Saved in:
| Main Authors: | Kalinin, Nikita P., Andersson, Joel Daniel |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.17994 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Banded Square Root Matrix Factorization for Differentially Private Model Training
by: Kalinin, Nikita P., et al.
Published: (2024)
by: Kalinin, Nikita P., et al.
Published: (2024)
Beyond Square Roots: Explicit Memory-Efficient Factorization for Multi-Epoch Private Learning
by: Kalinin, Nikita P., et al.
Published: (2026)
by: Kalinin, Nikita P., et al.
Published: (2026)
Normalized Square Root: Sharper Matrix Factorization Bounds for Differentially Private Continual Counting
by: Henzinger, Monika, et al.
Published: (2025)
by: Henzinger, Monika, et al.
Published: (2025)
DP-MicroAdam: Private and Frugal Algorithm for Training and Fine-tuning
by: Hudişteanu, Mihaela, et al.
Published: (2025)
by: Hudişteanu, Mihaela, et al.
Published: (2025)
Binned Group Algebra Factorization for Differentially Private Continual Counting
by: Henzinger, Monika, et al.
Published: (2025)
by: Henzinger, Monika, et al.
Published: (2025)
Back to Square Roots: An Optimal Bound on the Matrix Factorization Error for Multi-Epoch Differentially Private SGD
by: Kalinin, Nikita P., et al.
Published: (2025)
by: Kalinin, Nikita P., et al.
Published: (2025)
Improved Accuracy for Private Continual Cardinality Estimation in Fully Dynamic Streams via Matrix Factorization
by: Andersson, Joel Daniel, et al.
Published: (2026)
by: Andersson, Joel Daniel, et al.
Published: (2026)
DP-λCGD: Efficient Noise Correlation for Differentially Private Model Training
by: Kalinin, Nikita P., et al.
Published: (2026)
by: Kalinin, Nikita P., et al.
Published: (2026)
Sampling-Free Privacy Accounting for Matrix Mechanisms under Random Allocation
by: Schuchardt, Jan, et al.
Published: (2026)
by: Schuchardt, Jan, et al.
Published: (2026)
Matrix Factorization for Practical Continual Mean Estimation Under User-Level Differential Privacy
by: Kalinin, Nikita P., et al.
Published: (2026)
by: Kalinin, Nikita P., et al.
Published: (2026)
Notes on Sampled Gaussian Mechanism
by: Kalinin, Nikita P.
Published: (2024)
by: Kalinin, Nikita P.
Published: (2024)
DP-KAN: Differentially Private Kolmogorov-Arnold Networks
by: Kalinin, Nikita P., et al.
Published: (2024)
by: Kalinin, Nikita P., et al.
Published: (2024)
Streaming Private Continual Counting via Binning
by: Andersson, Joel Daniel, et al.
Published: (2024)
by: Andersson, Joel Daniel, et al.
Published: (2024)
A Smooth Binary Mechanism for Efficient Private Continual Observation
by: Andersson, Joel Daniel, et al.
Published: (2023)
by: Andersson, Joel Daniel, et al.
Published: (2023)
Continual Release Moment Estimation with Differential Privacy
by: Kalinin, Nikita P., et al.
Published: (2025)
by: Kalinin, Nikita P., et al.
Published: (2025)
Population Risk Bounds for Kolmogorov-Arnold Networks Trained by DP-SGD with Correlated Noise
by: Wang, Puyu, et al.
Published: (2026)
by: Wang, Puyu, et al.
Published: (2026)
Decoupled Relative Learning Rate Schedules
by: Ludziejewski, Jan, et al.
Published: (2025)
by: Ludziejewski, Jan, et al.
Published: (2025)
Wave Physics-informed Matrix Factorizations
by: Tetali, Harsha Vardhan, et al.
Published: (2023)
by: Tetali, Harsha Vardhan, et al.
Published: (2023)
Training Dynamics of the Cooldown Stage in Warmup-Stable-Decay Learning Rate Scheduler
by: Dremov, Aleksandr, et al.
Published: (2025)
by: Dremov, Aleksandr, et al.
Published: (2025)
Seesaw: Accelerating Training by Balancing Learning Rate and Batch Size Scheduling
by: Meterez, Alexandru, et al.
Published: (2025)
by: Meterez, Alexandru, et al.
Published: (2025)
An Adaptive Volatility-based Learning Rate Scheduler
by: Ren, Kieran Chai Kai
Published: (2025)
by: Ren, Kieran Chai Kai
Published: (2025)
The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training
by: Schaipp, Fabian, et al.
Published: (2025)
by: Schaipp, Fabian, et al.
Published: (2025)
Learning Rate Schedules in the Presence of Distribution Shift
by: Fahrbach, Matthew, et al.
Published: (2023)
by: Fahrbach, Matthew, et al.
Published: (2023)
Scaling up the Banded Matrix Factorization Mechanism for Differentially Private ML
by: McKenna, Ryan
Published: (2024)
by: McKenna, Ryan
Published: (2024)
ScheduleFree+: Scaling Learning-Rate-Free & Schedule-Free Learning to Large Language Models
by: Defazio, Aaron
Published: (2026)
by: Defazio, Aaron
Published: (2026)
Private Rate-Constrained Optimization with Applications to Fair Learning
by: Yaghini, Mohammad, et al.
Published: (2025)
by: Yaghini, Mohammad, et al.
Published: (2025)
Faster Rates for Private Adversarial Bandits
by: Asi, Hilal, et al.
Published: (2025)
by: Asi, Hilal, et al.
Published: (2025)
Age Aware Scheduling for Differentially-Private Federated Learning
by: Lin, Kuan-Yu, et al.
Published: (2024)
by: Lin, Kuan-Yu, et al.
Published: (2024)
DPQuant: Efficient and Differentially-Private Model Training via Dynamic Quantization Scheduling
by: Gao, Yubo, et al.
Published: (2025)
by: Gao, Yubo, et al.
Published: (2025)
Cyclical Log Annealing as a Learning Rate Scheduler
by: Naveen, Philip
Published: (2024)
by: Naveen, Philip
Published: (2024)
Nonnegative Matrix Factorization through Cone Collapse
by: Nguyen, Manh, et al.
Published: (2025)
by: Nguyen, Manh, et al.
Published: (2025)
Predicting Battery Capacity Fade Using Probabilistic Machine Learning Models With and Without Pre-Trained Priors
by: Kenney, Michael J., et al.
Published: (2024)
by: Kenney, Michael J., et al.
Published: (2024)
Optimal Linear Decay Learning Rate Schedules and Further Refinements
by: Defazio, Aaron, et al.
Published: (2023)
by: Defazio, Aaron, et al.
Published: (2023)
Functional Scaling Laws in Kernel Regression: Loss Dynamics and Learning Rate Schedules
by: Li, Binghui, et al.
Published: (2025)
by: Li, Binghui, et al.
Published: (2025)
Cumulative Learning Rate Adaptation: Revisiting Path-Based Schedules for SGD and Adam
by: Atamna, Asma, et al.
Published: (2025)
by: Atamna, Asma, et al.
Published: (2025)
Dimension-free Private Mean Estimation for Anisotropic Distributions
by: Dagan, Yuval, et al.
Published: (2024)
by: Dagan, Yuval, et al.
Published: (2024)
Power Scheduler: A Batch Size and Token Number Agnostic Learning Rate Scheduler
by: Shen, Yikang, et al.
Published: (2024)
by: Shen, Yikang, et al.
Published: (2024)
Averaging Rate Scheduler for Decentralized Learning on Heterogeneous Data
by: Aketi, Sai Aparna, et al.
Published: (2024)
by: Aketi, Sai Aparna, et al.
Published: (2024)
On the Query Complexity of Training Data Reconstruction in Private Learning
by: Mukherjee, Prateeti, et al.
Published: (2023)
by: Mukherjee, Prateeti, et al.
Published: (2023)
High-Dimensional Private Linear Regression with Optimal Rates
by: Bombari, Simone, et al.
Published: (2025)
by: Bombari, Simone, et al.
Published: (2025)
Similar Items
-
Banded Square Root Matrix Factorization for Differentially Private Model Training
by: Kalinin, Nikita P., et al.
Published: (2024) -
Beyond Square Roots: Explicit Memory-Efficient Factorization for Multi-Epoch Private Learning
by: Kalinin, Nikita P., et al.
Published: (2026) -
Normalized Square Root: Sharper Matrix Factorization Bounds for Differentially Private Continual Counting
by: Henzinger, Monika, et al.
Published: (2025) -
DP-MicroAdam: Private and Frugal Algorithm for Training and Fine-tuning
by: Hudişteanu, Mihaela, et al.
Published: (2025) -
Binned Group Algebra Factorization for Differentially Private Continual Counting
by: Henzinger, Monika, et al.
Published: (2025)