:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	McMahan, H. Brendan, Xu, Zheng, Zhang, Yanxiang
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2408.08868
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

On Design Principles for Private Adaptive Optimizers
by: Ganesh, Arun, et al.
Published: (2025)

An Inversion Theorem for Buffered Linear Toeplitz (BLT) Matrices and Applications to Streaming Differential Privacy
by: McMahan, H. Brendan, et al.
Published: (2025)

Federated Learning in Practice: Reflections and Projections
by: Daly, Katharine, et al.
Published: (2024)

Deterministic Policies for Constrained Reinforcement Learning in Polynomial Time
by: McMahan, Jeremy
Published: (2024)

Polynomial-Time Approximability of Constrained Reinforcement Learning
by: McMahan, Jeremy
Published: (2025)

It's My Data Too: Private ML for Datasets with Multi-User Training Examples
by: Ganesh, Arun, et al.
Published: (2025)

Can Public Large Language Models Help Private Cross-device Federated Learning?
by: Wang, Boxin, et al.
Published: (2023)

Anytime-Constrained Equilibria in Polynomial Time
by: McMahan, Jeremy
Published: (2024)

Anytime-Constrained Reinforcement Learning
by: McMahan, Jeremy, et al.
Published: (2023)

Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy
by: Koloskova, Anastasia, et al.
Published: (2023)

If You Don't Understand It, Don't Use It: Eliminating Trojans with Filters Between Layers
by: Hernandez, Adriano
Published: (2024)

One-shot Empirical Privacy Estimation for Federated Learning
by: Andrew, Galen, et al.
Published: (2023)

Correlated Noise Mechanisms for Differentially Private Learning
by: Pillutla, Krishna, et al.
Published: (2025)

Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy
by: Dvijotham, Krishnamurthy, et al.
Published: (2024)

Optimal Attack and Defense for Reinforcement Learning
by: McMahan, Jeremy, et al.
Published: (2023)

Roping in Uncertainty: Robustness and Regularization in Markov Games
by: McMahan, Jeremy, et al.
Published: (2024)

xAI-Drop: Don't Use What You Cannot Explain
by: De Luca, Vincenzo Marco, et al.
Published: (2024)

Cognitive Energy Modeling for Neuroadaptive Human-Machine Systems using EEG and WGAN-GP
by: Sattiraju, Sriram, et al.
Published: (2026)

Motion-Based User Identification across XR and Metaverse Applications by Deep Classification and Similarity Learning
by: Schach, Lukas, et al.
Published: (2025)

Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs
by: Johnson, Daniel D., et al.
Published: (2024)

JAX-Privacy: A library for differentially private machine learning
by: McKenna, Ryan, et al.
Published: (2026)

Don't Freeze, Don't Crash: Extending the Safe Operating Range of Neural Navigation in Dense Crowds
by: Zhang, Jiefu, et al.
Published: (2026)

Fine-Tuning Large Language Models with User-Level Differential Privacy
by: Charles, Zachary, et al.
Published: (2024)

Position: Don't Use the CLT in LLM Evals With Fewer Than a Few Hundred Datapoints
by: Bowyer, Sam, et al.
Published: (2025)

Data Poisoning to Fake a Nash Equilibrium in Markov Games
by: Wu, Young, et al.
Published: (2023)

Efficient Language Model Architectures for Differentially Private Federated Learning
by: Ro, Jae Hun, et al.
Published: (2024)

Don't Use the Names Students Want
by: Rebecca Weaver
Published: (2025)

Optimally Installing Strict Equilibria
by: McMahan, Jeremy, et al.
Published: (2025)

Prompt Public Large Language Models to Synthesize Data for Private On-device Applications
by: Wu, Shanshan, et al.
Published: (2024)

How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy
by: Ponomareva, Natalia, et al.
Published: (2025)

Inception: Efficiently Computable Misinformation Attacks on Markov Games
by: McMahan, Jeremy, et al.
Published: (2024)

Shared Autonomy with IDA: Interventional Diffusion Assistance
by: McMahan, Brandon J., et al.
Published: (2024)

Transformers Don't In-Context Learn Least Squares Regression
by: Hill, Joshua, et al.
Published: (2025)

Don't Trade Off Safety: Diffusion Regularization for Constrained Offline RL
by: Guo, Junyu, et al.
Published: (2025)

Do's and Don'ts: Learning Desirable Skills with Instruction Videos
by: Kim, Hyunseung, et al.
Published: (2024)

Don't Forget Imagination!
by: Vityaev, Evgenii E., et al.
Published: (2025)

The Full-scale Assembly Simulation Testbed (FAST) Dataset
by: Moore, Alec G., et al.
Published: (2024)

Aggregating Data for Optimal and Private Learning
by: Agarwal, Sushant, et al.
Published: (2024)

Position: Don't be Afraid of Over-Smoothing And Over-Squashing
by: Kormann, Niklas, et al.
Published: (2026)

When Models Don't Collapse: On the Consistency of Iterative MLE
by: Barzilai, Daniel, et al.
Published: (2025)