Distribution-free two-sample testing with blurred total variation distance
Fuente:
arXiv
Saved in:
| Main Authors: | Hore, Rohan, Barber, Rina Foygel |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
The Limits of Assumption-free Tests for Algorithm Performance
by: Luo, Yuetian, et al.
Published: (2024)
by: Luo, Yuetian, et al.
Published: (2024)
Bagging Provides Assumption-free Stability
by: Soloff, Jake A., et al.
Published: (2023)
by: Soloff, Jake A., et al.
Published: (2023)
Predictive inference for time series: why is split conformal effective despite temporal dependence?
by: Barber, Rina Foygel, et al.
Published: (2025)
by: Barber, Rina Foygel, et al.
Published: (2025)
Is Algorithmic Stability Testable? A Unified Framework under Computational Constraints
by: Luo, Yuetian, et al.
Published: (2024)
by: Luo, Yuetian, et al.
Published: (2024)
Are all models wrong? Fundamental limits in distribution-free empirical model falsification
by: Müller, Manuel M., et al.
Published: (2025)
by: Müller, Manuel M., et al.
Published: (2025)
Stability and Accuracy Trade-offs in Statistical Estimation
by: Chakraborty, Abhinav, et al.
Published: (2026)
by: Chakraborty, Abhinav, et al.
Published: (2026)
Assumption-free stability for ranking problems
by: Liang, Ruiting, et al.
Published: (2025)
by: Liang, Ruiting, et al.
Published: (2025)
Building a stable classifier with the inflated argmax
by: Soloff, Jake A., et al.
Published: (2024)
by: Soloff, Jake A., et al.
Published: (2024)
Testing conditional independence under isotonicity
by: Hore, Rohan, et al.
Published: (2025)
by: Hore, Rohan, et al.
Published: (2025)
Theoretical Foundations of Conformal Prediction
by: Angelopoulos, Anastasios N., et al.
Published: (2024)
by: Angelopoulos, Anastasios N., et al.
Published: (2024)
Leave a Window Out: Modifying the Jackknife for Predictive Inference in Time Series
by: Jiang, Hanyang, et al.
Published: (2026)
by: Jiang, Hanyang, et al.
Published: (2026)
Distribution-free inference with hierarchical data
by: Lee, Yonghoon, et al.
Published: (2023)
by: Lee, Yonghoon, et al.
Published: (2023)
Algorithmic stability implies training-conditional coverage for distribution-free prediction methods
by: Liang, Ruiting, et al.
Published: (2023)
by: Liang, Ruiting, et al.
Published: (2023)
Hoeffding and Bernstein inequalities for weighted sums of exchangeable random variables
by: Barber, Rina Foygel
Published: (2024)
by: Barber, Rina Foygel
Published: (2024)
Concentration Inequalities for Exchangeable Tensors and Matrix-valued Data
by: Cheng, Chen, et al.
Published: (2026)
by: Cheng, Chen, et al.
Published: (2026)
One-shot Conditional Sampling: MMD meets Nearest Neighbors
by: Chatterjee, Anirban, et al.
Published: (2025)
by: Chatterjee, Anirban, et al.
Published: (2025)
Conformal prediction with local weights: randomization enables local guarantees
by: Hore, Rohan, et al.
Published: (2023)
by: Hore, Rohan, et al.
Published: (2023)
Unifying Different Theories of Conformal Prediction
by: Barber, Rina Foygel, et al.
Published: (2025)
by: Barber, Rina Foygel, et al.
Published: (2025)
False discovery rate control with compound p-values
by: Barber, Rina Foygel, et al.
Published: (2025)
by: Barber, Rina Foygel, et al.
Published: (2025)
Mosaic inference on panel data
by: Spector, Asher, et al.
Published: (2025)
by: Spector, Asher, et al.
Published: (2025)
Stability via resampling: statistical problems beyond the real line
by: Soloff, Jake A., et al.
Published: (2024)
by: Soloff, Jake A., et al.
Published: (2024)
Distribution-free root cause analysis
by: Hore, Rohan, et al.
Published: (2026)
by: Hore, Rohan, et al.
Published: (2026)
False positive control in time series coincidence detection
by: Liang, Ruiting, et al.
Published: (2025)
by: Liang, Ruiting, et al.
Published: (2025)
Distribution free M-estimation
by: Areces, Felipe, et al.
Published: (2025)
by: Areces, Felipe, et al.
Published: (2025)
Distributionally robust risk evaluation with an isotonic constraint
by: Gui, Yu, et al.
Published: (2024)
by: Gui, Yu, et al.
Published: (2024)
A variational approach to dimension-free self-normalized concentration
by: Chugg, Ben, et al.
Published: (2025)
by: Chugg, Ben, et al.
Published: (2025)
High-accuracy and dimension-free sampling with diffusions
by: Gatmiry, Khashayar, et al.
Published: (2026)
by: Gatmiry, Khashayar, et al.
Published: (2026)
Network two-sample test for block models
by: Nguen, Chung Kyong, et al.
Published: (2024)
by: Nguen, Chung Kyong, et al.
Published: (2024)
Joint learning of a network of linear dynamical systems via total variation penalization
by: Donnat, Claire, et al.
Published: (2025)
by: Donnat, Claire, et al.
Published: (2025)
Estimation and inference for the Wasserstein distance between mixing measures in topic models
by: Bing, Xin, et al.
Published: (2022)
by: Bing, Xin, et al.
Published: (2022)
$L^2$ over Wasserstein: Statistical Analysis for Optimal Transport
by: Passeggeri, Riccardo, et al.
Published: (2026)
by: Passeggeri, Riccardo, et al.
Published: (2026)
Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networks
by: Bobbia, Benjamin, et al.
Published: (2024)
by: Bobbia, Benjamin, et al.
Published: (2024)
Adaptive variational Bayes: Optimality, computation and applications
by: Ohn, Ilsang, et al.
Published: (2021)
by: Ohn, Ilsang, et al.
Published: (2021)
A distribution-free valid p-value for finite samples of bounded random variables
by: Alvarez, Joaquin
Published: (2024)
by: Alvarez, Joaquin
Published: (2024)
On the distance between mean and geometric median in high dimensions
by: Schwank, Richard, et al.
Published: (2025)
by: Schwank, Richard, et al.
Published: (2025)
Adaptive sparse variational approximations for Gaussian process regression
by: Nieman, Dennis, et al.
Published: (2025)
by: Nieman, Dennis, et al.
Published: (2025)
Improved learning theory for kernel distribution regression with two-stage sampling
by: Bachoc, François, et al.
Published: (2023)
by: Bachoc, François, et al.
Published: (2023)
Optimal structure learning and conditional independence testing
by: Gao, Ming, et al.
Published: (2025)
by: Gao, Ming, et al.
Published: (2025)
Posterior and variational inference for deep neural networks with heavy-tailed weights
by: Castillo, Ismaël, et al.
Published: (2024)
by: Castillo, Ismaël, et al.
Published: (2024)
Learning Survival Distributions with the Asymmetric Laplace Distribution
by: Sheng, Deming, et al.
Published: (2025)
by: Sheng, Deming, et al.
Published: (2025)
Similar Items
-
The Limits of Assumption-free Tests for Algorithm Performance
by: Luo, Yuetian, et al.
Published: (2024) -
Bagging Provides Assumption-free Stability
by: Soloff, Jake A., et al.
Published: (2023) -
Predictive inference for time series: why is split conformal effective despite temporal dependence?
by: Barber, Rina Foygel, et al.
Published: (2025) -
Is Algorithmic Stability Testable? A Unified Framework under Computational Constraints
by: Luo, Yuetian, et al.
Published: (2024) -
Are all models wrong? Fundamental limits in distribution-free empirical model falsification
by: Müller, Manuel M., et al.
Published: (2025)