822 citations · 2.4k across the 23 of their papers we have counts for
5 papers · 1 filter
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…
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…
Learning to Perform Physics Experiments via Deep Reinforcement Learning
Misha Denil, Pulkit Agrawal, Tejas D Kulkarni +3
When encountering novel objects, humans are able to infer a wide range of physical properties such as mass, friction and deformability by interacting with them in a goal driven way…
Deep Successor Reinforcement Learning
Tejas D. Kulkarni, Ardavan Saeedi, Simanta Gautam +1
Learning robust value functions given raw observations and rewards is now possible with model-free and model-based deep reinforcement learning algorithms. There is a third alternat…
Variational Particle Approximations
Ardavan Saeedi, Tejas D Kulkarni, Vikash Mansinghka +1
Approximate inference in high-dimensional, discrete probabilistic models is a central problem in computational statistics and machine learning. This paper describes discrete partic…