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  1. 12. pro 2019.

    Efficiently learning Fourier sparse set functions: spotlight at (4:10pm W Ballroom C; poster 44) with Andisheh Amrollahi, Amir Zandieh and

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  2. 12. pro 2019.

    Stochastic bandits allowing uncertainty about the observed context: poster 44 with Johannes Kirschner

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  3. 11. pro 2019.

    A domain agnostic measure for monitoring and evaluating GANs: poster 135 with Paulina Grnarova, Kfir Levy, Aurelien Lucchi, Nathanael Perraudin, and Thomas Hofmann

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  4. 11. pro 2019.

    Fast learning in games via opportunistic hallucination: poster 6 with Pier Giuseppe Sessa, and Maryam Kamgarpour

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    11. pro 2019.

    Adaptive decision making through submodularity at (East Exhibition Hall B + C, #164). Marko Mitrovic, , Moran Feldman,

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  6. 11. pro 2019.

    Sequential decision making with submodularity at , and applications to machine teaching (posters 142 and 164), with et al

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  7. 10. pro 2019.

    New results on safe exploration for Bayesian optimization, active learning etc. Poster 197

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    Alhussein Fawzi, researcher, will talk about recent advances in at Carving Through Data, a introductory course to , combining & March 9-13, 2020

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  9. 27. stu 2019.

    We have open postdoctoral fellowship positions in the Foundations of Data Science program at .

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    13. stu 2019.
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    25. lis 2019.

    This work was led by Adish Singla's group at MPI-SWS, with Anette Hunziker as first author: Also joint work with , , , Pietro Perona, and Manuel Gomez-Rodriguez. (5/5)

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  12. 2. lis 2019.
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    We are happy to announce the 2nd edition of Carving Through Data, a introductory course to , combining & , will take place in March 2020 in !

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  14. 12. lip 2019.

    Scaling safe Bayesian optimization to high dimensions via one-dimensional subspaces. Both local and global convergence guarantees. Experiments on the SwissFEL free electron laser. (Poster 147, ) Great collaboration with

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  15. 12. lip 2019.

    Adapting structured importance sampling distributions for stochastic optimization online using ideas from bandits, with applications to clustering and RL (Poster 157, )

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  16. 11. lip 2019.

    Training generative models where data and samples can be in different spaces. Why and how? (Poster 173, )

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  17. 11. lip 2019.

    Fitting stochastic differential equations to data? See our paper today (Talk 11:40 Room 101, Poster 216, )

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  18. 11. lip 2019.

    We use continuous submodular optimization to address non-convex problems in variational inference in our paper (Poster 98, )

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    10. lip 2019.

    # AReS and MaRS – Adversarial and MMD-Minimizing Regression for SDEs Using GPs, we efficiently estimate drift and diffusion parameters without discretisation. 🗣 Wed 11:40 @ Room 101 📜 Wed 18:30 @ Pacific Ballroom #216 1st ✍ Gab Abbati

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    21. svi 2019.

    Our paper ‘Learning Generative Models across Incomparable Spaces' is finally on arXiv. By learning distributions relationally rather than absolutely, we disentangle data and generator space! Joint work with , & Stefanie Jegelka.

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