:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Flemings, James, Gan, Haosheng, Li, Hongyi, Razaviyayn, Meisam, Annavaram, Murali
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2501.19287
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Differentially Private Next-Token Prediction of Large Language Models
by: Flemings, James, et al.
Published: (2024)

Adaptively Private Next-Token Prediction of Large Language Models
by: Flemings, James, et al.
Published: (2024)

Differentially Private Knowledge Distillation via Synthetic Text Generation
by: Flemings, James, et al.
Published: (2024)

Memory-Efficient Differentially Private Training with Gradient Random Projection
by: Mulrooney, Alex, et al.
Published: (2025)

Output Perturbation for Differentially Private Convex Optimization: Faster and More General
by: Lowy, Andrew, et al.
Published: (2021)

PrivacySIM: Evaluating LLM Simulation of User Privacy Behavior
by: Flemings, James, et al.
Published: (2026)

Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses
by: Lowy, Andrew, et al.
Published: (2021)

Optimal Differentially Private Model Training with Public Data
by: Lowy, Andrew, et al.
Published: (2023)

Private Stochastic Optimization With Large Worst-Case Lipschitz Parameter
by: Lowy, Andrew, et al.
Published: (2022)

DOPPLER: Differentially Private Optimizers with Low-pass Filter for Privacy Noise Reduction
by: Zhang, Xinwei, et al.
Published: (2024)

Differentially Private Retrieval-Augmented Generation
by: Tang, Tingting, et al.
Published: (2026)

A Stochastic Optimization Framework for Private and Fair Learning From Decentralized Data
by: Gupta, Devansh, et al.
Published: (2024)

Estimating Privacy Leakage of Augmented Contextual Knowledge in Language Models
by: Flemings, James, et al.
Published: (2024)

DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction
by: Zhang, Xinwei, et al.
Published: (2024)

ICPL: Few-shot In-context Preference Learning via LLMs
by: Yu, Chao, et al.
Published: (2024)

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts
by: Li, Zeman, et al.
Published: (2025)

Sampling and Loss Weights in Multi-Domain Training
by: Salmani, Mahdi, et al.
Published: (2025)

f-FERM: A Scalable Framework for Robust Fair Empirical Risk Minimization
by: Baharlouei, Sina, et al.
Published: (2023)

Neural Network-Based Score Estimation in Diffusion Models: Optimization and Generalization
by: Han, Yinbin, et al.
Published: (2024)

Zero-shot and Few-shot Generation Strategies for Artificial Clinical Records
by: Frayling, Erlend, et al.
Published: (2024)

Nested Learning: The Illusion of Deep Learning Architectures
by: Behrouz, Ali, et al.
Published: (2025)

On the Inherent Privacy of Zeroth Order Projected Gradient Descent
by: Gupta, Devansh, et al.
Published: (2025)

Policy Gradient Converges to the Globally Optimal Policy for Nearly Linear-Quadratic Regulators
by: Han, Yinbin, et al.
Published: (2023)

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence
by: Han, Yinbin, et al.
Published: (2024)

Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
by: Teufel, Felix, et al.
Published: (2025)

Edge Private Graph Neural Networks with Singular Value Perturbation
by: Tang, Tingting, et al.
Published: (2024)

Revisiting Chain-of-Thought Prompting: Zero-shot Can Be Stronger than Few-shot
by: Cheng, Xiang, et al.
Published: (2025)

MPC-Pipe: an Efficient Pipeline Scheme for Secure Multi-party Machine Learning Inference
by: Wang, Yongqin, et al.
Published: (2022)

It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization
by: Behrouz, Ali, et al.
Published: (2025)

Less is More: Convergence Benefits of Fewer Data Weight Updates over Longer Horizon
by: Das, Rudrajit, et al.
Published: (2026)

Medical Coding with Biomedical Transformer Ensembles and Zero/Few-shot Learning
by: Ziletti, Angelo, et al.
Published: (2022)

Tradeoffs between convergence rate and noise amplification for momentum-based accelerated optimization algorithms
by: Mohammadi, Hesameddin, et al.
Published: (2022)

Inductive Graph Few-shot Class Incremental Learning
by: Li, Yayong, et al.
Published: (2024)

LRD-MPC: Efficient MPC Inference through Low-rank Decomposition
by: Tang, Tingting, et al.
Published: (2026)

Early Stopping for Large Reasoning Models via Confidence Dynamics
by: Hosseini, Parsa, et al.
Published: (2026)

Synthetic Text Generation for Training Large Language Models via Gradient Matching
by: Nguyen, Dang, et al.
Published: (2025)

Learning to Learn for Few-shot Continual Active Learning
by: Ho, Stella, et al.
Published: (2023)

Model Diffusion for Certifiable Few-shot Transfer Learning
by: Rezk, Fady, et al.
Published: (2025)

Enhancing Unsupervised Graph Few-shot Learning via Set Functions and Optimal Transport
by: Liu, Yonghao, et al.
Published: (2025)

Adaptive Verifiable Coded Computing: Towards Fast, Secure and Private Distributed Machine Learning
by: Tang, Tingting, et al.
Published: (2021)