Information Hidden in Gradients of Regression with Target Noise
Fuente:
arXiv
Saved in:
| Main Authors: | Jamshidi, Arash, Haitsiukevich, Katsiaryna, Puolamäki, Kai |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
GRADSTOP: Early Stopping of Gradient Descent via Posterior Sampling
by: Jamshidi, Arash, et al.
Published: (2025)
by: Jamshidi, Arash, et al.
Published: (2025)
Learning Trajectories of Hamiltonian Systems with Neural Networks
by: Haitsiukevich, Katsiaryna, et al.
Published: (2022)
by: Haitsiukevich, Katsiaryna, et al.
Published: (2022)
Improved Training of Physics-Informed Neural Networks with Model Ensembles
by: Haitsiukevich, Katsiaryna, et al.
Published: (2022)
by: Haitsiukevich, Katsiaryna, et al.
Published: (2022)
In-Context Symbolic Regression: Leveraging Large Language Models for Function Discovery
by: Merler, Matteo, et al.
Published: (2024)
by: Merler, Matteo, et al.
Published: (2024)
PhiPlot: A Web-Based Interactive EDA Environment for Atmospherically Relevant Molecules
by: Loukojärvi, Matias, et al.
Published: (2026)
by: Loukojärvi, Matias, et al.
Published: (2026)
Diffusion models as probabilistic neural operators for recovering unobserved states of dynamical systems
by: Haitsiukevich, Katsiaryna, et al.
Published: (2024)
by: Haitsiukevich, Katsiaryna, et al.
Published: (2024)
Gradient Boosting Mapping for Dimensionality Reduction and Feature Extraction
by: Patron, Anri, et al.
Published: (2024)
by: Patron, Anri, et al.
Published: (2024)
ExplainReduce: Generating global explanations from many local explanations
by: Seppäläinen, Lauri, et al.
Published: (2025)
by: Seppäläinen, Lauri, et al.
Published: (2025)
Sample Complexity of Nonparametric Closeness Testing for Continuous Distributions and Its Application to Causal Discovery with Hidden Confounding
by: Jamshidi, Fateme, et al.
Published: (2025)
by: Jamshidi, Fateme, et al.
Published: (2025)
Fast and Interpretable Machine Learning Modelling of Atmospheric Molecular Clusters
by: Seppäläinen, Lauri, et al.
Published: (2025)
by: Seppäläinen, Lauri, et al.
Published: (2025)
Using Slisemap to interpret physical data
by: Seppäläinen, Lauri, et al.
Published: (2023)
by: Seppäläinen, Lauri, et al.
Published: (2023)
A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression
by: Jamshidi, Ainaz, et al.
Published: (2025)
by: Jamshidi, Ainaz, et al.
Published: (2025)
Information-Theoretic Generalization Bounds for Stochastic Gradient Descent with Predictable Virtual Noise
by: Partohaghighi, Mohammad
Published: (2026)
by: Partohaghighi, Mohammad
Published: (2026)
Sharp Bounds for Poly-GNNs and the Effect of Graph Noise
by: Vinas, Luciano, et al.
Published: (2024)
by: Vinas, Luciano, et al.
Published: (2024)
On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing
by: Behboodi, Arash, et al.
Published: (2024)
by: Behboodi, Arash, et al.
Published: (2024)
Non-geodesically-convex optimization in the Wasserstein space
by: Luu, Hoang Phuc Hau, et al.
Published: (2024)
by: Luu, Hoang Phuc Hau, et al.
Published: (2024)
Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise
by: Pouplin, Thomas, et al.
Published: (2024)
by: Pouplin, Thomas, et al.
Published: (2024)
Stochastic Gradient Descent for Nonparametric Additive Regression
by: Chen, Xin, et al.
Published: (2024)
by: Chen, Xin, et al.
Published: (2024)
NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models
by: Li, Zeming, et al.
Published: (2025)
by: Li, Zeming, et al.
Published: (2025)
Efficient Conformal Prediction for Regression Models under Label Noise
by: Cohen, Yahav, et al.
Published: (2025)
by: Cohen, Yahav, et al.
Published: (2025)
Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning
by: Sun, Chenglu, et al.
Published: (2025)
by: Sun, Chenglu, et al.
Published: (2025)
Learning Curves of Stochastic Gradient Descent in Kernel Regression
by: Zhang, Haihan, et al.
Published: (2025)
by: Zhang, Haihan, et al.
Published: (2025)
Spectrum: Targeted Training on Signal to Noise Ratio
by: Hartford, Eric, et al.
Published: (2024)
by: Hartford, Eric, et al.
Published: (2024)
RieszBoost: Gradient Boosting for Riesz Regression
by: Lee, Kaitlyn J., et al.
Published: (2025)
by: Lee, Kaitlyn J., et al.
Published: (2025)
On Uniform Error Bounds for Kernel Regression under Non-Gaussian Noise
by: Teutsch, Johannes, et al.
Published: (2026)
by: Teutsch, Johannes, et al.
Published: (2026)
Noise-Augmented $\ell_0$ Regularization of Tensor Regression with Tucker Decomposition
by: Yan, Tian, et al.
Published: (2023)
by: Yan, Tian, et al.
Published: (2023)
Understanding Robust Machine Learning for Nonparametric Regression with Heavy-Tailed Noise
by: Feng, Yunlong, et al.
Published: (2025)
by: Feng, Yunlong, et al.
Published: (2025)
Confounded Budgeted Causal Bandits
by: Jamshidi, Fateme, et al.
Published: (2024)
by: Jamshidi, Fateme, et al.
Published: (2024)
Graph-Dependent Regret Bounds in Multi-Armed Bandits with Interference
by: Jamshidi, Fateme, et al.
Published: (2025)
by: Jamshidi, Fateme, et al.
Published: (2025)
Large Stepsizes Accelerate Gradient Descent for Regularized Logistic Regression
by: Wu, Jingfeng, et al.
Published: (2025)
by: Wu, Jingfeng, et al.
Published: (2025)
Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression
by: Wu, Jingfeng, et al.
Published: (2025)
by: Wu, Jingfeng, et al.
Published: (2025)
Nonparametric Instrumental Variable Regression through Stochastic Approximate Gradients
by: Fonseca, Yuri, et al.
Published: (2024)
by: Fonseca, Yuri, et al.
Published: (2024)
A Theoretical Analysis of Noise Geometry in Stochastic Gradient Descent
by: Wang, Mingze, et al.
Published: (2023)
by: Wang, Mingze, et al.
Published: (2023)
Robust Finite-Memory Policy Gradients for Hidden-Model POMDPs
by: Galesloot, Maris F. L., et al.
Published: (2025)
by: Galesloot, Maris F. L., et al.
Published: (2025)
Targeted Unlearning with Single Layer Unlearning Gradient
by: Cai, Zikui, et al.
Published: (2024)
by: Cai, Zikui, et al.
Published: (2024)
Gradient Boosting for Spatial Regression Models with Autoregressive Disturbances
by: Balzer, Michael
Published: (2025)
by: Balzer, Michael
Published: (2025)
Statistical Inference for Gradient Boosting Regression
by: Fang, Haimo, et al.
Published: (2025)
by: Fang, Haimo, et al.
Published: (2025)
Gradient Aligned Regression via Pairwise Losses
by: Zhu, Dixian, et al.
Published: (2024)
by: Zhu, Dixian, et al.
Published: (2024)
Trained Mamba Emulates Online Gradient Descent in In-Context Linear Regression
by: Jiang, Jiarui, et al.
Published: (2025)
by: Jiang, Jiarui, et al.
Published: (2025)
Scaling Law for Stochastic Gradient Descent in Quadratically Parameterized Linear Regression
by: Ding, Shihong, et al.
Published: (2025)
by: Ding, Shihong, et al.
Published: (2025)
Similar Items
-
GRADSTOP: Early Stopping of Gradient Descent via Posterior Sampling
by: Jamshidi, Arash, et al.
Published: (2025) -
Learning Trajectories of Hamiltonian Systems with Neural Networks
by: Haitsiukevich, Katsiaryna, et al.
Published: (2022) -
Improved Training of Physics-Informed Neural Networks with Model Ensembles
by: Haitsiukevich, Katsiaryna, et al.
Published: (2022) -
In-Context Symbolic Regression: Leveraging Large Language Models for Function Discovery
by: Merler, Matteo, et al.
Published: (2024) -
PhiPlot: A Web-Based Interactive EDA Environment for Atmospherically Relevant Molecules
by: Loukojärvi, Matias, et al.
Published: (2026)