Aurélien Bellet

Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis AISTATS 2025 Privacy Amplification Through Synthetic Data: Insights from Linear Regression ICML 2025 Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model ICLR 2025 Confidential-DPproof: Confidential Proof of Differentially Private Training ICLR 2024 Differentially Private Decentralized Learning with Random Walks ICML 2024 DP-SGD Without Clipping: The Lipschitz Neural Network Way ICLR 2024 Improved Stability and Generalization Guarantees of the Decentralized SGD Algorithm ICML 2024 Privacy Attacks in Decentralized Learning ICML 2024 Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration via Shift Reduction Lemmas ICML 2024 The Relative Gaussian Mechanism and its Application to Private Gradient Descent AISTATS 2024 Differential Privacy has Bounded Impact on Fairness in Classification ICML 2023 Fair Without Leveling Down: A New Intersectional Fairness Definition EMNLP 2023 From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning ICML 2023 High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent AISTATS 2023 One-Shot Federated Conformal Prediction ICML 2023 Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Data AISTATS 2023 Differentially Private Coordinate Descent for Composite Empirical Risk Minimization ICML 2022 Differentially Private Federated Learning on Heterogeneous Data AISTATS 2022 Enhancing Speech Privacy with Slicing Interspeech 2022 Fair NLP Models with Differentially Private Text Encoders EMNLP 2022 FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings NeurIPS 2022 Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging NeurIPS 2022 Privacy Amplification by Decentralization AISTATS 2022 Federated Multi-Task Learning under a Mixture of Distributions NeurIPS 2021 Learning Fair Scoring Functions: Bipartite Ranking under ROC-based Fairness Constraints AISTATS 2021 A Comparative Study of Speech Anonymization Metrics Interspeech 2020 Design Choices for X-Vector Based Speaker Anonymization Interspeech 2020 Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs AISTATS 2020 Joint Learning of the Graph and the Data Representation for Graph-Based Semi-Supervised Learning COLING 2020 metric-learn: Metric Learning Algorithms in Python JMLR 2020 Private Protocols for U-Statistics in the Local Model and Beyond AISTATS 2020 Kernel Approximation Methods for Speech Recognition JMLR 2019 Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion? Interspeech 2019 A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images EMNLP 2018 A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization ICML 2018 Personalized and Private Peer-to-Peer Machine Learning AISTATS 2018 Decentralized Collaborative Learning of Personalized Models over Networks AISTATS 2017 Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions ICML 2016 On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability NeurIPS 2016 Scaling-up Empirical Risk Minimization: Optimization of Incomplete U-statistics JMLR 2016 Extending Gossip Algorithms to Distributed Estimation of U-statistics NeurIPS 2015 SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk NeurIPS 2015 Similarity Learning for High-Dimensional Sparse Data AISTATS 2015