Cover Image

A Convex Framework for Confounding Robust Inference

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ishikawa, Kei, He, Niao, Kanamori, Takafumi
Format:	Preprint
Published:	2023
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Robust Estimation for Kernel Exponential Families with Smoothed Total Variation Distances
by: Kanamori, Takafumi, et al.
Published: (2024)

Robust Knowledge Transfer in Tiered Reinforcement Learning
by: Huang, Jiawei, et al.
Published: (2023)

Distributional Spectral Diagnostics for Localizing Grokking Transitions
by: Wang, Ziyue, et al.
Published: (2026)

Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization
by: Zhang, Liang, et al.
Published: (2023)

Mixture Proportion Estimation and Weakly-supervised Kernel Test for Conditional Independence
by: Hirose, Yushi, et al.
Published: (2026)

TULiP: Test-time Uncertainty Estimation via Linearization and Weight Perturbation
by: Zhang, Yuhui, et al.
Published: (2025)

Global Solutions to Non-Convex Functional Constrained Problems with Hidden Convexity
by: Fatkhullin, Ilyas, et al.
Published: (2025)

Robust VAEs via Generating Process of Noise Augmented Data
by: Irobe, Hiroo, et al.
Published: (2024)

Scaling-based Data Augmentation for Generative Models and its Theoretical Extension
by: Koike, Yoshitaka, et al.
Published: (2024)

Conformal Counterfactual Inference under Hidden Confounding
by: Chen, Zonghao, et al.
Published: (2024)

GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding
by: Oprescu, Miruna, et al.
Published: (2025)

Implicit Regularization of Sharpness-Aware Minimization for Scale-Invariant Problems
by: Li, Bingcong, et al.
Published: (2024)

Nonparametric Identification and Inference for Counterfactual Distributions with Confounding
by: Sun, Jianle, et al.
Published: (2026)

TiAda: A Time-scale Adaptive Algorithm for Nonconvex Minimax Optimization
by: Li, Xiang, et al.
Published: (2022)

Manifold Generalization Provably Proceeds Memorization in Diffusion Models
by: Shen, Zebang, et al.
Published: (2026)

Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement Learning
by: Yardim, Batuhan, et al.
Published: (2024)

On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation
by: Huang, Jiawei, et al.
Published: (2023)

Primal Methods for Variational Inequality Problems with Functional Constraints
by: Zhang, Liang, et al.
Published: (2024)

From Gradient Clipping to Normalization for Heavy Tailed SGD
by: Hübler, Florian, et al.
Published: (2024)

Linear Convergence of Entropy-Regularized Natural Policy Gradient with Linear Function Approximation
by: Cayci, Semih, et al.
Published: (2021)

Robust Personalized Recommendation under Hidden Confounding in MNAR
by: Li, Zongyu, et al.
Published: (2026)

Scalable Out-of-distribution Robustness in the Presence of Unobserved Confounders
by: Prashant, Parjanya, et al.
Published: (2024)

SALAAD: Sparse And Low-Rank Adaptation via ADMM for Large Language Model Inference
by: Ma, Hao, et al.
Published: (2026)

Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders
by: Luo, Gongxu, et al.
Published: (2025)

Long-term Causal Inference via Modeling Sequential Latent Confounding
by: Chen, Weilin, et al.
Published: (2025)

Causal Inference with Categorical Unobserved Confounder via Mixture Learning
by: Saha, Aytijhya, et al.
Published: (2026)

Robust Causal Analysis of Linear Cyclic Systems With Hidden Confounders
by: Lorbeer, Boris, et al.
Published: (2024)

A Hessian-Aware Stochastic Differential Equation for Modelling SGD
by: Li, Xiang, et al.
Published: (2024)

A Schrödinger Eigenfunction Method for Long-Horizon Stochastic Optimal Control
by: Claeys, Louis, et al.
Published: (2026)

LLM-Driven Treatment Effect Estimation Under Inference Time Text Confounding
by: Ma, Yuchen, et al.
Published: (2025)

Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage Perspective
by: Huang, Jiawei, et al.
Published: (2025)

Steering No-Regret Agents in MFGs under Model Uncertainty
by: Widmer, Leo, et al.
Published: (2025)

When Scores Learn Geometry: Rate Separations under the Manifold Hypothesis
by: Li, Xiang, et al.
Published: (2025)

Online $\mathrm{L}^{\natural}$-Convex Minimization
by: Yokoyama, Ken, et al.
Published: (2024)

Copula-ResLogit: A Deep-Copula Framework for Unobserved Confounding Effects
by: Kamal, Kimia, et al.
Published: (2026)

Multi-level Monte-Carlo Gradient Methods for Stochastic Optimization with Biased Oracles
by: Hu, Yifan, et al.
Published: (2024)

Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning
by: Hu, Yifan, et al.
Published: (2020)

Confounding-Robust Policy Improvement with Human-AI Teams
by: Gao, Ruijiang, et al.
Published: (2023)

Muown: Row-Norm Control for Muon Optimization
by: Lion, Kai, et al.
Published: (2026)

Truly No-Regret Learning in Constrained MDPs
by: Müller, Adrian, et al.
Published: (2024)