from Inverse Covariance Matrix via Sparse Matrix Decomposition
Shuyu Dong, Kento Uemura, Akito Fujii +4
Learning causal structures from observational data is a fundamental problem facing important computational challenges when the number of variables is large. In the context of linea…
From graphs to DAGs: a low-complexity model and a scalable algorithm
Shuyu Dong, Michèle Sebag
Learning directed acyclic graphs (DAGs) is long known a critical challenge at the core of probabilistic and causal modeling. The NoTears approach of (Zheng et al., 2018), through a…