Abhradeep Guha Thakurta
28 papers · 2013–2025 · 5 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (5) π£ Hot Topic Early Bird π Interdisciplinary Bridge π§ Keyword Pioneer π Academic Marathon (12)
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Academic Marathon
(12)
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Taxonomy Completionist
(30)
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Keyword Pioneer
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Deep Specialist
(19)
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Triple Crown
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Keyword Champion
(2)
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Topic Pioneer
ποΈ
Keyword Collector
(82)
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Prolific Year
(8)
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Trend Setter
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Century Club
(28)
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Unstoppable
(9)
β
The Questioner
(2)
Conferences
NIPS (15)
ICML (6)
ICLR (5)
ALT (1)
COLT (1)
Top co-authors
Research topics
Keywords
differential privacy
(19)
stochastic gradient descent
(4)
federated learning
(3)
excess risk
(2)
private training
(2)
online learning
(2)
convex optimization
(2)
user-level privacy
(2)
regret bound
(2)
matrix factorization
(2)
lasso estimator
(2)
feature selection
(2)
privacy amplification
(2)
sample complexity
(1)
uncertainty quantification
(1)
logistic regression
(1)
model selection
(1)
algorithmic stability
(1)
l1 regularization
(1)
vc dimension
(1)
Papers
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
Privacy Amplification for Matrix Mechanisms
ICLR 2024
Correlated Noise Provably Beats Independent Noise for Differentially Private Learning
ICLR 2024
Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation
ICML 2024
Improved Differentially Private and Lazy Online Convex Optimization: Lower Regret without Smoothness Requirements
ICML 2024
Faster Differentially Private Convex Optimization via Second-Order Methods
NIPS 2023
Training Private Models That Know What They Donβt Know
NIPS 2023
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks
NIPS 2023
(Amplified) Banded Matrix Factorization: A unified approach to private training
NIPS 2023
Measuring Forgetting of Memorized Training Examples
ICLR 2023
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning
ICML 2023
Why Is Public Pretraining Necessary for Private Model Training?
ICML 2023
Multi-Task Differential Privacy Under Distribution Skew
ICML 2023
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams
NIPS 2022
When Does Differentially Private Learning Not Suffer in High Dimensions?
NIPS 2022
A Separation Result Between Data-oblivious and Data-aware Poisoning Attacks
NIPS 2021
Differentially Private Model Personalization
NIPS 2021
Privacy Amplification via Random Check-Ins
NIPS 2020
The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space
NIPS 2020
Private Stochastic Convex Optimization with Optimal Rates
NIPS 2019
Model-Agnostic Private Learning
NIPS 2018
Practical Locally Private Heavy Hitters
NIPS 2017
Nearly Optimal Private LASSO
NIPS 2015
(Near) Dimension Independent Risk Bounds for Differentially Private Learning
ICML 2014
Differentially Private Feature Selection via Stability Arguments, and the Robustness of the Lasso
COLT 2013
(Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings
NIPS 2013