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20192024
most citedDAG-GNN: DAG Structure Learning with Graph Neural Networks

129 citations · 215 across the 11 of their papers we have counts for

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7 papers · 1 filter

cs.LG2024★ 1 cited

Self-Supervised Contrastive Pre-Training for Multivariate Point Processes

Xiao Shou, Dharmashankar Subramanian, Debarun Bhattacharjya +2

Self-supervision is one of the hallmarks of representation learning in the increasingly popular suite of foundation models including large language models such as BERT and GPT-3, b…

cs.LG2020★ 30 cited

DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks

Dennis Wei, Tian Gao, Yue Yu

This paper re-examines a continuous optimization framework dubbed NOTEARS for learning Bayesian networks. We first generalize existing algebraic characterizations of acyclicity to…

cs.LG2020

A Multi-Channel Neural Graphical Event Model with Negative Evidence

Tian Gao, Dharmashankar Subramanian, Karthikeyan Shanmugam +2

Event datasets are sequences of events of various types occurring irregularly over the time-line, and they are increasingly prevalent in numerous domains. Existing work for modelin…

cs.LG2019★ 7 cited

Characterization of Overlap in Observational Studies

Michael Oberst, Fredrik D. Johansson, Dennis Wei +4

Overlap between treatment groups is required for non-parametric estimation of causal effects. If a subgroup of subjects always receives the same intervention, we cannot estimate th…

cs.LG2019★ 16 cited

Generalized Linear Rule Models

Dennis Wei, Sanjeeb Dash, Tian Gao +1

This paper considers generalized linear models using rule-based features, also referred to as rule ensembles, for regression and probabilistic classification. Rules facilitate mode…

cs.LG2019★ 129 cited

DAG-GNN: DAG Structure Learning with Graph Neural Networks

Yue Yu, Jie Chen, Tian Gao +1

Learning a faithful directed acyclic graph (DAG) from samples of a joint distribution is a challenging combinatorial problem, owing to the intractable search space superexponential…

cs.LG2019

A Sequential Set Generation Method for Predicting Set-Valued Outputs

Tian Gao, Jie Chen, Vijil Chenthamarakshan +1

Consider a general machine learning setting where the output is a set of labels or sequences. This output set is unordered and its size varies with the input. Whereas multi-label c…