Pritish Kamath
30 papers · 2018–2026 · 5 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (40) 🏃 Academic Marathon · Sprint 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Silver 🐣 Hot Topic Early Bird
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Dynamic Duo
(20)
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Keyword Champion
(3)
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Triple Crown
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Deep Specialist
(13)
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Prolific Year · Productive
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Unstoppable · Bronze
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The Questioner
(4)
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Century Club · Bronze
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Renaissance Researcher
(6)
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Keyword Collector
(136)
Top co-authors
Research topics
Machine Learning
(24)
Application Areas
(14)
Optimization & Theory · Machine Learning
(12)
Security & Privacy
(11)
Differential Privacy
(10)
Mathematics & Optimization
(8)
Deep Learning
(7)
Core Methods
(7)
Optimization & Theory · Deep Learning
(7)
Artificial Intelligence
(6)
Optimization
(6)
Core AI
(5)
Learning Types
(5)
Mathematics
(3)
Computer Science
(2)
Keywords
differential privacy
(11)
stochastic gradient descent
(4)
neural network
(4)
poisson subsampling
(3)
label differential privacy
(3)
adaptive batch linear queries
(2)
gradient sparsity
(2)
dp-sgd
(2)
convex optimization
(2)
representation learning
(2)
kernel methods
(2)
privacy accounting
(2)
bias-variance tradeoff
(2)
pac learning
(2)
machine unlearning
(2)
gradient descent
(2)
statistical estimation
(2)
stochastic convex optimization
(2)
differentiable learning
(2)
sample complexity
(1)
Papers
Fixed-Parameter Tractability of Private Synthetic Data Generation
COLT 2026
Invited Open Problem: Is the Power of Deep Learning over Linear Models Inherently Distribution Dependent?
COLT 2026
Balls-and-Bins Sampling for DP-SGD
AISTATS 2025
Empirical Privacy Variance
ICML 2025
PREM: Privately Answering Statistical Queries with Relative Error
COLT 2025
Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy
ICLR 2025
Differentially Private Optimization with Sparse Gradients
NeurIPS 2024
How Private are DP-SGD Implementations?
ICML 2024
Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization
ICML 2024
LabelDP-Pro: Learning with Label Differential Privacy via Projections
ICLR 2024
Learning Neural Networks with Sparse Activations
COLT 2024
On Convex Optimization with Semi-Sensitive Features
COLT 2024
Scalable DP-SGD: Shuffling vs. Poisson Subsampling
NeurIPS 2024
On Computing Pairwise Statistics with Local Differential Privacy
NeurIPS 2023
On User-Level Private Convex Optimization
ICML 2023
Optimal Unbiased Randomizers for Regression with Label Differential Privacy
NeurIPS 2023
Regression with Label Differential Privacy
ICLR 2023
Sparsity-Preserving Differentially Private Training of Large Embedding Models
NeurIPS 2023
Ticketed Learning–Unlearning Schemes
COLT 2023
User-Level Differential Privacy With Few Examples Per User
NeurIPS 2023
Anonymized Histograms in Intermediate Privacy Models
NeurIPS 2022
Do More Negative Samples Necessarily Hurt In Contrastive Learning?
ICML 2022
Faster Privacy Accounting via Evolving Discretization
ICML 2022
Private Isotonic Regression
NeurIPS 2022
Understanding the Eluder Dimension
NeurIPS 2022
Does Invariant Risk Minimization Capture Invariance?
AISTATS 2021
On the Power of Differentiable Learning versus PAC and SQ Learning
NeurIPS 2021
Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels
ICML 2021
Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity
COLT 2020
Bayesian Inference of Temporal Task Specifications from Demonstrations
NeurIPS 2018