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Tejas Kulkarni

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Practical Differentially Private Hyperparameter Tuning with Subsampling

Jan 27, 2023
Antti Koskela, Tejas Kulkarni

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Locally Differentially Private Bayesian Inference

Oct 27, 2021
Tejas Kulkarni, Joonas Jälkö, Samuel Kaski, Antti Honkela

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Differentially Private Bayesian Inference for Generalized Linear Models

Nov 09, 2020
Tejas Kulkarni, Joonas Jälkö, Antti Koskela, Samuel Kaski, Antti Honkela

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Representation Matters: Improving Perception and Exploration for Robotics

Nov 03, 2020
Markus Wulfmeier, Arunkumar Byravan, Tim Hertweck, Irina Higgins, Ankush Gupta, Tejas Kulkarni, Malcolm Reynolds, Denis Teplyashin, Roland Hafner, Thomas Lampe, Martin Riedmiller

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Private Protocols for U-Statistics in the Local Model and Beyond

Oct 09, 2019
James Bell, Aurélien Bellet, Adrià Gascón, Tejas Kulkarni

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Unsupervised Doodling and Painting with Improved SPIRAL

Oct 02, 2019
John F. J. Mellor, Eunbyung Park, Yaroslav Ganin, Igor Babuschkin, Tejas Kulkarni, Dan Rosenbaum, Andy Ballard, Theophane Weber, Oriol Vinyals, S. M. Ali Eslami

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Unsupervised Learning of Object Keypoints for Perception and Control

Jun 19, 2019
Tejas Kulkarni, Ankush Gupta, Catalin Ionescu, Sebastian Borgeaud, Malcolm Reynolds, Andrew Zisserman, Volodymyr Mnih

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Generating Diverse Programs with Instruction Conditioned Reinforced Adversarial Learning

Dec 03, 2018
Aishwarya Agrawal, Mateusz Malinowski, Felix Hill, Ali Eslami, Oriol Vinyals, Tejas Kulkarni

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Unsupervised Control Through Non-Parametric Discriminative Rewards

Nov 28, 2018
David Warde-Farley, Tom Van de Wiele, Tejas Kulkarni, Catalin Ionescu, Steven Hansen, Volodymyr Mnih

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