Image de couverture de livre

Outcome-Aware Spectral Feature Learning for Instrumental Variable Regression

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Meunier, Dimitri, Wornbard, Jakub, Kostic, Vladimir R., Moulin, Antoine, Fröhlich, Alek, Lounici, Karim, Pontil, Massimiliano, Gretton, Arthur
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Demystifying Spectral Feature Learning for Instrumental Variable Regression
par: Meunier, Dimitri, et autres
Publié: (2025)

Toward Scalable and Valid Conditional Independence Testing with Spectral Representations
par: Frohlich, Alek, et autres
Publié: (2025)

Equivariant Representation Learning for Symmetry-Aware Inference with Guarantees
par: Ordoñez-Apraez, Daniel, et autres
Publié: (2025)

Toeplitz Based Spectral Methods for Data-driven Dynamical Systems
par: Kostic, Vladimir R., et autres
Publié: (2026)

Semiparametrically Efficient Inference for Kernel Measures of Noise Heterogeneity
par: Wornbard, Jakub, et autres
Publié: (2026)

Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression
par: Kim, Juno, et autres
Publié: (2025)

Learning the Infinitesimal Generator of Stochastic Diffusion Processes
par: Kostic, Vladimir R., et autres
Publié: (2024)

Learning invariant representations of time-homogeneous stochastic dynamical systems
par: Kostic, Vladimir R., et autres
Publié: (2023)

Nonparametric Instrumental Variable Regression with Observed Covariates
par: Shen, Zikai, et autres
Publié: (2025)

Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups
par: Kostic, Vladimir R., et autres
Publié: (2024)

Nonparametric Instrumental Regression via Kernel Methods is Minimax Optimal
par: Meunier, Dimitri, et autres
Publié: (2024)

Neural Conditional Probability for Uncertainty Quantification
par: Kostic, Vladimir R., et autres
Publié: (2024)

A Spectral-Grassmann Wasserstein metric for operator representations of dynamical systems
par: Germain, Thibaut, et autres
Publié: (2025)

A conversion theorem and minimax optimality for continuum contextual bandits
par: Akhavan, Arya, et autres
Publié: (2024)

Nonparametric Instrumental Variable Analysis Without Structural Equations: Debiased Inference on Functionals of Inverse Problems with No Solutions
par: Shen, Zikai, et autres
Publié: (2026)

Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms
par: Meunier, Dimitri, et autres
Publié: (2024)

VertCoHiRF: Decentralized Vertical Clustering Beyond k-means
par: Belucci, Bruno, et autres
Publié: (2026)

CoHiRF: Hierarchical Consensus for Interpretable Clustering Beyond Scalability Limits
par: Meziani, Katia, et autres
Publié: (2025)

Nonlinear Meta-Learning Can Guarantee Faster Rates
par: Meunier, Dimitri, et autres
Publié: (2023)

From Biased to Unbiased Dynamics: An Infinitesimal Generator Approach
par: Devergne, Timothée, et autres
Publié: (2024)

A randomized algorithm to solve reduced rank operator regression
par: Turri, Giacomo, et autres
Publié: (2023)

The seeds of the future are in the present: A blind exploration of metastable states
par: Devergne, Timothée, et autres
Publié: (2025)

Geometric Dictionary Learning of Dynamical Systems with Optimal Transport
par: Germain, Thibaut, et autres
Publié: (2026)

kooplearn: A Scikit-Learn Compatible Library of Algorithms for Evolution Operator Learning
par: Turri, Giacomo, et autres
Publié: (2025)

An Empirical Bernstein Inequality for Dependent Data in Hilbert Spaces and Applications
par: Mirzaei, Erfan, et autres
Publié: (2025)

Spectral Representation for Causal Estimation with Hidden Confounders
par: Sun, Haotian, et autres
Publié: (2024)

Regularized least squares learning with heavy-tailed noise is minimax optimal
par: Mollenhauer, Mattes, et autres
Publié: (2025)

Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm
par: Li, Zhu, et autres
Publié: (2023)

Towards a Unified Analysis of Neural Networks in Nonparametric Instrumental Variable Regression: Optimization and Generalization
par: Chen, Zonghao, et autres
Publié: (2025)

Density Ratio-Free Doubly Robust Proxy Causal Learning
par: Bozkurt, Bariscan, et autres
Publié: (2025)

Density Ratio-based Proxy Causal Learning Without Density Ratios
par: Bozkurt, Bariscan, et autres
Publié: (2025)

Doubly Robust Proxy Causal Learning with Neural Mean Embeddings
par: Bozkurt, Bariscan, et autres
Publié: (2026)

Homological Lagrangian monodromy for some monotone tori
par: Augustynowicz, Marcin, et autres
Publié: (2022)

Geometry-Aware Instrumental Variable Regression
par: Kremer, Heiner, et autres
Publié: (2024)

Dynamics Harmonic Analysis of Robotic Systems: Application in Data-Driven Koopman Modelling
par: Ordoñez-Apraez, Daniel, et autres
Publié: (2023)

Multi-Source and Test-Time Domain Adaptation on Multivariate Signals using Spatio-Temporal Monge Alignment
par: Gnassounou, Théo, et autres
Publié: (2024)

AdaCap: An Adaptive Contrastive Approach for Small-Data Neural Networks
par: Belucci, Bruno, et autres
Publié: (2025)

Possibilistic Instrumental Variable Regression
par: Steiner, Gregor, et autres
Publié: (2025)

QuantFormer: Learning to Quantize for Neural Activity Forecasting in Mouse Visual Cortex
par: Calcagno, Salvatore, et autres
Publié: (2024)

Operator World Models for Reinforcement Learning
par: Novelli, Pietro, et autres
Publié: (2024)