12 citations · 12 across the 10 of their papers we have counts for
6 papers · 1 filter
On the Recoverability of Causal Relations from Bulk Gene Expression Data
Gongxu Luo, Boyang Sun, Kun Zhang
Bulk gene expression profiling, which aggregates pooled RNA across cells within a biological sample, remains important in the single-cell era because it is typically less noisy, mo…
MoRe: Modular Representations for Principled Continual Representation Learning on Sequential Data
Jiaqi Sun, Boyang Sun, Rasmy M. H. +2
Continual learning requires models to adapt to new data while preserving previously acquired knowledge. At its core, this challenge can be viewed as principled one-step adaptation:…
Permutation-Based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Data
Xinshuai Dong, Ignavier Ng, Boyang Sun +6
Recent advances have shown that statistical tests for the rank of cross-covariance matrices play an important role in causal discovery. These rank tests include partial correlation…
Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders
Gongxu Luo, Haoyue Dai, Loka Li +3
Gene regulatory network inference (GRNI) aims to discover how genes causally regulate each other from gene expression data. It is well-known that statistical dependencies in observ…
Continual Learning of Nonlinear Independent Representations
Boyang Sun, Ignavier Ng, Guangyi Chen +3
Identifying the causal relations between interested variables plays a pivotal role in representation learning as it provides deep insights into the dataset. Identifiability, as the…
Subspace Identification for Multi-Source Domain Adaptation
Zijian Li, Ruichu Cai, Guangyi Chen +3
Multi-source domain adaptation (MSDA) methods aim to transfer knowledge from multiple labeled source domains to an unlabeled target domain. Although current methods achieve target…