Arun Ganesh
13 papers · 2020–2025 · 5 venues · across top CS/AI venues
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Achievements
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🏃 Academic Marathon · Sprint 🌉 Interdisciplinary Bridge · Silver 🗺️ Taxonomy Completionist (17) 🌍 Conference Polyglot · Silver 🗃️ Keyword Collector (52)
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The Questioner
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Prolific Year · Productive
💎
Century Club · Bronze
Venues
Top co-authors
Research topics
Machine Learning
(11)
Optimization & Theory
(9)
Application Areas
(7)
Artificial Intelligence
(6)
Mathematics & Optimization
(4)
Security & Privacy
(4)
Optimization
(4)
Core AI
(3)
Learning Paradigms
(3)
Deep Learning
(2)
Bayesian & Probabilistic · Machine Learning
(2)
Differential Privacy
(2)
Privacy
(2)
Bayesian & Probabilistic · Artificial Intelligence
(1)
Core Methods
(1)
Keywords
differential privacy
(12)
privacy amplification
(3)
matrix mechanism
(2)
matrix factorization
(2)
exponential mechanism
(2)
linear regression
(2)
private training
(2)
private model training
(2)
stochastic gradient descent
(2)
transfer learning
(1)
renyi divergence
(1)
markov chain monte carlo
(1)
variance reduction
(1)
convex loss
(1)
non-convex optimization
(1)
loss landscape
(1)
logistic regression
(1)
newton's method
(1)
mirror descent
(1)
distribution shift
(1)
Papers
It’s My Data Too: Private ML for Datasets with Multi-User Training Examples
ICML 2025
Near-Exact Privacy Amplification for Matrix Mechanisms
ICLR 2025
Near-Optimal Rates for O(1)-Smooth DP-SCO with a Single Epoch and Large Batches
ALT 2025
The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD
ICLR 2025
Correlated Noise Provably Beats Independent Noise for Differentially Private Learning
ICLR 2024
Privacy Amplification for Matrix Mechanisms
ICLR 2024
(Amplified) Banded Matrix Factorization: A unified approach to private training
NeurIPS 2023
Faster Differentially Private Convex Optimization via Second-Order Methods
NeurIPS 2023
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks
NeurIPS 2023
Universality of Langevin Diffusion for Private Optimization, with Applications to Sampling from Rashomon Sets
COLT 2023
Why Is Public Pretraining Necessary for Private Model Training?
ICML 2023
Public Data-Assisted Mirror Descent for Private Model Training
ICML 2022
Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC
NeurIPS 2020