TabOrder learns unsupervised causal variable orderings and enforces them with order-constrained attention for tabular prediction and imputation under distribution shifts.
Advances in neural information processing systems , volume=
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
years
2026 5verdicts
UNVERDICTED 5representative citing papers
PerturbedVAE disentangles perturbation-specific signals from invariant gene expression structure to recover causal representations and improve out-of-distribution prediction in single-cell perturbation modeling.
DAG-DC-ADMM jointly clusters subjects and learns their cluster-specific causal DAGs via structural equation modeling, groupwise truncated Lasso fusion penalties, and an ADMM solver for the resulting nonconvex problem.
DAGgr aggregates weighted candidate DAGs using out-of-sample predictive likelihood and an acyclicity-preserving threshold, with claimed finite-sample bounds and consistency, outperforming baselines in simulations and protein network data.
TriOpt recovers topological ordering via Sherman-Morrison rank-1 updates on linear kernels and then solves a convex continuous optimization for the linear DAG structure.
citing papers explorer
-
Learning Causal Orderings for In-Context Tabular Prediction
TabOrder learns unsupervised causal variable orderings and enforces them with order-constrained attention for tabular prediction and imputation under distribution shifts.
-
What Makes a Representation Good for Single-Cell Perturbation Prediction?
PerturbedVAE disentangles perturbation-specific signals from invariant gene expression structure to recover causal representations and improve out-of-distribution prediction in single-cell perturbation modeling.
-
A Unified Framework for Structure-Aware Clustering and Heterogeneous Causal Graph Learning
DAG-DC-ADMM jointly clusters subjects and learns their cluster-specific causal DAGs via structural equation modeling, groupwise truncated Lasso fusion penalties, and an ADMM solver for the resulting nonconvex problem.
-
Stable Causal Discovery via Directed Acyclic Graph Aggregation
DAGgr aggregates weighted candidate DAGs using out-of-sample predictive likelihood and an acyclicity-preserving threshold, with claimed finite-sample bounds and consistency, outperforming baselines in simulations and protein network data.
-
TriOpt: A Scalable Algorithm for Linear Causal Discovery
TriOpt recovers topological ordering via Sherman-Morrison rank-1 updates on linear kernels and then solves a convex continuous optimization for the linear DAG structure.