Manuel Gomez-Rodriguez

@autreche

Human-centric machine learning at the Max Planck Institute for Software Systems.

Kaiserslautern
Vrijeme pridruživanja: srpanj 2011.

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  1. 27. sij

    If you are at at EPFL, don't miss the AI & Education track . Thrilled to join a great set of speakers to talk about the Memorize algorithm () and our on-going large-scale interventional experiment on personalized learning.

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  2. 24. sij

    My take on "Human-Centric Machine Learning: Feedback loops, Human-AI Collaboration and Strategic Behavior": . A talk/lecture on some of my recent work. Tons of opportunities for future work!

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  3. 24. sij

    Finally, we experiment with synthetic and real lending data to both illustrate our theoretical findings and show that, under strategic behavior, the policies our algorithm find do much better than deterministic threshold rules (n/n)

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  4. 24. sij

    Moreover, under no assumptions on the cost individuals pay to change their features, we develop an iterative search algorithm that is guaranteed to find locally optimal decision policies also in polynomial time (5/n)

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  5. 24. sij

    But, if the cost individuals pay to change their features satisfies a monotonicity assumption, we can narrow down the search for the optimal policy to a family of decision policies with desirable properties. This allows for a polynomial time heuristic search algorithm (4/n)

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  6. 24. sij

    Then, we first show that, in general, we cannot expect to find optimal decision policies in polynomial time under strategic behavior and there are cases in which deterministic policies are suboptimal (3/n)

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  7. 24. sij

    Individuals may use knowledge, gained by transparency, to invest effort strategically in order to receive a beneficial decision. In our work, we show that this strategic investment of effort by individuals can be cast as an optimal transport problem at a population level (2/n)

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  8. 24. sij

    Happy to share our (updated) work on optimal decision making under strategic behavior , which lies in the emerging (super exciting) field of strategic machine learning. This work aims to find decision policies that lead individuals to self-improvement (1/n)

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  9. proslijedio/la je Tweet
    9. sij

    There's an important difference between decisions and predictions, especially when it comes to fairness. Check out our paper "Fair Decisions Despite Imperfect Predictions" with Isabel Valera

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  10. 6. pro 2019.

    Thoughtful, nuanced, and well written article on algorithmic bias vs human bias:

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  11. 28. stu 2019.

    If you go to , consider attending our workshop on human-centric ML , we have a terrific set of invited speakers (, Deirdre Mulligan, , Finale Doshi-Velez, ), contributed papers and panels!

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    30. lis 2019.

    Announcing an exciting new conference: "Foundations of Responsible Computing" (FORC). Here is the call for papers: . The deadline is February 11, and (if I can say so myself :-) it has an absolutely amazing steering and program committee. 1/

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  13. 25. lis 2019.

    Great collaboration on a problem in personalized education! New machine learning algorithm, theory, and user study on spaced repetition.

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    22. lis 2019.

    If you're confused by all the "Robot solves Rubik's cube" reporting / tweeting, this is a good, balanced read: Kudo's to BBC for noting the paper / research has not yet been peer-reviewed. 1/2

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  15. 15. lis 2019.

    The Max Planck Institutes for Informatics (Saarbruecken), Software Systems (Saarbruecken and Kaiserslautern), and Security and Privacy (Bochum) invite applications for tenure-track faculty in all areas of computer science!

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  16. proslijedio/la je Tweet

    One of the most effective things a government can do to foster an ecosystem of AI startups is to have a National Open Data Strategy. Put all government data behind APIs, sponsor the creation of high-quality public datasets in various domains relevant to local markets & language.

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  17. 12. ruj 2019.

    "Can a social media user guess what her followers want?" In our new paper, we address this question from a theoretical perspective and then provide practical algorithms to detect strategic posting

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  18. 9. ruj 2019.

    We are starting to developed machine learning models especially optimized to work together with humans, under different level of automation. Check out regression under human assistance: . Many more to come!

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  19. 3. ruj 2019.

    Your NeurIPS submission didn’t get it? Consider submitting a short version to the NeurIPS workshop on human-centric ML!

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  20. 14. kol 2019.

    We have posted the CfPs for the NeurIPS workshops on temporal point processes () and human-centric ML () which I help organizing! Consider submitting and spread the voice!

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