Copertina

Data-Driven Influence Functions for Optimization-Based Causal Inference

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Jordan, Michael I., Wang, Yixin, Zhou, Angela
Natura:	Preprint
Pubblicazione:	2022
Soggetti:	Methodology Machine Learning Optimization and Control
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Locally Adaptive Multi-Objective Learning
di: Kaur, Jivat Neet, et al.
Pubblicazione: (2026)

Structured Difference-of-Q via Orthogonal Learning
di: Cao, Defu, et al.
Pubblicazione: (2024)

From Contextual Data to Newsvendor Decisions: On the Actual Performance of Data-Driven Algorithms
di: Besbes, Omar, et al.
Pubblicazione: (2023)

Data-Driven Sequential Sampling for Tail Risk Mitigation
di: Ahn, Dohyun, et al.
Pubblicazione: (2025)

Robust Data-driven Prescriptiveness Optimization
di: Poursoltani, Mehran, et al.
Pubblicazione: (2023)

Optimizing Input Data Collection for Ranking and Selection
di: Song, Eunhye, et al.
Pubblicazione: (2025)

Causal Invariance Learning via Efficient Nonconvex Optimization
di: Wang, Zhenyu, et al.
Pubblicazione: (2024)

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

Application-Driven Learning: A Closed-Loop Prediction and Optimization Approach Applied to Dynamic Reserves and Demand Forecasting
di: Garcia, Joaquim Dias, et al.
Pubblicazione: (2021)

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

Bridging Constraints and Stochasticity: A Fully First-Order Method for Stochastic Bilevel Optimization with Linear Constraints
di: Phan, Cac, et al.
Pubblicazione: (2025)

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

AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization
di: Vaswani, Namrata
Pubblicazione: (2025)

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

Learning Sequential Decisions from Multiple Sources via Group-Robust Markov Decision Processes
di: Xu, Mingyuan, et al.
Pubblicazione: (2026)

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

"Over-optimizing" for Normality: Budget-constrained Uncertainty Quantification for Contextual Decision-making
di: Wang, Yanyuan, et al.
Pubblicazione: (2025)

Valid Inference After Causal Discovery
di: Gradu, Paula, et al.
Pubblicazione: (2022)

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

Statistical Performance Guarantee for Subgroup Identification with Generic Machine Learning
di: Li, Michael Lingzhi, et al.
Pubblicazione: (2023)

Arc travel time and path choice model estimation subsumed
di: Mohammadpour, Sobhan, et al.
Pubblicazione: (2022)

The Stochastic Proximal Distance Algorithm
di: Jiang, Haoyu, et al.
Pubblicazione: (2022)

Automatic Outlier Rectification via Optimal Transport
di: Blanchet, Jose, et al.
Pubblicazione: (2024)

Fast Robust Kernel Regression through Sign Gradient Descent with Early Stopping
di: Allerbo, Oskar
Pubblicazione: (2023)

SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems
di: Islam, H M Mohaimanul, et al.
Pubblicazione: (2024)

A distribution-free mixed-integer optimization approach to hierarchical modelling of clustered and longitudinal data
di: Sankaranarayanan, Madhav, et al.
Pubblicazione: (2023)

Random Pareto front surfaces
di: Tu, Ben, et al.
Pubblicazione: (2024)

Optimal Cross-Validation for Sparse Linear Regression
di: Cory-Wright, Ryan, et al.
Pubblicazione: (2023)

Changing the Kernel During Training Leads to Double Descent in Kernel Regression
di: Allerbo, Oskar
Pubblicazione: (2023)

Calibrated Multi-Level Quantile Forecasting
di: Ding, Tiffany, et al.
Pubblicazione: (2025)

Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design
di: Saves, Paul, et al.
Pubblicazione: (2025)

Convergence of projected stochastic natural gradient variational inference for various step size and sample or batch size schedules
di: Guilmeau, Thomas, et al.
Pubblicazione: (2026)

Simultaneous estimation of multiple discrete unimodal distributions under stochastic order constraints
di: Yoshida, Yasuhiro, et al.
Pubblicazione: (2026)

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

An Optimistic Algorithm for Online Convex Optimization with Adversarial Constraints
di: Lekeufack, Jordan, et al.
Pubblicazione: (2024)

BoTier: Multi-Objective Bayesian Optimization with Tiered Composite Objectives
di: Haddadnia, Mohammad, et al.
Pubblicazione: (2025)

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

Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need
di: Yang, Shangda, et al.
Pubblicazione: (2024)

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

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