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=
9 Pith papers cite this work. Polarity classification is still indexing.
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Identifiability is proven for recurrent nonlinear switching dynamical systems under flexible assumptions, and ΩSDS is introduced as a flow-based estimator that improves disentanglement and forecasting over VAE-based methods.
LiMIAM and DirectLiMIAM enable causal discovery from observational data under mean-independent but dependent disturbances, outperforming LiNGAM in simulations and recovering plausible orderings in oil market data.
A new directed tree structure learning framework for zero-inflated compositional nodes uses KL divergence scoring and column-stochastic transition matrices for conditional expectations, with proven consistency and finite-sample guarantees.
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.
Proposes verb-based paradigm with timing computation to enable data-driven discovery of patient trajectories and counterfactual timing from EHR data without domain knowledge.
citing papers explorer
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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.
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End-to-End Identifiable and Consistent Recurrent Switching Dynamical Systems
Identifiability is proven for recurrent nonlinear switching dynamical systems under flexible assumptions, and ΩSDS is introduced as a flow-based estimator that improves disentanglement and forecasting over VAE-based methods.
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Causal discovery under mean independence and linearity
LiMIAM and DirectLiMIAM enable causal discovery from observational data under mean-independent but dependent disturbances, outperforming LiNGAM in simulations and recovering plausible orderings in oil market data.
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Structure Learning for Directed Trees with Zero-Inflated Compositional Nodes
A new directed tree structure learning framework for zero-inflated compositional nodes uses KL divergence scoring and column-stochastic transition matrices for conditional expectations, with proven consistency and finite-sample guarantees.
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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.
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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.
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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.
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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.
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To Use AI as Dice of Possibilities with Timing Computation
Proposes verb-based paradigm with timing computation to enable data-driven discovery of patient trajectories and counterfactual timing from EHR data without domain knowledge.