747 citations · 751 across the 3 of their papers we have counts for
6 papers
Marginal Release Under Local Differential Privacy
Tejas Kulkarni, Graham Cormode, Divesh Srivastava
Many analysis and machine learning tasks require the availability of marginal statistics on multidimensional datasets while providing strong privacy guarantees for the data subject…
Constrained Differential Privacy for Count Data
Graham Cormode, Tejas Kulkarni, Divesh Srivastava
Concern about how to aggregate sensitive user data without compromising individual privacy is a major barrier to greater availability of data. The model of differential privacy has…
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…
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
Tejas D. Kulkarni, Karthik R. Narasimhan, Ardavan Saeedi +1
Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. The primary difficulty arises due to insufficient e…
Understanding Visual Concepts with Continuation Learning
William F. Whitney, Michael Chang, Tejas Kulkarni +1
We introduce a neural network architecture and a learning algorithm to produce factorized symbolic representations. We propose to learn these concepts by observing consecutive fram…
Deep Convolutional Inverse Graphics Network
Tejas D. Kulkarni, Will Whitney, Pushmeet Kohli +1
This paper presents the Deep Convolution Inverse Graphics Network (DC-IGN), a model that learns an interpretable representation of images. This representation is disentangled with…