Learning single-index models via harmonic decomposition
Fuente:
arXiv
Saved in:
| Main Authors: | Joshi, Nirmit, Koubbi, Hugo, Misiakiewicz, Theodor, Srebro, Nathan |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries
by: Joshi, Nirmit, et al.
Published: (2024)
by: Joshi, Nirmit, et al.
Published: (2024)
Statistical-Computational Trade-offs in Learning Multi-Index Models via Harmonic Analysis
by: Latourelle-Vigeant, Hugo, et al.
Published: (2026)
by: Latourelle-Vigeant, Hugo, et al.
Published: (2026)
A non-asymptotic theory of Kernel Ridge Regression: deterministic equivalents, test error, and GCV estimator
by: Misiakiewicz, Theodor, et al.
Published: (2024)
by: Misiakiewicz, Theodor, et al.
Published: (2024)
Asymptotics of Random Feature Regression Beyond the Linear Scaling Regime
by: Hu, Hong, et al.
Published: (2024)
by: Hu, Hong, et al.
Published: (2024)
A Theory of Learning with Autoregressive Chain of Thought
by: Joshi, Nirmit, et al.
Published: (2025)
by: Joshi, Nirmit, et al.
Published: (2025)
An Optimized Franz-Parisi Criterion and its Equivalence with SQ Lower Bounds
by: Chen, Siyu, et al.
Published: (2025)
by: Chen, Siyu, et al.
Published: (2025)
Noisy Interpolation Learning with Shallow Univariate ReLU Networks
by: Joshi, Nirmit, et al.
Published: (2023)
by: Joshi, Nirmit, et al.
Published: (2023)
When does Gaussian equivalence fail and how to fix it: Non-universal behavior of random features with quadratic scaling
by: Wen, Garrett G., et al.
Published: (2025)
by: Wen, Garrett G., et al.
Published: (2025)
Learning single index model with gradient descent: spectral initialization and precise asymptotics
by: Chen, Yuchen, et al.
Published: (2025)
by: Chen, Yuchen, et al.
Published: (2025)
Gradient descent for deep equilibrium single-index models
by: Dandapanthula, Sanjit, et al.
Published: (2025)
by: Dandapanthula, Sanjit, et al.
Published: (2025)
Observable adjustments in single-index models for regularized M-estimators
by: Bellec, Pierre C
Published: (2022)
by: Bellec, Pierre C
Published: (2022)
Positive Distribution Shift as a Framework for Understanding Tractable Learning
by: Medvedev, Marko, et al.
Published: (2026)
by: Medvedev, Marko, et al.
Published: (2026)
Learning to Think from Multiple Thinkers
by: Joshi, Nirmit, et al.
Published: (2026)
by: Joshi, Nirmit, et al.
Published: (2026)
Streaming data recovery via Bayesian tensor train decomposition
by: Huang, Yunyu, et al.
Published: (2023)
by: Huang, Yunyu, et al.
Published: (2023)
Learning to Answer from Correct Demonstrations
by: Joshi, Nirmit, et al.
Published: (2025)
by: Joshi, Nirmit, et al.
Published: (2025)
The Central Role of the Loss Function in Reinforcement Learning
by: Wang, Kaiwen, et al.
Published: (2024)
by: Wang, Kaiwen, et al.
Published: (2024)
Sparse PCA: Phase Transitions in the Critical Regime
by: Feldman, Michael J., et al.
Published: (2024)
by: Feldman, Michael J., et al.
Published: (2024)
Average Gradient Outer Product in kernel regression provably recovers the central subspace for multi-index models
by: Zhu, Libin, et al.
Published: (2026)
by: Zhu, Libin, et al.
Published: (2026)
Efficient Inference for Inverse Reinforcement Learning and Dynamic Discrete Choice Models
by: van der Laan, Lars, et al.
Published: (2025)
by: van der Laan, Lars, et al.
Published: (2025)
Robust Learning of Multi-index Models via Iterative Subspace Approximation
by: Diakonikolas, Ilias, et al.
Published: (2025)
by: Diakonikolas, Ilias, et al.
Published: (2025)
Hoeffding decomposition of black-box models with dependent inputs
by: Idrissi, Marouane Il, et al.
Published: (2023)
by: Idrissi, Marouane Il, et al.
Published: (2023)
Adaptive finite element type decomposition of Gaussian processes
by: Kim, Jaehoan, et al.
Published: (2025)
by: Kim, Jaehoan, et al.
Published: (2025)
Fast kernel methods: Sobolev, physics-informed, and additive models
by: Doumèche, Nathan, et al.
Published: (2025)
by: Doumèche, Nathan, et al.
Published: (2025)
Pseudo-Labeling for Unsupervised Domain Adaptation with Kernel GLMs
by: Weill, Nathan, et al.
Published: (2026)
by: Weill, Nathan, et al.
Published: (2026)
Risk-Controlled Post-Processing of Decision Policies
by: Joshi, Sunay, et al.
Published: (2026)
by: Joshi, Sunay, et al.
Published: (2026)
Gaussian random field approximation via Stein's method with applications to wide random neural networks
by: Balasubramanian, Krishnakumar, et al.
Published: (2023)
by: Balasubramanian, Krishnakumar, et al.
Published: (2023)
A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities
by: Dandi, Yatin, et al.
Published: (2024)
by: Dandi, Yatin, et al.
Published: (2024)
Active Learning via Regression Beyond Realizability
by: Ganju, Atul, et al.
Published: (2025)
by: Ganju, Atul, et al.
Published: (2025)
Multitask Learning and Bandits via Robust Statistics
by: Xu, Kan, et al.
Published: (2021)
by: Xu, Kan, et al.
Published: (2021)
Clustered Switchback Designs for Experimentation Under Spatio-temporal Interference
by: Jia, Su, et al.
Published: (2023)
by: Jia, Su, et al.
Published: (2023)
Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands
by: van der Laan, Lars, et al.
Published: (2025)
by: van der Laan, Lars, et al.
Published: (2025)
Provable Sample-Efficient Transfer Learning Conditional Diffusion Models via Representation Learning
by: Cheng, Ziheng, et al.
Published: (2025)
by: Cheng, Ziheng, et al.
Published: (2025)
Constraint-based causal discovery with tiered background knowledge and latent variables in single or overlapping datasets
by: Bang, Christine W., et al.
Published: (2025)
by: Bang, Christine W., et al.
Published: (2025)
Physics-informed kernel learning
by: Doumèche, Nathan, et al.
Published: (2024)
by: Doumèche, Nathan, et al.
Published: (2024)
Finite-sample performance of the maximum likelihood estimator in logistic regression
by: Chardon, Hugo, et al.
Published: (2024)
by: Chardon, Hugo, et al.
Published: (2024)
Seeded graph matching for the correlated Gaussian Wigner model via the projected power method
by: Araya, Ernesto, et al.
Published: (2022)
by: Araya, Ernesto, et al.
Published: (2022)
Finite-Dimensional Gaussian Approximation for Deep Neural Networks: Universality in Random Weights
by: Balasubramanian, Krishnakumar, et al.
Published: (2025)
by: Balasubramanian, Krishnakumar, et al.
Published: (2025)
A Statistical Theory of Contrastive Learning via Approximate Sufficient Statistics
by: Lin, Licong, et al.
Published: (2025)
by: Lin, Licong, et al.
Published: (2025)
Interactive Learning of Single-Index Models via Stochastic Gradient Descent
by: Rajaraman, Nived, et al.
Published: (2026)
by: Rajaraman, Nived, et al.
Published: (2026)
A Unified View on Learning Unnormalized Distributions via Noise-Contrastive Estimation
by: Ryu, J. Jon, et al.
Published: (2024)
by: Ryu, J. Jon, et al.
Published: (2024)
Similar Items
-
On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries
by: Joshi, Nirmit, et al.
Published: (2024) -
Statistical-Computational Trade-offs in Learning Multi-Index Models via Harmonic Analysis
by: Latourelle-Vigeant, Hugo, et al.
Published: (2026) -
A non-asymptotic theory of Kernel Ridge Regression: deterministic equivalents, test error, and GCV estimator
by: Misiakiewicz, Theodor, et al.
Published: (2024) -
Asymptotics of Random Feature Regression Beyond the Linear Scaling Regime
by: Hu, Hong, et al.
Published: (2024) -
A Theory of Learning with Autoregressive Chain of Thought
by: Joshi, Nirmit, et al.
Published: (2025)