175 citations · 1.2k across the 65 of their papers we have counts for
8 papers · 1 filter
Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations
Yuanyuan Wang, Wei Huang, Mingming Gong +4
Ordinary Differential Equations (ODEs) have recently gained a lot of attention in machine learning. However, the theoretical aspects, e.g., identifiability and asymptotic propertie…
On the Sparse DAG Structure Learning Based on Adaptive Lasso
Danru Xu, Erdun Gao, Wei Huang +3
Learning the underlying Bayesian Networks (BNs), represented by directed acyclic graphs (DAGs), of the concerned events from purely-observational data is a crucial part of evidenti…
Instance-dependent Label-noise Learning under a Structural Causal Model
Yu Yao, Tongliang Liu, Mingming Gong +3
Label noise will degenerate the performance of deep learning algorithms because deep neural networks easily overfit label errors. Let X and Y denote the instance and clean label, r…
Likelihood-Free Overcomplete ICA and Applications in Causal Discovery
Chenwei Ding, Mingming Gong, Kun Zhang +1
Causal discovery witnessed significant progress over the past decades. In particular, many recent causal discovery methods make use of independent, non-Gaussian noise to achieve id…
Causal Generative Domain Adaptation Networks
Mingming Gong, Kun Zhang, Biwei Huang +3
An essential problem in domain adaptation is to understand and make use of distribution changes across domains. For this purpose, we first propose a flexible Generative Domain Adap…
Learning with Biased Complementary Labels
Xiyu Yu, Tongliang Liu, Mingming Gong +1
In this paper, we study the classification problem in which we have access to easily obtainable surrogate for true labels, namely complementary labels, which specify classes that o…
Transfer Learning with Label Noise
Xiyu Yu, Tongliang Liu, Mingming Gong +3
Transfer learning aims to improve learning in target domain by borrowing knowledge from a related but different source domain. To reduce the distribution shift between source and t…
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components
Philipp Geiger, Kun Zhang, Mingming Gong +2
A widely applied approach to causal inference from a non-experimental time series , often referred to as "(linear) Granger causal analysis", is to regress present on past and in…