The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks
Fuente:
arXiv
Saved in:
| Main Authors: | Liu, Ziquan, Cui, Yufei, Yan, Yan, Xu, Yi, Ji, Xiangyang, Liu, Xue, Chan, Antoni B. |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction
by: Chen, Danhui, et al.
Published: (2025)
by: Chen, Danhui, et al.
Published: (2025)
Cost-Sensitive Conformal Training with Provably Controllable Learning Bounds
by: Jia, Xuesong, et al.
Published: (2025)
by: Jia, Xuesong, et al.
Published: (2025)
Machine Learning on Dynamic Functional Connectivity: Promise, Pitfalls, and Interpretations
by: Ding, Jiaqi, et al.
Published: (2024)
by: Ding, Jiaqi, et al.
Published: (2024)
Locally Adaptive Conformal Inference for Operator Models
by: Harris, Trevor, et al.
Published: (2025)
by: Harris, Trevor, et al.
Published: (2025)
Ensuring Calibration Robustness in Split Conformal Prediction Under Adversarial Attacks
by: Qian, Xunlei, et al.
Published: (2025)
by: Qian, Xunlei, et al.
Published: (2025)
Multi-Source Conformal Inference Under Distribution Shift
by: Liu, Yi, et al.
Published: (2024)
by: Liu, Yi, et al.
Published: (2024)
Stochastic Weakly Convex Optimization Under Heavy-Tailed Noises
by: Zhu, Tianxi, et al.
Published: (2025)
by: Zhu, Tianxi, et al.
Published: (2025)
Response to Promises and Pitfalls of Deep Kernel Learning
by: Wilson, Andrew Gordon, et al.
Published: (2025)
by: Wilson, Andrew Gordon, et al.
Published: (2025)
PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks
by: Feng, Chen, et al.
Published: (2024)
by: Feng, Chen, et al.
Published: (2024)
Promises and Pitfalls of Threshold-based Auto-labeling
by: Vishwakarma, Harit, et al.
Published: (2022)
by: Vishwakarma, Harit, et al.
Published: (2022)
Inference Attacks: A Taxonomy, Survey, and Promising Directions
by: Wu, Feng, et al.
Published: (2024)
by: Wu, Feng, et al.
Published: (2024)
Robust Fast Adaptation from Adversarially Explicit Task Distribution Generation
by: Wang, Cheems, et al.
Published: (2024)
by: Wang, Cheems, et al.
Published: (2024)
Flow-Matching Based Refiner for Molecular Conformer Generation
by: Xu, Xiangyang, et al.
Published: (2025)
by: Xu, Xiangyang, et al.
Published: (2025)
Evaluating the Promise and Pitfalls of LLMs in Hiring Decisions
by: Anzenberg, Eitan, et al.
Published: (2025)
by: Anzenberg, Eitan, et al.
Published: (2025)
Adversarial Bias: Data Poisoning Attacks on Fairness
by: Chan, Eunice, et al.
Published: (2025)
by: Chan, Eunice, et al.
Published: (2025)
Disttack: Graph Adversarial Attacks Toward Distributed GNN Training
by: Zhang, Yuxiang, et al.
Published: (2024)
by: Zhang, Yuxiang, et al.
Published: (2024)
Online Conformal Abstention for Factuality Control Under Adversarial Bandit Feedback
by: Lee, Minjae, et al.
Published: (2025)
by: Lee, Minjae, et al.
Published: (2025)
Generative Synthetic Data for Causal Inference: Pitfalls, Remedies, and Opportunities
by: Xu, Yichen
Published: (2026)
by: Xu, Yichen
Published: (2026)
Pitfalls of Conformal Predictions for Medical Image Classification
by: Mehrtens, Hendrik, et al.
Published: (2025)
by: Mehrtens, Hendrik, et al.
Published: (2025)
Deep Learning Inference on Heterogeneous Mobile Processors: Potentials and Pitfalls
by: Liu, Sicong, et al.
Published: (2024)
by: Liu, Sicong, et al.
Published: (2024)
Sampling-aware Adversarial Attacks Against Large Language Models
by: Beyer, Tim, et al.
Published: (2025)
by: Beyer, Tim, et al.
Published: (2025)
Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions
by: Fuhr, Jonathan, et al.
Published: (2024)
by: Fuhr, Jonathan, et al.
Published: (2024)
Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines
by: Li, Yuchen, et al.
Published: (2024)
by: Li, Yuchen, et al.
Published: (2024)
Adaptive Conformal Inference by Betting
by: Podkopaev, Aleksandr, et al.
Published: (2024)
by: Podkopaev, Aleksandr, et al.
Published: (2024)
Similarity-based Label Inference Attack against Training and Inference of Split Learning
by: Liu, Junlin, et al.
Published: (2022)
by: Liu, Junlin, et al.
Published: (2022)
MF-CLIP: Leveraging CLIP as Surrogate Models for No-box Adversarial Attacks
by: Zhang, Jiaming, et al.
Published: (2023)
by: Zhang, Jiaming, et al.
Published: (2023)
Adversarially Robust Control of Conditional Value-at-Risk via Rockafellar-Uryasev Conformal Inference
by: Chen, Catherine, et al.
Published: (2026)
by: Chen, Catherine, et al.
Published: (2026)
Conformal Counterfactual Inference under Hidden Confounding
by: Chen, Zonghao, et al.
Published: (2024)
by: Chen, Zonghao, et al.
Published: (2024)
Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference
by: Griesemer, Sam, et al.
Published: (2024)
by: Griesemer, Sam, et al.
Published: (2024)
On Evaluating The Performance of Watermarked Machine-Generated Texts Under Adversarial Attacks
by: Liu, Zesen, et al.
Published: (2024)
by: Liu, Zesen, et al.
Published: (2024)
Stochastic Bandits Robust to Adversarial Attacks
by: Wang, Xuchuang, et al.
Published: (2024)
by: Wang, Xuchuang, et al.
Published: (2024)
MRMMIA: Membership Inference Attacks on Memory in Chat Agents
by: Chen, Kai, et al.
Published: (2026)
by: Chen, Kai, et al.
Published: (2026)
Distributionally Robust Set Representation Learning Under Inference-Time Element Corruption
by: Chen, Yankai, et al.
Published: (2026)
by: Chen, Yankai, et al.
Published: (2026)
Robust Satisficing Gaussian Process Bandits Under Adversarial Attacks
by: Saday, Artun, et al.
Published: (2025)
by: Saday, Artun, et al.
Published: (2025)
Passive Inference Attacks on Split Learning via Adversarial Regularization
by: Zhu, Xiaochen, et al.
Published: (2023)
by: Zhu, Xiaochen, et al.
Published: (2023)
Robust Deep Reinforcement Learning with Adaptive Adversarial Perturbations in Action Space
by: Liu, Qianmei, et al.
Published: (2024)
by: Liu, Qianmei, et al.
Published: (2024)
Distributed Learning with Adversarial Gradient Perturbations
by: Sangsiri, Nawapon, et al.
Published: (2026)
by: Sangsiri, Nawapon, et al.
Published: (2026)
CLMIA: Membership Inference Attacks via Unsupervised Contrastive Learning
by: Chen, Depeng, et al.
Published: (2024)
by: Chen, Depeng, et al.
Published: (2024)
Alleviating Performance Disparity in Adversarial Spatiotemporal Graph Learning Under Zero-Inflated Distribution
by: Bai, Songran, et al.
Published: (2025)
by: Bai, Songran, et al.
Published: (2025)
Hidden in Plain Sight: Undetectable Adversarial Bias Attacks on Vulnerable Patient Populations
by: Kulkarni, Pranav, et al.
Published: (2024)
by: Kulkarni, Pranav, et al.
Published: (2024)
Similar Items
-
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction
by: Chen, Danhui, et al.
Published: (2025) -
Cost-Sensitive Conformal Training with Provably Controllable Learning Bounds
by: Jia, Xuesong, et al.
Published: (2025) -
Machine Learning on Dynamic Functional Connectivity: Promise, Pitfalls, and Interpretations
by: Ding, Jiaqi, et al.
Published: (2024) -
Locally Adaptive Conformal Inference for Operator Models
by: Harris, Trevor, et al.
Published: (2025) -
Ensuring Calibration Robustness in Split Conformal Prediction Under Adversarial Attacks
by: Qian, Xunlei, et al.
Published: (2025)