Copertina

Causal Invariance Learning via Efficient Nonconvex Optimization

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Wang, Zhenyu, Hu, Yifan, Bühlmann, Peter, Guo, Zijian
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Methodology Machine Learning Optimization and Control Statistics Theory
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Statistical Analysis of Conditional Group Distributionally Robust Optimization with Cross-Entropy Loss
di: Guo, Zijian, et al.
Pubblicazione: (2025)

Robustness Against Weak or Invalid Instruments: Exploring Nonlinear Treatment Models with Machine Learning
di: Guo, Zijian, et al.
Pubblicazione: (2022)

Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning
di: Gu, Yihong, et al.
Pubblicazione: (2024)

Selecting the Best Optimizing System
di: Si, Nian, et al.
Pubblicazione: (2022)

Efficient Online Learning in Interacting Particle Systems
di: Sharrock, Louis, et al.
Pubblicazione: (2026)

Tensor-on-Tensor Regression: Riemannian Optimization, Over-parameterization, Statistical-computational Gap, and Their Interplay
di: Luo, Yuetian, et al.
Pubblicazione: (2022)

Estimating Higher-Order Mixed Memberships via the $\ell_{2,\infty}$ Tensor Perturbation Bound
di: Agterberg, Joshua, et al.
Pubblicazione: (2022)

Beyond Maximum Likelihood: Variational Inequality Estimation for Generalized Linear Models
di: Zhu, Linglingzhi, et al.
Pubblicazione: (2025)

$K$-Nearest-Neighbor Resampling for Off-Policy Evaluation in Stochastic Control
di: Giegrich, Michael, et al.
Pubblicazione: (2023)

Low-Rank Thinning
di: Carrell, Annabelle Michael, et al.
Pubblicazione: (2025)

Fundamental Computational Limits in Pursuing Invariant Causal Prediction and Invariance-Guided Regularization
di: Gu, Yihong, et al.
Pubblicazione: (2025)

Gradient descent inference in empirical risk minimization
di: Han, Qiyang, et al.
Pubblicazione: (2024)

Extrapolation-Aware Nonparametric Statistical Inference
di: Pfister, Niklas, et al.
Pubblicazione: (2024)

StablePCA: Distributionally Robust Learning of Shared Representations from Multi-Source Data
di: Wang, Zhenyu, et al.
Pubblicazione: (2025)

Optimal estimators of cross-partial derivatives and surrogates of functions
di: Lamboni, Matieyendou
Pubblicazione: (2024)

Global Group Fairness in Federated Learning via Function Tracking
di: Rychener, Yves, et al.
Pubblicazione: (2025)

On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models
di: Zhang, Yangjing, et al.
Pubblicazione: (2022)

Treatment Effect Estimation with Observational Network Data using Machine Learning
di: Emmenegger, Corinne, et al.
Pubblicazione: (2022)

Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation
di: Wang, Zhenyu, et al.
Pubblicazione: (2023)

Data-Driven Influence Functions for Optimization-Based Causal Inference
di: Jordan, Michael I., et al.
Pubblicazione: (2022)

On the Uniform Convergence of Subdifferentials in Stochastic Optimization and Learning
di: Ruan, Feng
Pubblicazione: (2024)

A novel sequential method for building upper and lower bounds of moments of distributions
di: Martin, Solal, et al.
Pubblicazione: (2025)

Randomization Inference When N Equals One
di: Liang, Tengyuan, et al.
Pubblicazione: (2023)

Robustly Learning Monotone Generalized Linear Models via Data Augmentation
di: Zarifis, Nikos, et al.
Pubblicazione: (2025)

Optimistic search: Change point estimation for large-scale data via adaptive logarithmic queries
di: Kovács, Solt, et al.
Pubblicazione: (2020)

Learning Large Causal Structures from Inverse Covariance Matrix via Sparse Matrix Decomposition
di: Dong, Shuyu, et al.
Pubblicazione: (2022)

Variational Transport: A Convergent Particle-BasedAlgorithm for Distributional Optimization
di: Yang, Zhuoran, et al.
Pubblicazione: (2020)

Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic
di: Sordello, Matteo, et al.
Pubblicazione: (2019)

Generating Correlation Matrices with Graph Structures Using Convex Optimization
di: Fakhar, Ali, et al.
Pubblicazione: (2025)

DCILP: A Distributed Approach for Large-Scale Causal Structure Learning
di: Dong, Shuyu, et al.
Pubblicazione: (2024)

Multi-environment Invariance Learning with Missing Data
di: Jia, Yiran, et al.
Pubblicazione: (2026)

Optimal transport natural gradient for statistical manifolds with continuous sample space
di: Chen, Yifan, et al.
Pubblicazione: (2018)

Learning the Uncertainty Sets for Control Dynamics via Set Membership: A Non-Asymptotic Analysis
di: Li, Yingying, et al.
Pubblicazione: (2023)

Set Invariance with Probability One for Controlled Diffusion: Score-based Approach
di: Wang, Wenqing, et al.
Pubblicazione: (2025)

Stochastic Optimization with Optimal Importance Sampling
di: Aolaritei, Liviu, et al.
Pubblicazione: (2025)

Federated Optimization of Smooth Loss Functions
di: Jadbabaie, Ali, et al.
Pubblicazione: (2022)

Certified Multi-Fidelity Zeroth-Order Optimization
di: de Montbrun, Étienne, et al.
Pubblicazione: (2023)

Robust Assortment Optimization from Observational Data
di: Lu, Miao, et al.
Pubblicazione: (2026)

Trajectory-Restricted Optimization Conditions and Geometry-Aware Linear Convergence
di: Chaudhry, Faris, et al.
Pubblicazione: (2026)

Online Inference of Constrained Optimization: Primal-Dual Optimality and Sequential Quadratic Programming
di: Gao, Yihang, et al.
Pubblicazione: (2025)