Zachary Charles
14 papers · 2018–2025 · 6 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (28) 🏃 Academic Marathon · Sprint 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Silver 🐣 Hot Topic Early Bird
🔬
Deep Specialist
(10)
🧭
Keyword Pioneer
🌈
Renaissance Researcher
(5)
❓
The Questioner
🔥
Unstoppable · Bronze
🗃️
Keyword Collector
(65)
💎
Century Club · Bronze
Top co-authors
Research topics
Machine Learning
(14)
Optimization & Theory · Machine Learning
(14)
Deep Learning
(8)
Artificial Intelligence
(7)
Learning Paradigms · Artificial Intelligence
(5)
Optimization & Theory · Deep Learning
(5)
Application Areas
(4)
Core AI
(3)
Learning Paradigms · Machine Learning
(3)
Mathematics & Optimization
(2)
Learning Types
(2)
Optimization
(2)
Computer Science
(1)
Natural Language Processing
(1)
Security & Privacy
(1)
Keywords
federated learning
(7)
distributed learning
(4)
distributed training
(3)
gradient aggregation
(2)
differential privacy
(2)
gradient descent
(2)
federated averaging
(2)
adversarial perturbation
(2)
byzantine fault tolerance
(2)
byzantine resilience
(2)
convex optimization
(1)
adversarial robustness
(1)
automatic differentiation
(1)
quadratic models
(1)
convergence analysis
(1)
data augmentation
(1)
stochastic gradient descent
(1)
gradient computation
(1)
surrogate loss
(1)
geometric analysis
(1)
Papers
Scaling Laws for Differentially Private Language Models
ICML 2025
Federated Automatic Differentiation
JMLR 2024
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy
NeurIPS 2023
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning
NeurIPS 2023
Iterated Vector Fields and Conservatism, with Applications to Federated Learning
ALT 2022
Adaptive Federated Optimization
ICLR 2021
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning
AISTATS 2021
On Large-Cohort Training for Federated Learning
NeurIPS 2021
A Geometric Perspective on the Transferability of Adversarial Directions
AISTATS 2019
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation
NeurIPS 2019
Does Data Augmentation Lead to Positive Margin?
ICML 2019
ATOMO: Communication-efficient Learning via Atomic Sparsification
NeurIPS 2018
DRACO: Byzantine-resilient Distributed Training via Redundant Gradients
ICML 2018
Stability and Generalization of Learning Algorithms that Converge to Global Optima
ICML 2018