Cover Image

Hard labels sampled from sparse targets mislead rotation invariant algorithms

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ghosh, Avrajit, Yu, Bin, Warmuth, Manfred, Bartlett, Peter
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Statistics Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Noise misleads rotation invariant algorithms on sparse targets
by: Warmuth, Manfred K., et al.
Published: (2024)

A Statistical Analysis for Supervised Deep Learning with Exponential Families for Intrinsically Low-dimensional Data
by: Chakraborty, Saptarshi, et al.
Published: (2024)

A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data
by: Chakraborty, Saptarshi, et al.
Published: (2024)

Theoretical limits of descending $\ell_0$ sparse-regression ML algorithms
by: Stojnic, Mihailo
Published: (2024)

Improved Scaling Laws in Linear Regression via Data Reuse
by: Lin, Licong, et al.
Published: (2025)

Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data
by: Frei, Spencer, et al.
Published: (2022)

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts
by: Ha, Wooseok, et al.
Published: (2025)

A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional Data
by: Chakraborty, Saptarshi, et al.
Published: (2024)

On the Statistical Properties of Generative Adversarial Models for Low Intrinsic Data Dimension
by: Chakraborty, Saptarshi, et al.
Published: (2024)

Adaptive sparse variational approximations for Gaussian process regression
by: Nieman, Dennis, et al.
Published: (2025)

Misclassification bounds for PAC-Bayesian sparse deep learning
by: Mai, The Tien
Published: (2024)

Generalization Properties of Score-matching Diffusion Models for Intrinsically Low-dimensional Data
by: Chakraborty, Saptarshi, et al.
Published: (2026)

Classification by sparse generalized additive models
by: Abramovich, Felix
Published: (2022)

A sparse PAC-Bayesian approach for high-dimensional quantile prediction
by: Mai, The Tien
Published: (2024)

Effective regions and kernels in continuous sparse regularisation, with application to sketched mixtures
by: De Castro, Yohann, et al.
Published: (2025)

Gaussian Mixture Model with unknown diagonal covariances via continuous sparse regularization
by: Giard, Romane, et al.
Published: (2025)

Adaptive conformal classification with noisy labels
by: Sesia, Matteo, 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)

Adaptive posterior concentration rates for sparse high-dimensional linear regression with random design and unknown error variance
by: Mai, The Tien
Published: (2024)

Optimal sparse phase retrieval via a quasi-Bayesian approach
by: Mai, The Tien
Published: (2025)

High-dimensional prediction for count response via sparse exponential weights
by: Mai, The Tien
Published: (2024)

On Linear Separability under Linear Compression with Applications to Hard Support Vector Machine
by: McVay, Paul, et al.
Published: (2022)

Model-agnostic information transfer and fusion for classification with label noise
by: Guojun, Zhu, et al.
Published: (2026)

A general framework for inference on algorithm-agnostic variable importance
by: Williamson, Brian D., et al.
Published: (2020)

A Full Adagrad algorithm with O(Nd) operations
by: Godichon-Baggioni, Antoine, et al.
Published: (2024)

Optimal and instance-dependent guarantees for Markovian linear stochastic approximation
by: Mou, Wenlong, et al.
Published: (2021)

Scaling Laws in Linear Regression: Compute, Parameters, and Data
by: Lin, Licong, et al.
Published: (2024)

Iteratively reweighted kernel machines efficiently learn sparse functions
by: Zhu, Libin, et al.
Published: (2025)

Flow-based generative models as iterative algorithms in probability space
by: Xie, Yao, et al.
Published: (2025)

How many labelers do you have? A closer look at gold-standard labels
by: Cheng, Chen, et al.
Published: (2022)

Cloning is as Hard as Learning for Stabilizer States
by: Bansal, Nikhil, et al.
Published: (2026)

New affine invariant ensemble samplers and their dimensional scaling
by: Chen, Yifan
Published: (2025)

Improved Hardness Results for Learning Intersections of Halfspaces
by: Tiegel, Stefan
Published: (2024)

A fast score-based search algorithm for maximal ancestral graphs using entropy
by: Hu, Zhongyi, et al.
Published: (2024)

Analysis of a multi-target linear shrinkage covariance estimator
by: Oriol, Benoit
Published: (2024)

Robust SVD Made Easy: A fast and reliable algorithm for large-scale data analysis
by: Han, Sangil, et al.
Published: (2024)

UCB algorithms for multi-armed bandits: Precise regret and adaptive inference
by: Han, Qiyang, et al.
Published: (2024)

It's Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation
by: Jin, Jikai, et al.
Published: (2025)

On the Hardness of Learning One Hidden Layer Neural Networks
by: Li, Shuchen, et al.
Published: (2024)

Asymptotic breakdown point analysis of the minimum density power divergence estimator under independent non-homogeneous setups
by: Jana, Suryasis, et al.
Published: (2025)