45 citations · 128 across the 9 of their papers we have counts for
9 papers
What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL?
Rui Yang, Yong Lin, Xiaoteng Ma +3
Offline goal-conditioned RL (GCRL) offers a way to train general-purpose agents from fully offline datasets. In addition to being conservative within the dataset, the generalizatio…
Active Prompting with Chain-of-Thought for Large Language Models
Shizhe Diao, Pengcheng Wang, Yong Lin +3
The increasing scale of large language models (LLMs) brings emergent abilities to various complex tasks requiring reasoning, such as arithmetic and commonsense reasoning. It is kno…
Probabilistic Bilevel Coreset Selection
Xiao Zhou, Renjie Pi, Weizhong Zhang +2
The goal of coreset selection in supervised learning is to produce a weighted subset of data, so that training only on the subset achieves similar performance as training on the en…
Model Agnostic Sample Reweighting for Out-of-Distribution Learning
Xiao Zhou, Yong Lin, Renjie Pi +4
Distributionally robust optimization (DRO) and invariant risk minimization (IRM) are two popular methods proposed to improve out-of-distribution (OOD) generalization performance of…
Stable Learning via Sparse Variable Independence
Han Yu, Peng Cui, Yue He +4
The problem of covariate-shift generalization has attracted intensive research attention. Previous stable learning algorithms employ sample reweighting schemes to decorrelate the c…
Particle-based Variational Inference with Preconditioned Functional Gradient Flow
Hanze Dong, Xi Wang, Yong Lin +1
Particle-based variational inference (VI) minimizes the KL divergence between model samples and the target posterior with gradient flow estimates. With the popularity of Stein vari…
Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learning
Jiahui Gao, Renjie Pi, Yong Lin +7
There is a rising interest in further exploring the zero-shot learning potential of large pre-trained language models (PLMs). A new paradigm called data-generation-based zero-shot…
ZIN: When and How to Learn Invariance Without Environment Partition?
Yong Lin, Shengyu Zhu, Lu Tan +1
It is commonplace to encounter heterogeneous data, of which some aspects of the data distribution may vary but the underlying causal mechanisms remain constant. When data are divid…
Black-box Prompt Learning for Pre-trained Language Models
Shizhe Diao, Zhichao Huang, Ruijia Xu +4
The increasing scale of general-purpose Pre-trained Language Models (PLMs) necessitates the study of more efficient adaptation across different downstream tasks. In this paper, we…