23 citations · 42 across the 6 of their papers we have counts for
5 papers · 1 filter
RanPAC: Random Projections and Pre-trained Models for Continual Learning
Mark D. McDonnell, Dong Gong, Amin Parveneh +2
Continual learning (CL) aims to incrementally learn different tasks (such as classification) in a non-stationary data stream without forgetting old ones. Most CL works focus on tac…
The Neural Process Family: Survey, Applications and Perspectives
Saurav Jha, Dong Gong, Xuesong Wang +2
The standard approaches to neural network implementation yield powerful function approximation capabilities but are limited in their abilities to learn meta representations and rea…
Truncated Matrix Power Iteration for Differentiable DAG Learning
Zhen Zhang, Ignavier Ng, Dong Gong +5
Recovering underlying Directed Acyclic Graph (DAG) structures from observational data is highly challenging due to the combinatorial nature of the DAG-constrained optimization prob…
Latent Covariate Shift: Unlocking Partial Identifiability for Multi-Source Domain Adaptation
Yuhang Liu, Zhen Zhang, Dong Gong +5
Multi-source domain adaptation (MSDA) addresses the challenge of learning a label prediction function for an unlabeled target domain by leveraging both the labeled data from multip…
Identifying Weight-Variant Latent Causal Models
Yuhang Liu, Zhen Zhang, Dong Gong +5
The task of causal representation learning aims to uncover latent higher-level causal variables that affect lower-level observations. Identifying the true latent causal variables f…