:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Lebeda, Christian Janos, Even, Mathieu, Bellet, Aurélien, Josse, Julie
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2505.19589
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Causal Evaluation of Membership Inference Attacks
by: Even, Mathieu, et al.
Published: (2026)

Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees
by: Lebeda, Christian Janos, et al.
Published: (2026)

Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis
by: Khellaf, Rémi, et al.
Published: (2024)

Optimal Transport with Heterogeneously Missing Data
by: Bleistein, Linus, et al.
Published: (2025)

Principled Federated Random Forests for Heterogeneous Data
by: Khellaf, Rémi, et al.
Published: (2026)

Federated Causal Inference from Multi-Site Observational Data via Propensity Score Aggregation
by: Khellaf, Rémi, et al.
Published: (2025)

Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging
by: Cyffers, Edwige, et al.
Published: (2022)

Differentially Private Decentralized Learning with Random Walks
by: Cyffers, Edwige, et al.
Published: (2024)

Preference-based Conditional Treatment Effects and Policy Learning
by: Parnas, Dovid, et al.
Published: (2026)

Differentially Private and Federated Structure Learning in Bayesian Networks
by: Fehri, Ghita Fassy El, et al.
Published: (2025)

Rethinking the Win Ratio: A Causal Framework for Hierarchical Outcome Analysis
by: Even, Mathieu, et al.
Published: (2025)

PLAN: Variance-Aware Private Mean Estimation
by: Aumüller, Martin, et al.
Published: (2023)

The Correlated Gaussian Sparse Histogram Mechanism
by: Lebeda, Christian Janos, et al.
Published: (2024)

The Relative Gaussian Mechanism and its Application to Private Gradient Descent
by: Hendrikx, Hadrien, et al.
Published: (2023)

Set-Valued Policy Learning
by: Fuentes-Vicente, Laura, et al.
Published: (2026)

Private Rate-Constrained Optimization with Applications to Fair Learning
by: Yaghini, Mohammad, et al.
Published: (2025)

Better Differentially Private Approximate Histograms and Heavy Hitters using the Misra-Gries Sketch
by: Lebeda, Christian Janos, et al.
Published: (2023)

Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model
by: Cebere, Tudor, et al.
Published: (2024)

Optimal Transport under Group Fairness Constraints
by: Bleistein, Linus, et al.
Published: (2026)

Loss Gap Parity for Fairness in Heterogeneous Federated Learning
by: Erraji, Brahim, et al.
Published: (2026)

Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition
by: Lebeda, Christian Janos, et al.
Published: (2024)

Differentially Private Covariate Balancing Causal Inference
by: Ohnishi, Yuki, et al.
Published: (2024)

Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs
by: Zantedeschi, Valentina, et al.
Published: (2019)

Weighted Fourier Factorizations: Optimal Gaussian Noise for Differentially Private Marginal and Product Queries
by: Lebeda, Christian Janos, et al.
Published: (2025)

Privacy Auditing with Zero (0) Training Run
by: Cebere, Tudor, et al.
Published: (2026)

Privacy Attacks in Decentralized Learning
by: Mrini, Abdellah El, et al.
Published: (2024)

When Pattern-by-Pattern Works: Theoretical and Empirical Insights for Logistic Models with Missing Values
by: Muller, Christophe, et al.
Published: (2025)

Meta-learning of shared linear representations beyond well-specified linear regression
by: Even, Mathieu, et al.
Published: (2025)

On Gossip Algorithms for Machine Learning with Pairwise Objectives
by: Colin, Igor, et al.
Published: (2026)

Causal mediation analysis with one or multiple mediators: a comparative study
by: Abécassis, Judith, et al.
Published: (2025)

Privacy Amplification Through Synthetic Data: Insights from Linear Regression
by: Pierquin, Clément, et al.
Published: (2025)

Synthetic Data Generation for Intersectional Fairness by Leveraging Hierarchical Group Structure
by: Maheshwari, Gaurav, et al.
Published: (2024)

Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration
by: Pierquin, Clément, et al.
Published: (2023)

Adaptive Personalized Federated Learning via Multi-task Averaging of Kernel Mean Embeddings
by: Fermanian, Jean-Baptiste, et al.
Published: (2026)

Byzantine Failures Harm the Generalization of Robust Distributed Learning Algorithms More Than Data Poisoning
by: Boudou, Thomas, et al.
Published: (2025)

Marginal and training-conditional guarantees in one-shot federated conformal prediction
by: Humbert, Pierre, et al.
Published: (2024)

MMD-based Variable Importance for Distributional Random Forest
by: Bénard, Clément, et al.
Published: (2023)

Privacy Amplification Persists under Unlimited Synthetic Data Release
by: Pierquin, Clément, et al.
Published: (2026)

Efficient Differentiable Discovery of Causal Order
by: Chevalley, Mathieu, et al.
Published: (2024)

Practical do-Shapley Explanations with Estimand-Agnostic Causal Inference
by: Parafita, Álvaro, et al.
Published: (2025)