Pritish Kamath
28 papers · 2018–2025 · 5 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (5) π Academic Marathon (7) π§ Keyword Pioneer π Interdisciplinary Bridge π£ Hot Topic Early Bird
π§
Keyword Pioneer
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Cross-Pollinator
(8)
π
Conference Polyglot
(5)
π€
Dynamic Duo
(19)
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Triple Crown
π¬
Deep Specialist
(12)
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Keyword Champion
(2)
β‘
Prolific Year
(7)
ποΈ
Keyword Collector
(94)
β
The Questioner
(3)
π₯
Unstoppable
(6)
π
Century Club
(28)
Conferences
NIPS (11)
ICML (7)
COLT (5)
ICLR (3)
AISTATS (2)
Top co-authors
Research topics
Keywords
differential privacy
(8)
stochastic gradient descent
(3)
neural network
(3)
differentiable learning
(2)
representation learning
(2)
pac learning
(2)
kernel methods
(2)
gradient descent
(2)
gradient sparsity
(2)
convex optimization
(2)
statistical estimation
(2)
stochastic convex optimization
(2)
mean estimation
(1)
function approximation
(1)
bayesian inference
(1)
contrastive learning
(1)
causal inference
(1)
privacy-preserving learning
(1)
domain generalization
(1)
tangent kernels
(1)
Papers
PREM: Privately Answering Statistical Queries with Relative Error
COLT 2025
Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy
ICLR 2025
Empirical Privacy Variance
ICML 2025
Balls-and-Bins Sampling for DP-SGD
AISTATS 2025
How Private are DP-SGD Implementations?
ICML 2024
Differentially Private Optimization with Sparse Gradients
NIPS 2024
LabelDP-Pro: Learning with Label Differential Privacy via Projections
ICLR 2024
Scalable DP-SGD: Shuffling vs. Poisson Subsampling
NIPS 2024
On Convex Optimization with Semi-Sensitive Features
COLT 2024
Learning Neural Networks with Sparse Activations
COLT 2024
Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization
ICML 2024
Ticketed LearningβUnlearning Schemes
COLT 2023
Sparsity-Preserving Differentially Private Training of Large Embedding Models
NIPS 2023
User-Level Differential Privacy With Few Examples Per User
NIPS 2023
On Computing Pairwise Statistics with Local Differential Privacy
NIPS 2023
Optimal Unbiased Randomizers for Regression with Label Differential Privacy
NIPS 2023
Regression with Label Differential Privacy
ICLR 2023
On User-Level Private Convex Optimization
ICML 2023
Anonymized Histograms in Intermediate Privacy Models
NIPS 2022
Understanding the Eluder Dimension
NIPS 2022
Do More Negative Samples Necessarily Hurt In Contrastive Learning?
ICML 2022
Faster Privacy Accounting via Evolving Discretization
ICML 2022
Private Isotonic Regression
NIPS 2022
Does Invariant Risk Minimization Capture Invariance?
AISTATS 2021
On the Power of Differentiable Learning versus PAC and SQ Learning
NIPS 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
NIPS 2018