822 citations · 2.4k across the 23 of their papers we have counts for
24 papers
Practical Differentially Private Hyperparameter Tuning with Subsampling
Antti Koskela, Tejas Kulkarni
Tuning the hyperparameters of differentially private (DP) machine learning (ML) algorithms often requires use of sensitive data and this may leak private information via hyperparam…
Locally Differentially Private Bayesian Inference
Tejas Kulkarni, Joonas Jälkö, Samuel Kaski +1
In recent years, local differential privacy (LDP) has emerged as a technique of choice for privacy-preserving data collection in several scenarios when the aggregator is not trustw…
Representation Matters: Improving Perception and Exploration for Robotics
Markus Wulfmeier, Arunkumar Byravan, Tim Hertweck +8
Projecting high-dimensional environment observations into lower-dimensional structured representations can considerably improve data-efficiency for reinforcement learning in domain…
Differentially Private Bayesian Inference for Generalized Linear Models
Tejas Kulkarni, Joonas Jälkö, Antti Koskela +2
Generalized linear models (GLMs) such as logistic regression are among the most widely used arms in data analyst's repertoire and often used on sensitive datasets. A large body of…
Unsupervised Doodling and Painting with Improved SPIRAL
John F. J. Mellor, Eunbyung Park, Yaroslav Ganin +7
We investigate using reinforcement learning agents as generative models of images (extending arXiv:1804.01118). A generative agent controls a simulated painting environment, and is…
Private Protocols for U-Statistics in the Local Model and Beyond
James Bell, Aurélien Bellet, Adrià Gascón +1
In this paper, we study the problem of computing -statistics of degree , i.e., quantities that come in the form of averages over pairs of data points, in the local model of d…