Conformal Prediction for Privacy-Preserving Machine Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Balinsky, Alexander David, Krzeminski, Dominik, Balinsky, Alexander |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Enhancing Conformal Prediction Using E-Test Statistics
by: Balinsky, A. A., et al.
Published: (2024)
by: Balinsky, A. A., et al.
Published: (2024)
When Can We Reuse a Calibration Set for Multiple Conformal Predictions?
by: Balinsky, A. A., et al.
Published: (2025)
by: Balinsky, A. A., et al.
Published: (2025)
Decision Making in Changing Environments: Robustness, Query-Based Learning, and Differential Privacy
by: Chen, Fan, et al.
Published: (2025)
by: Chen, Fan, et al.
Published: (2025)
Label Noise Robustness of Conformal Prediction
by: Einbinder, Bat-Sheva, et al.
Published: (2022)
by: Einbinder, Bat-Sheva, et al.
Published: (2022)
Bias-Aware Conformal Prediction for Metric-Based Imaging Pipelines
by: Cheung, Matt Y., et al.
Published: (2024)
by: Cheung, Matt Y., et al.
Published: (2024)
Conformal Policy Control
by: Prinster, Drew, et al.
Published: (2026)
by: Prinster, Drew, et al.
Published: (2026)
Outcome-Based Online Reinforcement Learning: Algorithms and Fundamental Limits
by: Chen, Fan, et al.
Published: (2025)
by: Chen, Fan, et al.
Published: (2025)
Understanding In-Context Learning on Structured Manifolds: Bridging Attention to Kernel Methods
by: Shen, Zhaiming, et al.
Published: (2025)
by: Shen, Zhaiming, et al.
Published: (2025)
Conformal Risk Control
by: Angelopoulos, Anastasios N., et al.
Published: (2022)
by: Angelopoulos, Anastasios N., et al.
Published: (2022)
Near-Optimal Learning and Planning in Separated Latent MDPs
by: Chen, Fan, et al.
Published: (2024)
by: Chen, Fan, et al.
Published: (2024)
Learning with Differentially Private (Sliced) Wasserstein Gradients
by: Rodríguez-Vítores, David, et al.
Published: (2025)
by: Rodríguez-Vítores, David, et al.
Published: (2025)
Beyond Covariance Matrix: The Statistical Complexity of Private Linear Regression
by: Chen, Fan, et al.
Published: (2025)
by: Chen, Fan, et al.
Published: (2025)
A Theory of the Mechanics of Information: Generalization Through Measurement of Uncertainty (Learning is Measuring)
by: Hazard, Christopher J., et al.
Published: (2025)
by: Hazard, Christopher J., et al.
Published: (2025)
Attack-Aware Noise Calibration for Differential Privacy
by: Kulynych, Bogdan, et al.
Published: (2024)
by: Kulynych, Bogdan, et al.
Published: (2024)
Byzantine Machine Learning: MultiKrum and an optimal notion of robustness
by: Bareilles, Gilles, et al.
Published: (2026)
by: Bareilles, Gilles, et al.
Published: (2026)
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models
by: Rajendran, Goutham, et al.
Published: (2024)
by: Rajendran, Goutham, et al.
Published: (2024)
Compression, Generalization and Learning
by: Campi, Marco C., et al.
Published: (2023)
by: Campi, Marco C., et al.
Published: (2023)
Beyond the Calibration Point: Mechanism Comparison in Differential Privacy
by: Kaissis, Georgios, et al.
Published: (2024)
by: Kaissis, Georgios, et al.
Published: (2024)
Online Learning with Unknown Constraints
by: Sridharan, Karthik, et al.
Published: (2024)
by: Sridharan, Karthik, et al.
Published: (2024)
Adaptive Sample Aggregation In Transfer Learning
by: Hanneke, Steve, et al.
Published: (2024)
by: Hanneke, Steve, et al.
Published: (2024)
Provable Reward-Agnostic Preference-Based Reinforcement Learning
by: Zhan, Wenhao, et al.
Published: (2023)
by: Zhan, Wenhao, et al.
Published: (2023)
On the Statistical Capacity of Deep Generative Models
by: Tam, Edric, et al.
Published: (2025)
by: Tam, Edric, et al.
Published: (2025)
Learning Hierarchical Polynomials of Multiple Nonlinear Features with Three-Layer Networks
by: Fu, Hengyu, et al.
Published: (2024)
by: Fu, Hengyu, et al.
Published: (2024)
Chemical Reaction Networks Learn Better than Spiking Neural Networks
by: Jaffard, Sophie, et al.
Published: (2026)
by: Jaffard, Sophie, et al.
Published: (2026)
Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning
by: Foster, Dylan J., et al.
Published: (2024)
by: Foster, Dylan J., et al.
Published: (2024)
Beyond identifiability: Learning causal representations with few environments and finite samples
by: Lee, Inbeom, et al.
Published: (2026)
by: Lee, Inbeom, et al.
Published: (2026)
A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional Data
by: Chakraborty, Saptarshi, et al.
Published: (2024)
by: Chakraborty, Saptarshi, et al.
Published: (2024)
Labels or Preferences? Budget-Constrained Learning with Human Judgments over AI-Generated Outputs
by: Dong, Zihan, et al.
Published: (2026)
by: Dong, Zihan, et al.
Published: (2026)
Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions
by: Javanmard, Adel, et al.
Published: (2024)
by: Javanmard, Adel, et al.
Published: (2024)
Towards a Sharp Analysis of Offline Policy Learning for $f$-Divergence-Regularized Contextual Bandits
by: Zhao, Qingyue, et al.
Published: (2025)
by: Zhao, Qingyue, et al.
Published: (2025)
Unified Algorithms for RL with Decision-Estimation Coefficients: PAC, Reward-Free, Preference-Based Learning, and Beyond
by: Chen, Fan, et al.
Published: (2022)
by: Chen, Fan, et al.
Published: (2022)
Multivariate Standardized Residuals for Conformal Prediction
by: Braun, Sacha, et al.
Published: (2025)
by: Braun, Sacha, et al.
Published: (2025)
Neural Networks Learn Generic Multi-Index Models Near Information-Theoretic Limit
by: Zhang, Bohan, et al.
Published: (2025)
by: Zhang, Bohan, et al.
Published: (2025)
Max-Rank: Efficient Multiple Testing for Conformal Prediction
by: Timans, Alexander, et al.
Published: (2023)
by: Timans, Alexander, et al.
Published: (2023)
Adversarial Subspace Generation for Outlier Detection in High-Dimensional Data
by: Cribeiro-Ramallo, Jose, et al.
Published: (2025)
by: Cribeiro-Ramallo, Jose, et al.
Published: (2025)
Informationally Compressive Anonymization: Non-Degrading Sensitive Input Protection for Privacy-Preserving Supervised Machine Learning
by: Samuelson, Jeremy J
Published: (2026)
by: Samuelson, Jeremy J
Published: (2026)
Le Cam Distortion: A Decision-Theoretic Framework for Robust Transfer Learning
by: Akdemir, Deniz
Published: (2025)
by: Akdemir, Deniz
Published: (2025)
A Comparative Analysis of Statistical and Machine Learning Models for Outlier Detection in Bitcoin Limit Order Books
by: Letteri, Ivan
Published: (2025)
by: Letteri, Ivan
Published: (2025)
Probabilistic Conformal Prediction with Approximate Conditional Validity
by: Plassier, Vincent, et al.
Published: (2024)
by: Plassier, Vincent, et al.
Published: (2024)
Learning to Fuse Temporal Proximity Networks: A Case Study in Chimpanzee Social Interactions
by: He, Yixuan, et al.
Published: (2025)
by: He, Yixuan, et al.
Published: (2025)
Similar Items
-
Enhancing Conformal Prediction Using E-Test Statistics
by: Balinsky, A. A., et al.
Published: (2024) -
When Can We Reuse a Calibration Set for Multiple Conformal Predictions?
by: Balinsky, A. A., et al.
Published: (2025) -
Decision Making in Changing Environments: Robustness, Query-Based Learning, and Differential Privacy
by: Chen, Fan, et al.
Published: (2025) -
Label Noise Robustness of Conformal Prediction
by: Einbinder, Bat-Sheva, et al.
Published: (2022) -
Bias-Aware Conformal Prediction for Metric-Based Imaging Pipelines
by: Cheung, Matt Y., et al.
Published: (2024)