Zico Kolter

@zicokolter

Associate professor at Carnegie Mellon University. Research focuses on machine learning, optimization, deep learning, and applications in energy systems.

Pittsburgh, PA
Vrijeme pridruživanja: ožujak 2017.

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  1. Prikvačeni tweet
    27. lip 2018.

    Excited to announce a collaboration between CMU and the Bosch Center for AI (BCAI). I'm joining BCAI as chief scientist of AI research, and they are funding my CMU group (where I'm remaining full time). Press release: My comments:

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  2. proslijedio/la je Tweet
    27. sij

    I am extremely excited to join the Artificial Intelligence lab and team! I am looking forward towards new challenges and opportunities! 2 years with M*Modal (also as a part of ) were good and productive, but it's time for new adventures!

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  3. proslijedio/la je Tweet
    24. sij

    When I invented adversarial training as a defense against adversarial examples, I focused on making it as cheap and scalable as possible. Eric and collaborators have now upgraded the original cheap version to compete with newer, more expensive versions.

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  4. proslijedio/la je Tweet
    23. sij

    Excited to hear from our confirmed speakers Seeta Peña Gangadharan at our upcoming ICLR workshop on trustworthy ML! Submit your work and join us on April 26 in Addis Ababa: Deadline Jan 31 AoE

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  5. proslijedio/la je Tweet
    15. sij

    1/ New paper on an old topic: turns out, FGSM works as well as PGD for adversarial training!* *Just avoid catastrophic overfitting, as seen in picture Paper: Code: Joint work with and to be at

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  6. proslijedio/la je Tweet

    Kolter's Team Wins First Place on Kaggle Competition with Over 2700 Teams Learn more → cc:

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  7. proslijedio/la je Tweet

    Congrats to the winners of the Outstanding New Directions Paper Award at : , chief scientist of research at , and , PhD student at Carnegie Mellon University! 🏆 More:

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  8. proslijedio/la je Tweet
    11. pro 2019.

    Congrats to our winners of the Outstanding New Directions Paper Award at @NeurIPS2019 – & from CMU and . Find the download of the full paper “Uniform Convergence may be unable to explain generalization in Deep Learning” here:

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  9. 10. pro 2019.

    Tomorrow, 12/11, I will be giving a talk at 3:15pm at the booth at . I will be covering some of the ongoing research collaborations between CMU and BCAI Pittsburgh. Come and find out about our work!

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  10. 10. pro 2019.

    Come to ’s talk at 10:05 on uniform convergence, in expo hall C! Then run over to ’s spotlight on deep equilibrium models at 10:40 in Ballroom C! My two PhD students with spotlights/orals are in different tracks in the same time slot naturally 😀.

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  11. 10. pro 2019.

    registration line, 7am alternate version...

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  12. 8. pro 2019.

    Naturally it wouldn’t be an excited tweet without a typo: talk of course is on 12/10 at 10:05. 😀

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  13. proslijedio/la je Tweet
    8. pro 2019.

    Thrilled that our paper w/ on generalization in deep learning has been selected for the Outstanding New Directions Paper Award at . Extremely grateful to the selection committee, reviewers & many others who provided useful feedback to improve our paper.

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  14. 8. pro 2019.

    Congrats to for winning the Outstanding New Directions Paper Award at ! Come see his talk this Tuesday, 9/10, at 10:05am in West Exhibition Hall C + B3. Paper available here:

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  15. 3. pro 2019.

    Excited to release our recent paper and library: easily optimize custom performance metrics (including constrained versions) using deep networks. Paper: Code:

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  16. proslijedio/la je Tweet
    4. stu 2019.

    This is cool! Instead of generic SGD, optimization layers represent the optimization of convex optimization problems directly: and the solution is itself differentiable.

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  17. proslijedio/la je Tweet
    31. lis 2019.

    Here's a 3 min video summary of our NeurIPS '19 oral paper on the generalization puzzle in deep learning. 📽️

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  18. proslijedio/la je Tweet
    31. lis 2019.

    Take a glimpse at what happened at this year’s hosted by the Bosch Center for AI and Cyber Valley

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  19. proslijedio/la je Tweet
    28. lis 2019.

    CVXPY is now differentiable. Try our PyTorch and TensorFlow layers using our package, cvxpylayers: (& see our NeurIPS paper for details )

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  20. proslijedio/la je Tweet
    28. lis 2019.
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  21. proslijedio/la je Tweet
    30. ruj 2019.

    Excited to share my first paper with : The Differentiable Cross-Entropy Method with Paper: Videos:

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