Cover Image

Markov Equivalence and Consistency in Differentiable Structure Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Deng, Chang, Bello, Kevin, Ravikumar, Pradeep, Aragam, Bryon
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Statistics Theory Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Differentiable Structure Learning and Causal Discovery for General Binary Data
by: Deng, Chang, et al.
Published: (2025)

Model-free Estimation of Latent Structure via Multiscale Nonparametric Maximum Likelihood
by: Aragam, Bryon, et al.
Published: (2024)

iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models
by: Chen, Tianyu, et al.
Published: (2023)

Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models
by: Rajendran, Goutham, et al.
Published: (2024)

Greedy equivalence search for nonparametric graphical models
by: Aragam, Bryon
Published: (2024)

KL-BSS: Rethinking optimality for neighbourhood selection in structural equation models
by: Gao, Ming, et al.
Published: (2023)

Optimal structure learning and conditional independence testing
by: Gao, Ming, et al.
Published: (2025)

Beyond identifiability: Learning causal representations with few environments and finite samples
by: Lee, Inbeom, et al.
Published: (2026)

Learning general conditional independence structures via the neighbourhood lattice
by: Amini, Arash A., et al.
Published: (2022)

High-Dimensional Markov-switching Ordinary Differential Processes
by: Tsai, Katherine, et al.
Published: (2024)

Towards Interpretable Deep Generative Models via Causal Representation Learning
by: Moran, Gemma E., et al.
Published: (2025)

Theoretical Analysis of Engression and Reverse Markov Engression
by: Huang, Jiaqi, et al.
Published: (2026)

Identifying General Mechanism Shifts in Linear Causal Representations
by: Chen, Tianyu, et al.
Published: (2024)

Learning Linear Polytree Structural Equation Models
by: Lou, Xingmei, et al.
Published: (2021)

Strongly Consistent Community Detection in Popularity Adjusted Block Models
by: Yuan, Quan, et al.
Published: (2025)

Optimal Differentially Private Ranking from Pairwise Comparisons
by: Cai, T. Tony, et al.
Published: (2025)

GRAND: Graph Release with Assured Node Differential Privacy
by: Liu, Suqing, et al.
Published: (2025)

Hierarchical Bayesian Operator-induced Symbolic Regression Trees for Structural Learning of Scientific Expressions
by: Roy, Somjit, et al.
Published: (2025)

Conditional Score Learning for Quickest Change Detection in Markov Transition Kernels
by: Chen, Wuxia, et al.
Published: (2025)

Contextual Dynamic Pricing: Algorithms, Optimality, and Local Differential Privacy Constraints
by: Zhao, Zifeng, et al.
Published: (2024)

Federated Transfer Learning with Differential Privacy
by: Li, Mengchu, et al.
Published: (2024)

Statistical Inference for Gradient Boosting Regression
by: Fang, Haimo, et al.
Published: (2025)

Statistical Inference under Adaptive Sampling with LinUCB
by: Fan, Wei, et al.
Published: (2025)

A renormalization-group inspired lattice-based framework for piecewise generalized linear models
by: Chang, Joshua C.
Published: (2026)

Debiased Regression for Root-N-Consistent Conditional Mean Estimation
by: Kato, Masahiro
Published: (2024)

Consistent model selection in the spiked Wigner model via AIC-type criteria
by: Mukherjee, Soumendu Sundar
Published: (2023)

The Fundamental Limits of Structure-Agnostic Functional Estimation
by: Balakrishnan, Sivaraman, et al.
Published: (2023)

Intrinsic effective sample size for manifold-valued Markov chain Monte Carlo via kernel discrepancy
by: You, Kisung
Published: (2026)

Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes
by: Lu, Miao, et al.
Published: (2022)

Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation
by: Jin, Jikai, et al.
Published: (2024)

Structural adaptation and rate accelerated estimation in bivariate functional data
by: Kassi, Omar, et al.
Published: (2024)

Optimal and Structure-Adaptive CATE Estimation with Kernel Ridge Regression
by: Kim, Seok-Jin
Published: (2026)

Conditional Predictive Inference for General Structured Data with Group Symmetries
by: Shen, Yichen, et al.
Published: (2026)

Structural Foundations for Leading Digit Laws: Beyond Probabilistic Mixtures
by: Berman, Vladimir
Published: (2025)

Optimal Differentially Private PCA and Estimation for Spiked Covariance Matrices
by: Cai, T. Tony, et al.
Published: (2024)

Enhancing Signal Proportion Estimation Through Leveraging Arbitrary Covariance Structures
by: Bai, Jingtian, et al.
Published: (2025)

Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data
by: Guo, Siyuan, et al.
Published: (2022)

Dendrograms of Mixing Measures for Softmax-Gated Gaussian Mixture of Experts: Consistency without Model Sweeps
by: Hai, Do Tien, et al.
Published: (2025)

Information-Theoretic Limits and Strong Consistency on Binary Non-uniform Hypergraph Stochastic Block Models
by: Wang, Hai-Xiao
Published: (2023)

Structural Effect and Spectral Enhancement of High-Dimensional Regularized Linear Discriminant Analysis
by: Zhang, Yonghan, et al.
Published: (2025)