• Lionel Blonde in AISTATS


    AISTATS 2019 is well under way and Lionel Blonde is there to present his work on Sample-Efficient Imitation Learning via Generative Adversarial Nets. You can check his poster Th79 at the poster session tomorrow (Thursday, April 18, 13h30-16h30). He will be happy to explain how he improves upon GAIL by reducing the sample complexity by ...

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  • New machine learning courses


    The HEG (HES-SO Geneva) opened a new Data Science branch in its Master's programe in Information Studies. Our group is responsible for the two major modules on machine learning spanning over two semesters (16 credits in total). Building a new machine learning course is a challenge we are happy to embrace.

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  • NIPS 2018


    Meet us at the NIPS workshops were we will be presenting the following works:  Sample-Efficient Imitation Learning via Generative Adversarial Nets (Lionel Blondé, Alexandros Kalousis), in the Deep RL  and in the Imitation Learning and its Challenges in Robotics workshops. Continual Classification Learning Using Generative Models (Frantzeska Lavda, Jason Ramapuram, Magda Gregorova, Alexandros Kalousis), in the Continual Learning workshop. Variational Saccading: Efficient ...

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  • The blog is live!


    Read the blog to know about the latest endeavours of the DMML group!

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  • Welcome to Ye!


    Warm welcomes to our new colleague, Ye Yuan, who started his PhD with us in September 2018 to work on our gait project. Ye has got a master's degree in Computer Science from the Institute of Computing Technology of the University of Chinese Academy of Science and has got some experience with NLP and stock ...

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  • Busy Monday, 12 November 2018


    On Monday 12/November we will host a talk of Dr. Julien Mairal, Inria, Grenoble on: "Invariance and Stability to Deformations of Deep Convolutional Representations". Later the same day, Magda will defend her thesis on "Sparse Learning for variable selection with structures and nonlinearities".

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  • Lionel to speak at SMLD2018


    Lionel Blondé will present his work on "Sample-Efficient Imitation Learning via Generative Adversarial Nets" at the 2018 edition of the Swiss Machine Learning Day.

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  • Meet Jason at BMVC2018


    Jason Ramapuram will be at BMVC2018 to talk about his new paper "A New Benchmark and Progress Toward Improved Weakly Supervised Learning"

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  • New paper for ECML PKDD 2018


    We will present our new paper on Large-scale Nonlinear Variable Selection via Kernel Random Features at ECML PKDD 2018.

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  • Our paper accepted to UAI2018


    Our paper on Structured nonlinear variable selection has been accepted to the UAI2018 conference.

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