Copertina

Differentially private ratio statistics

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Shoham, Tomer, Ligettt, Katrina
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Differentially Private Nonparametric Confidence Intervals Under Minimal Distributional Assumptions
di: Shoham, Tomer, et al.
Pubblicazione: (2025)

Resampling methods for private statistical inference
di: Chadha, Karan, et al.
Pubblicazione: (2024)

Differentially private Bayesian tests
di: Chakraborty, Abhisek, et al.
Pubblicazione: (2024)

Robust CATE Estimation Using Novel Ensemble Methods
di: Machluf, Oshri, et al.
Pubblicazione: (2024)

Differentially private synthesis of Spatial Point Processes
di: Kim, Dangchan, et al.
Pubblicazione: (2025)

Accurate, private, secure, federated U-statistics with higher degree
di: Sinh, Quentin, et al.
Pubblicazione: (2026)

Unbiased Stochastic Optimization for Gaussian Processes on Finite Dimensional RKHS
di: Shoham, Neta, et al.
Pubblicazione: (2025)

Balancing Coverage and Draft Latency in Vocabulary Trimming for Faster Speculative Decoding
di: Shoham, Ofir Ben
Pubblicazione: (2026)

Differentially private and decentralized randomized power method
di: Nicolas, Julien, et al.
Pubblicazione: (2024)

How Well Can Differential Privacy Be Audited in One Run?
di: Keinan, Amit, et al.
Pubblicazione: (2025)

MedConceptsQA: Open Source Medical Concepts QA Benchmark
di: Shoham, Ofir Ben, et al.
Pubblicazione: (2024)

Flatness After All?
di: Shoham, Neta, et al.
Pubblicazione: (2025)

The Fast Mixing Mechanism for Differential Privacy
di: Lev, Omri, et al.
Pubblicazione: (2026)

DNA: Differentially private Neural Augmentation for contact tracing
di: Romijnders, Rob, et al.
Pubblicazione: (2024)

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches
di: Lev, Omri, et al.
Pubblicazione: (2025)

Directional-Clamp PPO
di: Karpel, Gilad, et al.
Pubblicazione: (2025)

Displacement-Resistant Extensions of DPO with Nonconvex $f$-Divergences
di: Pipano, Idan, et al.
Pubblicazione: (2026)

Differentially private projection-depth-based medians
di: Ramsay, Kelly, et al.
Pubblicazione: (2023)

Understanding Understanding: A Pragmatic Framework Motivated by Large Language Models
di: Leyton-Brown, Kevin, et al.
Pubblicazione: (2024)

CPLLM: Clinical Prediction with Large Language Models
di: Shoham, Ofir Ben, et al.
Pubblicazione: (2023)

Differentially private multivariate medians
di: Ramsay, Kelly, et al.
Pubblicazione: (2022)

Multiclass Loss Geometry Matters for Generalization of Gradient Descent in Separable Classification
di: Schliserman, Matan, et al.
Pubblicazione: (2025)

Exact Mean Square Linear Stability Analysis for SGD
di: Mulayoff, Rotem, et al.
Pubblicazione: (2023)

Complexity of Vector-valued Prediction: From Linear Models to Stochastic Convex Optimization
di: Schliserman, Matan, et al.
Pubblicazione: (2024)

A New Analysis of Differential Privacy's Generalization Guarantees
di: Jung, Christopher, et al.
Pubblicazione: (2019)

Fair Decisions from Calibrated Scores: Achieving Optimal Classification While Satisfying Sufficiency
di: Benger, Etam, et al.
Pubblicazione: (2026)

Generalization in the Face of Adaptivity: A Bayesian Perspective
di: Shenfeld, Moshe, et al.
Pubblicazione: (2021)

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks
di: Shoham, Elad, et al.
Pubblicazione: (2024)

Discrete-time Competing-Risks Regression with or without Penalization
di: Meir, Tomer, et al.
Pubblicazione: (2023)

Approximating Uniform Random Rotations by Two-Block Structured Hadamard Rotations in High Dimensions
di: Zilca, Tomer, et al.
Pubblicazione: (2026)

ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment
di: Borreda, Tomer, et al.
Pubblicazione: (2024)

Applications of Random Matrix Theory in Machine Learning and Brain Mapping
di: Lawrence, Katrina
Pubblicazione: (2025)

Near-Optimal differentially private low-rank trace regression with guaranteed private initialization
di: Zha, Mengyue
Pubblicazione: (2024)

Prediction-Powered Semi-Supervised Learning with Online Power Tuning
di: Shoham, Noa, et al.
Pubblicazione: (2025)

Learning Rate Annealing Improves Tuning Robustness in Stochastic Optimization
di: Attia, Amit, et al.
Pubblicazione: (2025)

From Contextual Combinatorial Semi-Bandits to Bandit List Classification: Improved Sample Complexity with Sparse Rewards
di: Erez, Liad, et al.
Pubblicazione: (2025)

How Free is Parameter-Free Stochastic Optimization?
di: Attia, Amit, et al.
Pubblicazione: (2024)

On the Alignment Between Supervised and Self-Supervised Contrastive Learning
di: Luthra, Achleshwar, et al.
Pubblicazione: (2025)

Scalable Principal-Agent Contract Design via Gradient-Based Optimization
di: Galanti, Tomer, et al.
Pubblicazione: (2025)

Self-Supervised Contrastive Learning is Approximately Supervised Contrastive Learning
di: Luthra, Achleshwar, et al.
Pubblicazione: (2025)