Cover Image

When Stronger Triggers Backfire: A High-Dimensional Theory of Backdoor Attacks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Flynn, Donald, Goldhirsh, Hadas Yaron, Keating, Jonathan P., Seroussi, Inbar
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

Minimax Rates for Learning Pairwise Interactions in Attention-Style Models
by: Zucker, Shai, et al.
Published: (2025)

The High Line: Exact Risk and Learning Rate Curves of Stochastic Adaptive Learning Rate Algorithms
by: Collins-Woodfin, Elizabeth, et al.
Published: (2024)

Optimal minimax rate of learning nonlocal interaction kernels
by: Wang, Xiong, et al.
Published: (2023)

Batches Stabilize the Minimum Norm Risk in High Dimensional Overparameterized Linear Regression
by: Ioushua, Shahar Stein, et al.
Published: (2023)

A Linear Approach to Data Poisoning
by: Flynn, Donald, et al.
Published: (2025)

High-Dimensional Importance-Weighted Information Criteria: Theory and Optimality
by: Cao, Yong-Syun, et al.
Published: (2025)

Generalization Bound and New Algorithm for Clean-Label Backdoor Attack
by: Yu, Lijia, et al.
Published: (2024)

Stronger Neyman Regret Guarantees for Adaptive Experimental Design
by: Noarov, Georgy, et al.
Published: (2025)

High-Dimensional Private Linear Regression with Optimal Rates
by: Bombari, Simone, et al.
Published: (2025)

Factor Augmented High-Dimensional SGD
by: Li, Shubo, et al.
Published: (2026)

Navigating Sparsities in High-Dimensional Linear Contextual Bandits
by: Zhao, Rui, et al.
Published: (2025)

Confidence Sequences for Generalized Linear Models via Regret Analysis
by: Clerico, Eugenio, et al.
Published: (2025)

Transfer Learning for Benign Overfitting in High-Dimensional Linear Regression
by: Kim, Yeichan, et al.
Published: (2025)

Optimizing High-Dimensional Oblique Splits
by: Chi, Chien-Ming
Published: (2025)

FLIPHAT: Joint Differential Privacy for High Dimensional Sparse Linear Bandits
by: Chakraborty, Sunrit, et al.
Published: (2024)

Minimax Rate-Optimal Algorithms for High-Dimensional Stochastic Linear Bandits
by: Liu, Jingyu, et al.
Published: (2025)

Sparse Tucker Decomposition and Graph Regularization for High-Dimensional Time Series Forecasting
by: Xia, Sijia, et al.
Published: (2026)

Inferring Change Points in High-Dimensional Regression via Approximate Message Passing
by: Arpino, Gabriel, et al.
Published: (2024)

Resolving Memorization in Empirical Diffusion Model for Manifold Data in High-Dimensional Spaces
by: Lyu, Yang, et al.
Published: (2025)

SOFARI: High-Dimensional Manifold-Based Inference
by: Zheng, Zemin, et al.
Published: (2023)

Entropic Optimal Transport Eigenmaps for Nonlinear Alignment and Joint Embedding of High-Dimensional Datasets
by: Landa, Boris, et al.
Published: (2024)

A Heteroskedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates
by: Fan, Qingliang, et al.
Published: (2022)

Order-Explicit Linearization of High-Dimensional $U$-Statistics
by: Ritzwoller, David M., et al.
Published: (2024)

Hypothesis Testing for High-Dimensional Matrix-Valued Data
by: Cui, Shijie, et al.
Published: (2024)

Adaptive and Stratified Subsampling for High-Dimensional Robust Estimation
by: Mittal, Prateek, et al.
Published: (2024)

High-Dimensional Asymptotics of Differentially Private PCA
by: Yun, Youngjoo, et al.
Published: (2025)

Sparsified-Learning for High-Dimensional Heavy-Tailed Locally Stationary Time Series, Concentration and Oracle Inequalities
by: Wang, Yingjie, et al.
Published: (2025)

TrIM: Transformed Iterative Mondrian Forests for Gradient-based Dimension Reduction and High-Dimensional Regression
by: Baptista, Ricardo, et al.
Published: (2024)

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation
by: Mallory, Matthew Esmaili, et al.
Published: (2025)

Optimal Discriminant Analysis in High-Dimensional Latent Factor Models
by: Bing, Xin, et al.
Published: (2022)

A General Framework for Treatment Effect Estimation in Semi-Supervised and High Dimensional Settings
by: Chakrabortty, Abhishek, et al.
Published: (2022)

Valid Inference for Machine Learning Model Parameters
by: Dey, Neil, et al.
Published: (2023)

A Power Transform
by: Barron, Jonathan T.
Published: (2025)

Sampling from Constrained Gibbs Measures: with Applications to High-Dimensional Bayesian Inference
by: Wang, Ruixiao, et al.
Published: (2026)

Optimal Robust Estimation under Local and Global Corruptions: Stronger Adversary and Smaller Error
by: Pittas, Thanasis, et al.
Published: (2024)

Statistical Inference for Manifold Similarity and Alignability across Noisy High-Dimensional Datasets
by: Chen, Hongrui, et al.
Published: (2025)

Limit Theorems for Stochastic Gradient Descent in High-Dimensional Single-Layer Networks
by: Rangriz, Parsa
Published: (2025)

Structural Effect and Spectral Enhancement of High-Dimensional Regularized Linear Discriminant Analysis
by: Zhang, Yonghan, et al.
Published: (2025)

Lasso Penalization for High-Dimensional Beta Regression Models: Computation, Analysis, and Inference
by: Ramezani, Niloofar, et al.
Published: (2025)

Minimax-Optimal Spectral Clustering with Covariance Projection for High-Dimensional Anisotropic Mixtures
by: Huang, Chengzhu, et al.
Published: (2025)