Cover Image

Incorporating priors in learning: a random matrix study under a teacher-student framework

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Tiomoko, Malik, Schnoor, Ekkehard
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Concept activation vectors: a unifying view and adversarial attacks
by: Schnoor, Ekkehard, et al.
Published: (2025)

$α$-TCAV: A Unified Framework for Testing with Concept Activation Vectors
by: Schnoor, Ekkehard, et al.
Published: (2026)

High-Dimensional Analysis of Bootstrap Ensemble Classifiers
by: Tiomoko, Malik, et al.
Published: (2025)

Random matrix theory improved Fréchet mean of symmetric positive definite matrices
by: Bouchard, Florent, et al.
Published: (2024)

Characterization of Gaussian Universality Breakdown in High-Dimensional Empirical Risk Minimization
by: Yaakoubi, Chiheb, et al.
Published: (2026)

Human in the Loop Adaptive Optimization for Improved Time Series Forecasting
by: Tiomoko, Malik, et al.
Published: (2025)

A Random Matrix Perspective of Echo State Networks: From Precise Bias--Variance Characterization to Optimal Regularization
by: Moakher, Yessin, et al.
Published: (2025)

Generalization in Representation Models via Random Matrix Theory: Application to Recurrent Networks
by: Moakher, Yessin, et al.
Published: (2025)

User-friendly Foundation Model Adapters for Multivariate Time Series Classification
by: Feofanov, Vasilii, et al.
Published: (2024)

Gaussian Process-based learning with new MCMC-based implementation of Wishart prior on correlation matrix
by: Warrior, Kane, et al.
Published: (2026)

Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting
by: Ilbert, Romain, et al.
Published: (2024)

LLMs as In-Context Meta-Learners for Model and Hyperparameter Selection
by: Hili, Youssef Attia El, et al.
Published: (2025)

Transformer learns the cross-task prior and regularization for in-context learning
by: Lu, Fei, et al.
Published: (2025)

Hi-fi functional priors by learning activations
by: Sendera, Marcin, et al.
Published: (2025)

Hopfield model with planted patterns: a teacher-student self-supervised learning model
by: Alemanno, Francesco, et al.
Published: (2023)

Concentration of a sparse Bayesian model with Horseshoe prior in estimating high-dimensional precision matrix
by: Mai, The Tien
Published: (2024)

On the role of memorization in learned priors for geophysical inverse problems
by: Siahkoohi, Ali, et al.
Published: (2026)

On student-teacher deviations in distillation: does it pay to disobey?
by: Nagarajan, Vaishnavh, et al.
Published: (2023)

Mantis: Lightweight Calibrated Foundation Model for User-Friendly Time Series Classification
by: Feofanov, Vasilii, et al.
Published: (2025)

Factor-MCLS: Multi-agent learning system with reward factor matrix and multi-critic framework for dynamic portfolio optimization
by: Sun, Ruoyu, et al.
Published: (2025)

Apprenticeship learning with prior beliefs using inverse optimization
by: Junca, Mauricio, et al.
Published: (2025)

RTNinja: A generalized machine learning framework for analyzing random telegraph noise signals in nanoelectronic devices
by: Varanasi, Anirudh, et al.
Published: (2025)

Active teacher selection for reward learning
by: Freedman, Rachel, et al.
Published: (2023)

Online federated learning framework for classification
by: Guo, Wenxing, et al.
Published: (2025)

Joint auto-encoders: a flexible multi-task learning framework
by: Epstein, Baruch, et al.
Published: (2017)

MLPrE -- A tool for preprocessing and exploratory data analysis prior to machine learning model construction
by: Maxwell, David S, et al.
Published: (2025)

Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts
by: van der Laan, Lars, et al.
Published: (2024)

Dyson Brownian motion and random matrix dynamics of weight matrices during learning
by: Aarts, Gert, et al.
Published: (2024)

Mitigating covariate shift in non-colocated data with learned parameter priors
by: Khan, Behraj, et al.
Published: (2024)

Neural Koopman prior for data assimilation
by: Frion, Anthony, et al.
Published: (2023)

On learning functions over biological sequence space: relating Gaussian process priors, regularization, and gauge fixing
by: Petti, Samantha, et al.
Published: (2025)

An active learning framework for multi-group mean estimation
by: Aznag, Abdellah, et al.
Published: (2025)

A transfer learning framework for weak-to-strong generalization
by: Somerstep, Seamus, et al.
Published: (2024)

A generalizable framework for low-rank tensor completion with numerical priors
by: Yuan, Shiran, et al.
Published: (2023)

Generative emulation of chaotic dynamics with coherent prior
by: Nathaniel, Juan, et al.
Published: (2025)

A duality framework for analyzing random feature and two-layer neural networks
by: Chen, Hongrui, et al.
Published: (2023)

A general framework for deep learning
by: Kengne, William, et al.
Published: (2025)

Propagating the prior from far to near offset: A self-supervised diffusion framework for progressively recovering near-offsets of towed-streamer data
by: Cheng, Shijun, et al.
Published: (2026)

A projection-based framework for gradient-free and parallel learning
by: Bergmeister, Andreas, et al.
Published: (2025)

DURENDAL: Graph deep learning framework for temporal heterogeneous networks
by: Dileo, Manuel, et al.
Published: (2023)