Meisam Razaviyayn
27 papers · 2014–2025 · 7 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (43) π Academic Marathon Β· Half π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Bronze π£ Hot Topic Early Bird
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Deep Specialist
(18)
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Prolific Year Β· Productive
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(135)
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Century Club Β· Bronze
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Renaissance Researcher
(5)
Top co-authors
Research topics
Machine Learning
(26)
Optimization & Theory
(23)
Artificial Intelligence
(9)
Application Areas
(9)
Core AI
(7)
Deep Learning
(6)
Mathematics & Optimization
(5)
Optimization
(4)
Security & Privacy
(3)
Core Methods
(3)
Differential Privacy
(3)
Learning Types
(3)
Mathematics
(3)
Natural Language Processing
(2)
Learning Paradigms
(2)
Keywords
differential privacy
(9)
non-convex optimization
(6)
stochastic optimization
(3)
attribution method
(3)
neural network
(3)
large language model
(2)
min-max optimization
(2)
renyi correlation
(2)
diffusion model
(2)
zeroth-order optimization
(2)
federated learning
(2)
convex optimization
(2)
empirical risk minimization
(2)
explainable ai
(2)
feature attribution
(2)
gradient descent
(2)
stochastic gradient descent
(2)
convergence analysis
(2)
integrated gradient
(2)
feature selection
(1)
Papers
Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models
ICLR 2025
DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction
ICLR 2025
Four Axiomatic Characterizations of the Integrated Gradients Attribution Method
JMLR 2025
On the Inherent Privacy of Zeroth-Order Projected Gradient Descent
AISTATS 2025
Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence
ICML 2025
Synthetic Text Generation for Training Large Language Models via Gradient Matching
ICML 2025
Differentially Private Next-Token Prediction of Large Language Models
NAACL 2024
DOPPLER: Differentially Private Optimizers with Low-pass Filter for Privacy Noise Reduction
NeurIPS 2024
f-FERM: A Scalable Framework for Robust Fair Empirical Risk Minimization
ICLR 2024
Neural Network-Based Score Estimation in Diffusion Models: Optimization and Generalization
ICLR 2024
Optimal Differentially Private Model Training with Public Data
ICML 2024
A Unifying Framework to the Analysis of Interaction Methods using Synergy Functions
ICML 2023
Improving Adversarial Robustness via Joint Classification and Multiple Explicit Detection Classes
AISTATS 2023
Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses
ICLR 2023
Private Non-Convex Federated Learning Without a Trusted Server
AISTATS 2023
Private Stochastic Optimization with Large Worst-Case Lipschitz Parameter: Optimal Rates for (Non-Smooth) Convex Losses and Extension to Non-Convex Losses
ALT 2023
Stochastic Differentially Private and Fair Learning
ICLR 2023
A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions
ICML 2022
Alternating Direction Method of Multipliers for Quantization
AISTATS 2021
Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems
NeurIPS 2020
RΓ©nyi Fair Inference
ICLR 2020
Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods
NeurIPS 2019
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks
ICML 2018
On the Convergence and Robustness of Training GANs with Regularized Optimal Transport
NeurIPS 2018
On Optimal Generalizability in Parametric Learning
NeurIPS 2017
Discrete RΓ©nyi Classifiers
NeurIPS 2015
Parallel Successive Convex Approximation for Nonsmooth Nonconvex Optimization
NeurIPS 2014