Kunal Talwar
37 papers · 2015–2025 · 4 conferences · across top CS/AI conferences
Achievements
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(15)
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(2)
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(16)
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(37)
Conferences
NIPS (16)
ICML (14)
COLT (4)
ICLR (3)
Top co-authors
Research topics
Keywords
differential privacy
(15)
regret bound
(6)
federated learning
(5)
stochastic gradient descent
(5)
online learning
(4)
convex optimization
(3)
frequency estimation
(3)
mean estimation
(3)
expert prediction
(3)
langevin dynamics
(2)
heterogeneous datum
(2)
local differential privacy
(2)
multi-armed bandit
(2)
online algorithm
(2)
online convex optimization
(2)
renyi divergence
(2)
stochastic convex optimization
(2)
optimal transport
(2)
semidefinite programming
(1)
bayesian learning
(1)
Papers
Improved Sample Complexity for Private Nonsmooth Nonconvex Optimization
ICML 2025
Adaptive Batch Size for Privately Finding Second-Order Stationary Points
ICLR 2025
Local Pan-privacy for Federated Analytics
ICML 2025
Faster Rates for Private Adversarial Bandits
ICML 2025
Instance-Optimal Private Density Estimation in the Wasserstein Distance
NIPS 2024
Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages
ICML 2024
Private Online Learning via Lazy Algorithms
NIPS 2024
Private and Personalized Frequency Estimation in a Federated Setting
NIPS 2024
Private Online Prediction from Experts: Separations and Faster Rates
COLT 2023
Fast Optimal Locally Private Mean Estimation via Random Projections
NIPS 2023
Resolving the Mixing Time of the Langevin Algorithm to its Stationary Distribution for Log-Concave Sampling
COLT 2023
Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime
ICML 2023
Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering
ICML 2022
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise
NIPS 2022
Optimal Algorithms for Mean Estimation under Local Differential Privacy
ICML 2022
Private frequency estimation via projective geometry
ICML 2022
FLAIR: Federated Learning Annotated Image Repository
NIPS 2022
Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss
NIPS 2022
Mean Estimation with User-level Privacy under Data Heterogeneity
NIPS 2022
Lossless Compression of Efficient Private Local Randomizers
ICML 2021
Characterizing Structural Regularities of Labeled Data in Overparameterized Models
ICML 2021
Private Adaptive Gradient Methods for Convex Optimization
ICML 2021
Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry
ICML 2021
Stochastic Optimization with Laggard Data Pipelines
NIPS 2020
Faster Differentially Private Samplers via RΓ©nyi Divergence Analysis of Discretized Langevin MCMC
NIPS 2020
On the Error Resistance of Hinge-Loss Minimization
NIPS 2020
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses
NIPS 2020
Semi-Cyclic Stochastic Gradient Descent
ICML 2019
Better Algorithms for Stochastic Bandits with Adversarial Corruptions
COLT 2019
Private Stochastic Convex Optimization with Optimal Rates
NIPS 2019
Computational Separations between Sampling and Optimization
NIPS 2019
Online Linear Quadratic Control
ICML 2018
Adversarially Robust Generalization Requires More Data
NIPS 2018
Scalable Private Learning with PATE
ICLR 2018
Learning Differentially Private Recurrent Language Models
ICLR 2018
Online learning over a finite action set with limited switching
COLT 2018
Nearly Optimal Private LASSO
NIPS 2015