4 papers · 1 filter
Context-Constrained Transfer Learning for Tabular Foundation Models via Data Distillation
Yijun Lin, Sai Li
Tabular Foundation Models (TFMs) have demonstrated strong empirical performance as black-box inference engines through in-context learning. However, their use in transfer learning…
Routing-Aware Expert Calibration for Machine Unlearning in Mixture-of-Experts Language Models
Jingyi Xie, Yijun Lin, Yinjiang Xiong +2
Machine unlearning is increasingly important for large language models, yet unlearning in Mixture-of-Experts (MoE) architectures remains underexplored. Unlike dense models, MoE arc…
Efficient machine unlearning with minimax optimality
Jingyi Xie, Linjun Zhang, Sai Li
There is a growing demand for efficient data removal to comply with regulations like the GDPR and to mitigate the influence of biased or corrupted data. This has motivated the fiel…
Personalizing black-box models for nonparametric regression with minimax optimality
Sai Li, Linjun Zhang
Recent advances in large-scale models, including deep neural networks and large language models, have substantially improved performance across a wide range of learning tasks. The…