REVIEW 1 major objections 2 minor 109 references
Multi-Agent Conformal Prediction with Personalized Statistical Validity
T0 review · 1 major / 2 minor · reviewed 2026-06-28 · grok-4.3
Pith's one-line read Personalized federated conformal prediction yields asymptotically valid coverage for each agent despite data heterogeneity.
desk verdict PFWCP claims per-agent asymptotic coverage in one-shot federated conformal prediction via local density ratios and weighted quantiles, but the convergence of those ratios under communication limits is the part that needs checking. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The PFWCP framework that integrates local density ratio weighting to correct heterogeneity with weighted quantile aggregation to maintain validity and privacy.
What would settle it
An experiment in which the empirical coverage for one or more individual agents falls materially below the nominal level under strong heterogeneity would falsify the asymptotic per-agent guarantees.
Extended reading notes
Core claim
PFWCP combines local density ratio weighting with weighted quantile aggregation to correct for heterogeneity while preserving privacy. The method yields asymptotically valid marginal and calibration-conditional coverage guarantees for each participating agent and supports protocols with one-shot communication. Theoretical analysis presents an adjustment to the coverage variance governed by an effective sample size expression necessary in weighted conformal prediction.
Load-bearing premise
Local density ratio estimates accurately correct for data heterogeneity and the weighted quantile aggregation preserves validity in the asymptotic regime under the paper's weighting scheme.
Editorial extensions
If this is right
- Each agent obtains valid prediction sets without sharing raw calibration data.
- Both marginal and calibration-conditional coverage hold asymptotically for every agent individually.
- Only one round of communication is required for the protocol.
- Coverage variance is characterized by an effective sample size that accounts for the weighting scheme.
Reading between the lines
- The method suggests density ratio weighting as a general tool for restoring validity in other federated uncertainty quantification settings.
- Finite-sample behavior under varying degrees of heterogeneity remains an open question left implicit by the asymptotic analysis.
- The one-shot communication property may enable deployment in bandwidth-constrained or latency-sensitive multi-agent systems.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper proposes Personalized Federated Weighted Conformal Prediction (PFWCP), a multi-agent framework that uses local density ratio weighting combined with weighted quantile aggregation to achieve asymptotically valid marginal and calibration-conditional coverage guarantees for each individual agent. It supports one-shot communication protocols while addressing data heterogeneity and privacy constraints, and includes a theoretical adjustment to coverage variance via an effective sample size expression, with experiments showing improved calibration over federated conformal baselines.
Significance. If the asymptotic per-agent guarantees hold under the stated conditions, the work would be significant for enabling personalized uncertainty quantification in federated settings with limited local data and heterogeneity, where prior methods either provide only average guarantees or lose validity. The effective-sample-size variance adjustment for weighted conformal prediction is a potentially useful technical contribution if the supporting rates are established.
major comments (1)
- [Theoretical analysis] Theoretical analysis (and abstract claim of asymptotic validity): the central per-agent calibration-conditional coverage guarantee requires that local density ratio estimators converge at a rate sufficient for the effective-sample-size adjustment to control the coverage error under the one-shot protocol. The manuscript does not identify or verify an explicit rate condition that the restricted one-shot information satisfies; without this, the conditional guarantee may fail asymptotically even if marginal coverage holds.
minor comments (2)
- Notation for the weighting scheme and effective sample size expression should be introduced with explicit definitions before the variance adjustment is stated, to improve readability of the theoretical claims.
- Experiments section: clarify whether the reported improvements are statistically significant across multiple random seeds and heterogeneous data partitions, and include a direct comparison of coverage error rates against the theoretical bound.
Simulated Author's Rebuttal
We thank the referee for the careful reading and constructive feedback. We respond to the single major comment below.
read point-by-point responses
-
Referee: [Theoretical analysis] Theoretical analysis (and abstract claim of asymptotic validity): the central per-agent calibration-conditional coverage guarantee requires that local density ratio estimators converge at a rate sufficient for the effective-sample-size adjustment to control the coverage error under the one-shot protocol. The manuscript does not identify or verify an explicit rate condition that the restricted one-shot information satisfies; without this, the conditional guarantee may fail asymptotically even if marginal coverage holds.
Authors: We agree that the current theoretical section assumes consistency of the local density ratio estimators without stating an explicit rate condition sufficient to ensure the calibration-conditional coverage error vanishes under the one-shot protocol. The marginal coverage result holds under milder conditions, but the conditional guarantee relies on the effective-sample-size term controlling the remainder, which does require a rate on the estimation error. In the revision we will add an explicit assumption (sup-norm error of each density ratio estimator = o_p(n_eff^{-1/2})) together with a short argument showing that the one-shot protocol can meet this rate when the shared model parameters permit consistent estimation at the required speed. This addition will be placed immediately before the statement of the conditional coverage theorem. revision: yes
Circularity Check
No significant circularity; derivation self-contained against external benchmarks
full rationale
The abstract and description present asymptotic marginal and calibration-conditional coverage guarantees derived from local density ratio weighting combined with weighted quantile aggregation, plus an explicit effective-sample-size variance adjustment described as a necessary theoretical correction for weighted conformal prediction. No equations or steps are shown reducing the coverage claims to fitted inputs by construction, self-definitional loops, or load-bearing self-citations whose cited results themselves depend on the target result. The one-shot protocol and per-agent guarantees rest on stated assumptions about estimator convergence rather than tautological renaming or ansatz smuggling. This is the normal case of an independent theoretical derivation.
Assumptions & free parameters
assumptions (2)
- domain assumption Density ratios between local and other agents' distributions can be estimated sufficiently accurately to enable valid reweighting
- domain assumption The asymptotic regime applies such that coverage guarantees hold as sample sizes increase
Cite this review
Pith. "Pith review of Multi-Agent Conformal Prediction with Personalized Statistical Validity." pith.science (2026). https://pith.science/paper/7GBSBYMA
@misc{pith2026260600717,
author = {Pith},
title = {Pith review of: Multi-Agent Conformal Prediction with Personalized Statistical Validity},
year = {2026},
howpublished = {\url{https://pith.science/paper/7GBSBYMA}},
note = {Machine review of arXiv:2606.00717}
}
read the original abstract
Uncertainty quantification is essential in high-stakes machine learning tasks. However, one of the principled solutions, conformal prediction, faces challenges under limited local calibration data, privacy constraints, and data heterogeneity. In multi-agent settings, existing works do not simultaneously and satisfactorily address these challenges with guarantees either limited to averages across agents or losing validity in heterogeneous settings. Hence, we propose personalized federated weighted conformal prediction (PFWCP), a framework that combines local density ratio weighting with weighted quantile aggregation to correct for heterogeneity while preserving privacy. The method yields asymptotically valid marginal and calibration-conditional coverage guarantees for each participating agent and supports protocols with one-shot communication. Theoretical analysis presents an adjustment to the coverage variance, governed by an effective sample size expression, which is necessary in the context of weighted conformal prediction, and experiments on synthetic and real datasets show improved calibration quality over state-of-the-art federated conformal baselines.
Figures
Figures from the paper (1 more)
Reference graph
Works this paper leans on
-
[1]
2025 , eprint=
Full-conformal novelty detection: A powerful and non-random approach , author=. 2025 , eprint=
2025
-
[2]
2024 , eprint=
Boosting e-BH via conditional calibration , author=. 2024 , eprint=
2024
-
[3]
Proceedings of the 37th International Conference on Neural Information Processing Systems , articleno =
Bashari, Meshi and Epstein, Amir and Romano, Yaniv and Sesia, Matteo , title =. Proceedings of the 37th International Conference on Neural Information Processing Systems , articleno =. 2023 , publisher =
2023
-
[4]
Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications , pages=
Enhancing Conformal Prediction Using E-Test Statistics , author=. Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications , pages=. 2024 , volume=
2024
-
[5]
Journal of the Royal Statistical Society Series B: Statistical Methodology , volume =
Ruodu Wang and Aaditya Ramdas , title =. Journal of the Royal Statistical Society Series B: Statistical Methodology , volume =. 2022 , month =
2022
-
[6]
Transactions on Machine Learning Research , issn=
E-Valuating Classifier Two-Sample Tests , author=. Transactions on Machine Learning Research , issn=
-
[7]
2025 , eprint=
E-Values Expand the Scope of Conformal Prediction , author=. 2025 , eprint=
2025
-
[8]
2024 , eprint=
Continuous Testing: Unifying Tests and E-values , author=. 2024 , eprint=
2024
Show all 109 references
-
[9]
2025 , eprint=
Real-time Program Evaluation using Anytime-valid Rank Tests , author=. 2025 , eprint=
2025
-
[10]
2025 , eprint=
Optimal Conformal Prediction, E-values, Fuzzy Prediction Sets and Subsequent Decisions , author=. 2025 , eprint=
2025
-
[11]
2025 , volume =
Foundations and Trends in Statistics , title =. 2025 , volume =. doi:10.1561/3600000002 , issn =
2025 doi
-
[12]
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics , pages =
Online multiple testing with e-values , author =. Proceedings of The 27th International Conference on Artificial Intelligence and Statistics , pages =. 2024 , editor =
2024
-
[13]
Yifan Zhang and Zijian Wei and Haojie Ren and Changliang Zou , booktitle=. e-. 2025 , url=
2025
-
[14]
The Annals of Statistics , number =
Stephen Bates and Emmanuel Cand. The Annals of Statistics , number =. 2023 , doi =
2023
-
[15]
The Annals of Statistics , number =
Ariane Marandon and Lihua Lei and David Mary and Etienne Roquain , title =. The Annals of Statistics , number =. 2024 , doi =
2024
-
[16]
Selection by prediction with conformal p-values , year =
Jin, Ying and Cand\`. Selection by prediction with conformal p-values , year =. Journal of Machine Learning Research , month =
-
[17]
The Thirteenth International Conference on Learning Representations , year=
Conditional Testing based on Localized Conformal \ p\ -values , author=. The Thirteenth International Conference on Learning Representations , year=
-
[18]
2025 , eprint=
Unified Conformalized Multiple Testing with Full Data Efficiency , author=. 2025 , eprint=
2025
-
[19]
2025 , eprint=
Sequentializing a Test: Anytime Validity is Free , author=. 2025 , eprint=
2025
-
[20]
2025 , eprint=
An online generalization of the (e-)Benjamini-Hochberg procedure , author=. 2025 , eprint=
2025
-
[21]
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics , pages =
Communication-Efficient Learning of Deep Networks from Decentralized Data , author =. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics , pages =. 2017 , volume =
2017
-
[22]
The Annals of Statistics , number =
Jia Gu and Song Xi Chen , title =. The Annals of Statistics , number =. 2024 , doi =
2024
-
[23]
and Raskar, Ramesh , title =
Lu, Charles and Yu, Yaodong and Karimireddy, Sai Praneeth and Jordan, Michael I. and Raskar, Ramesh , title =. proceedings of the 40th International Conference on Machine Learning , articleno =. 2023 , publisher =
2023
-
[24]
Federated Inference With Reliable Uncertainty Quantification Over Wireless Channels via Conformal Prediction , year=
Zhu, Meiyi and Zecchin, Matteo and Park, Sangwoo and Guo, Caili and Feng, Chunyan and Simeone, Osvaldo , journal=. Federated Inference With Reliable Uncertainty Quantification Over Wireless Channels via Conformal Prediction , year=
-
[25]
proceedings of the 40th International Conference on Machine Learning , pages =
One-Shot Federated Conformal Prediction , author =. proceedings of the 40th International Conference on Machine Learning , pages =. 2023 , volume =
2023
-
[26]
proceedings of the 27th International Conference on Artificial Intelligence and Statistics , pages =
Efficient Conformal Prediction under Data Heterogeneity , author =. proceedings of the 27th International Conference on Artificial Intelligence and Statistics , pages =. 2024 , volume =
2024
-
[27]
Proceedings of the 41st International Conference on Machine Learning , pages =
Multi-Source Conformal Inference Under Distribution Shift , author =. Proceedings of the 41st International Conference on Machine Learning , pages =. 2024 , volume =
2024
-
[28]
Statistical Theory and Related Fields , volume =
Yuan Gao and Weidong Liu and Hansheng Wang and Xiaozhou Wang and Yibo Yan and Riquan Zhang , title =. Statistical Theory and Related Fields , volume =. 2022 , publisher =
2022
-
[29]
Journal of the Royal Statistical Society Series B: Statistical Methodology , volume =
Sesia, Matteo and Wang, Y X Rachel and Tong, Xin , title =. Journal of the Royal Statistical Society Series B: Statistical Methodology , volume =. 2024 , month =
2024
-
[30]
proceedings of the 40th International Conference on Machine Learning , pages =
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift , author =. proceedings of the 40th International Conference on Machine Learning , pages =. 2023 , volume =
2023
-
[31]
The Annals of Statistics , number =
Adel Javanmard and Andrea Montanari , title =. The Annals of Statistics , number =. 2018 , doi =
2018
-
[32]
Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications , pages =
Conformal Predictive Systems Under Covariate Shift , author =. Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications , pages =. 2024 , editor =
2024
-
[33]
Conformal Prediction Under Covariate Shift , volume =
Tibshirani, Ryan J and Foygel Barber, Rina and Candes, Emmanuel and Ramdas, Aaditya , booktitle =. Conformal Prediction Under Covariate Shift , volume =
-
[34]
proceedings of the 39th Annual Conference on Neural Information Processing Systems , year=
Personalized Federated Conformal Prediction with Localization , author=. proceedings of the 39th Annual Conference on Neural Information Processing Systems , year=
-
[35]
2025 , eprint=
Probably Approximately Correct Labels , author=. 2025 , eprint=
2025
-
[36]
The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=
Robust Conformal Prediction Using Privileged Information , author=. The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=
-
[37]
2025 , eprint=
Conformal Prediction with Corrupted Labels: Uncertain Imputation and Robust Re-weighting , author=. 2025 , eprint=
2025
-
[38]
Conference on Uncertainty in Artificial Intelligence , pages =
Distribution-free uncertainty quantification for classification under label shift , author =. Conference on Uncertainty in Artificial Intelligence , pages =. 2021 , volume =
2021
-
[39]
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics , pages =
Conformal Prediction Under Generalized Covariate Shift with Posterior Drift , author =. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics , pages =. 2025 , volume =
2025
-
[40]
Proceedings of the 42nd International Conference on Machine Learning , pages =
Robust Conformal Outlier Detection under Contaminated Reference Data , author =. Proceedings of the 42nd International Conference on Machine Learning , pages =. 2025 , volume =
2025
-
[41]
Machine Learning , number =
Cherubin, Giovanni , title =. Machine Learning , number =. 2019 , doi =
2019
-
[42]
2022 , issn =
Multi split conformal prediction , journal =. 2022 , issn =. doi:10.1016/j.spl.2022.109395 , author =
2022 doi
-
[43]
Journal of the American Statistical Association , volume =
Yachong Yang and Arun Kumar Kuchibhotla , title =. Journal of the American Statistical Association , volume =. 2025 , publisher =
2025
-
[44]
2022 , issn =
Nested conformal prediction and quantile out-of-bag ensemble methods , journal =. 2022 , issn =. doi:10.1016/j.patcog.2021.108496 , author =
2022 doi
-
[45]
2024 , eprint=
Marginal and training-conditional guarantees in one-shot federated conformal prediction , author =. 2024 , eprint=
2024
-
[46]
Forty-second International Conference on Machine Learning , year=
False Coverage Proportion Control for Conformal Prediction , author=. Forty-second International Conference on Machine Learning , year=
-
[47]
Second Workshop on Test-Time Adaptation: Putting Updates to the Test! at ICML 2025 , year=
An Evidence-Based Post-Hoc Adjustment Framework for Anomaly Detection Under Data Contamination , author=. Second Workshop on Test-Time Adaptation: Putting Updates to the Test! at ICML 2025 , year=
2025
-
[48]
The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=
One Sample is Enough to Make Conformal Prediction Robust , author=. The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=
-
[49]
The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=
Conformal Prediction under L\'evy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbations , author=. The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=
-
[50]
The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=
Backward Conformal Prediction , author=. The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=
-
[51]
The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=
Class conditional conformal prediction for multiple inputs by p-value aggregation , author=. The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=
-
[52]
The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=
Adaptive Latent-Space Constraints in Personalized Federated Learning , author=. The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=
-
[53]
Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications , pages =
On the Calibration of Aggregated Conformal Predictors , author =. Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications , pages =. 2017 , editor =
2017
-
[54]
Artificial Intelligence Applications and Innovations , year=
Carlsson, Lars and Eklund, Martin and Norinder, Ulf , title=. Artificial Intelligence Applications and Innovations , year=
-
[55]
Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications , pages =
Combination of Conformal Predictors for Classification , author =. Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications , pages =. 2017 , volume =
2017
-
[56]
2024 , eprint=
Merging uncertainty sets via majority vote , author=. 2024 , eprint=
2024
-
[57]
2025 , eprint=
Theoretical Foundations of Conformal Prediction , author=. 2025 , eprint=
2025
-
[58]
Veeravalli and Anirban Roy and Susmit Jha , title =
Akshayaa Magesh and Venugopal V. Veeravalli and Anirban Roy and Susmit Jha , title =. Journal of Machine Learning Research , year =
-
[59]
Advances in Neural Information Processing Systems , editor =
Adaptive Conformal Inference Under Distribution Shift , author =. Advances in Neural Information Processing Systems , editor =
-
[60]
Isaac Gibbs and Emmanuel J. Cand. Conformal Inference for Online Prediction with Arbitrary Distribution Shifts , journal =. 2024 , volume =
2024
-
[61]
Oliveira and Paulo Orenstein and Thiago Ramos and Jo
Roberto I. Oliveira and Paulo Orenstein and Thiago Ramos and Jo. Split Conformal Prediction and Non-Exchangeable Data , journal =. 2024 , volume =
2024
-
[62]
The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=
Personalized Federated Learning via Feature Distribution Adaptation , author=. The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=
-
[63]
Proceedings of the 38th International Conference on Machine Learning , pages =
Ditto: Fair and Robust Federated Learning Through Personalization , author =. Proceedings of the 38th International Conference on Machine Learning , pages =. 2021 , editor =
2021
-
[64]
NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty , year=
Two Students: Enabling Uncertainty Quantification in Federated Learning Clients , author=. NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty , year=
2024
-
[65]
Hong-You Chen and Wei-Lun Chao , booktitle=. Fed. 2021 , url=
2021
-
[66]
Fed-ensemble: Ensemble Models in Federated Learning for Improved Generalization and Uncertainty Quantification , year=
Shi, Naichen and Lai, Fan and Al Kontar, Raed and Chowdhury, Mosharaf , journal=. Fed-ensemble: Ensemble Models in Federated Learning for Improved Generalization and Uncertainty Quantification , year=
-
[67]
The Thirteenth International Conference on Learning Representations , year=
Unlocking the Potential of Model Calibration in Federated Learning , author=. The Thirteenth International Conference on Learning Representations , year=
-
[68]
2025 , issn =
Federated learning meets Bayesian neural network: Robust and uncertainty-aware distributed variational inference , journal =. 2025 , issn =. doi:10.1016/j.neunet.2025.107135 , author =
2025 doi
-
[69]
2026 , eprint=
Tighter confidence intervals for quantiles of heterogeneous data , author=. 2026 , eprint=
2026
-
[70]
2024 , eprint=
Federated Prediction-Powered Inference from Decentralized Data , author=. 2024 , eprint=
2024
-
[71]
2025 , eprint=
Extending Prediction-Powered Inference through Conformal Prediction , author=. 2025 , eprint=
2025
-
[72]
Vadlamani and Amin Meghrazi and Srinivasan Parthasarathy , year=
Anutam Srinivasan and Aditya T. Vadlamani and Amin Meghrazi and Srinivasan Parthasarathy , year=. 2509.22907 , archivePrefix=
-
[73]
Journal of the Royal Statistical Society Series B: Statistical Methodology , volume =
Gibbs, Isaac and Cherian, John J and Candès, Emmanuel J , title =. Journal of the Royal Statistical Society Series B: Statistical Methodology , volume =. 2025 , month =
2025
-
[74]
2020 , editor =
Karimireddy, Sai Praneeth and Kale, Satyen and Mohri, Mehryar and Reddi, Sashank and Stich, Sebastian and Suresh, Ananda Theertha , booktitle =. 2020 , editor =
2020
-
[75]
Angelopoulos and Stephen Bates and Clara Fannjiang and Michael I
Anastasios N. Angelopoulos and Stephen Bates and Clara Fannjiang and Michael I. Jordan and Tijana Zrnic , title =. Science , volume =. 2023 , doi =
2023
-
[76]
Proceedings of the Asian Conference on Machine Learning , pages =
Conditional Validity of Inductive Conformal Predictors , author =. Proceedings of the Asian Conference on Machine Learning , pages =. 2012 , editor =
2012
-
[77]
Semi-Supervised Risk Control via Prediction-Powered Inference , year=
Einbinder, Bat-Sheva and Ringel, Liran and Romano, Yaniv , journal=. Semi-Supervised Risk Control via Prediction-Powered Inference , year=
-
[78]
The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=
Active, anytime-valid risk controlling prediction sets , author=. The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=
-
[79]
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics , pages =
Improving Adaptive Conformal Prediction Using Self-Supervised Learning , author =. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics , pages =. 2023 , editor =
2023
-
[80]
2025 , eprint=
Semi-Supervised Conformal Prediction With Unlabeled Nonconformity Score , author=. 2025 , eprint=
2025
-
[81]
Proceedings of the Twelfth Symposium on Conformal and Probabilistic Prediction with Applications , pages =
Conformal Prediction with Partially Labeled Data , author =. Proceedings of the Twelfth Symposium on Conformal and Probabilistic Prediction with Applications , pages =. 2023 , editor =
2023
-
[82]
Dataset shift in machine learning , chapter =
Dataset Shift in Machine Learning , author =. Dataset shift in machine learning , chapter =. 2009 , isbn =
2009
-
[83]
Advancing breast, lung and prostate cancer research with federated learning: A systematic review , journal =
Ankolekar, Anshu and Boie, Sebastian and Abdollahyan, Maryam and Gadaleta, Emanuela and Hasheminasab, Seyed Alireza and Yang, Guang and Beauville, Charles and Dikaios, Nikolaos and Kastis, George Anthony and Bussmann, Michael and Chelala, Claude and Khalid, Sara and Kruger, Ha...
2025 doi
-
[84]
2025 , issn =
Modeling unknowns: A vision for uncertainty-aware machine learning in healthcare , journal =. 2025 , issn =. doi:10.1016/j.ijmedinf.2025.106014 , author =
2025 doi
-
[85]
2005 , isbn =
Vovk, Vladimir and Gammerman, Alex and Shafer, Glenn , title =. 2005 , isbn =
2005
-
[86]
On the method of bounded differences , booktitle=
McDiarmid, Colin , year=. On the method of bounded differences , booktitle=
-
[87]
and Marcolini, Michael , title =
Brooks, Thomas and Pope, D. and Marcolini, Michael , title =. 1989 , howpublished =
1989
-
[88]
Progress in Artificial Intelligence , doi=
Event labeling combining ensemble detectors and background knowledge , publisher=. Progress in Artificial Intelligence , doi=. 2013 , issn=
2013
-
[89]
1998 , issn =
Modeling of strength of high-performance concrete using artificial neural networks , journal =. 1998 , issn =. doi:10.1016/S0008-8846(98)00165-3 , author =
1998 doi
-
[90]
2009 , howpublished =
Redmond, Michael , title =. 2009 , howpublished =
2009
-
[91]
Achilles and Helen Pate Bain and Fred Bellott and Jayne Boyd-Zaharias and Jeremy Finn and John Folger and John Johnston and Elizabeth Word , howpublished =
C.M. Achilles and Helen Pate Bain and Fred Bellott and Jayne Boyd-Zaharias and Jeremy Finn and John Folger and John Johnston and Elizabeth Word , howpublished =. Tennessee's Student Teacher Achievement Ratio (STAR) project , year =
-
[92]
2013 , howpublished =
Rana, Prashant , title =. 2013 , howpublished =
2013
-
[93]
proceedings of the 7th International Conference on Learning Representations , year=
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations , author=. proceedings of the 7th International Conference on Learning Representations , year=
-
[94]
Ord , title =
Alan Stuart and Keith J. Ord , title =
-
[95]
2026 , eprint=
Efficient Federated Conformal Prediction with Group-Conditional Guarantees , author=. 2026 , eprint=
2026
-
[96]
2026 , issn =
FCP-Pro: Federated conformal prediction algorithm based on prototype similarity , journal =. 2026 , issn =. doi:10.1016/j.patcog.2025.112514 , author =
2026 doi
-
[97]
2020 , doi =
The future of digital health with federated learning , journal =. 2020 , doi =
2020
-
[98]
2026 , url=
Conformal Data Contamination Tests for In-distribution Data Acquisition , author=. 2026 , url=
2026
-
[99]
Transactions on Machine Learning Research , issn=
Training-Conditional Coverage Bounds under Covariate Shift , author=. Transactions on Machine Learning Research , issn=
-
[100]
Covariate Shift Adaptation by Importance Weighted Cross Validation , journal =
Masashi Sugiyama and Matthias Krauledat and Klaus-Robert M. Covariate Shift Adaptation by Importance Weighted Cross Validation , journal =. 2007 , volume =
2007
-
[101]
Correcting Sample Selection Bias by Unlabeled Data , volume =
Huang, Jiayuan and Gretton, Arthur and Borgwardt, Karsten and Sch\". Correcting Sample Selection Bias by Unlabeled Data , volume =. Advances in Neural Information Processing Systems , editor =
-
[102]
Density Ratio Estimation in Machine Learning , publisher=
Sugiyama, Masashi and Suzuki, Taiji and Kanamori, Takafumi , year=. Density Ratio Estimation in Machine Learning , publisher=
-
[103]
Annals of the Institute of Statistical Mathematics , year =
Sugiyama, Masashi and Suzuki, Taiji and Nakajima, Shinichi and Kashima, Hisashi and von Bünau, Paul and Kawanabe, Motoaki , title =. Annals of the Institute of Statistical Mathematics , year =
-
[104]
proceedings of the 25th International Conference on Artificial Intelligence and Statistics , pages =
Density Ratio Estimation via Infinitesimal Classification , author =. proceedings of the 25th International Conference on Artificial Intelligence and Statistics , pages =. 2022 , volume =
2022
-
[105]
Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization , volume =
Nguyen, XuanLong and Wainwright, Martin J and Jordan, Michael , booktitle =. Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization , volume =
-
[106]
proceedings of the 42nd International Conference on Machine Learning , year=
Density Ratio Estimation with Conditional Probability Paths , author=. proceedings of the 42nd International Conference on Machine Learning , year=
-
[107]
Relative Density-Ratio Estimation for Robust Distribution Comparison , volume =
Yamada, Makoto and Suzuki, Taiji and Kanamori, Takafumi and Hachiya, Hirotaka and Sugiyama, Masashi , booktitle =. Relative Density-Ratio Estimation for Robust Distribution Comparison , volume =
-
[108]
Estimating the contamination Factor's distribution in unsupervised anomaly detection , year =
Perini, Lorenzo and B\". Estimating the contamination Factor's distribution in unsupervised anomaly detection , year =. proceedings of the 40th International Conference on Machine Learning , articleno =
-
[109]
Journal of Machine Learning Research , year=
Gilles Blanchard and Gyemin Lee and Clayton Scott , title=. Journal of Machine Learning Research , year=
Reviewed June 28, 2026 · model on record in the stance chip above.
Discussion (0). Continue with ORCID to comment.