Xinyi Chen

61 posts

Xinyi Chen

Xinyi Chen

@XinyiChen2

Katılım Eylül 2012
175 Takip Edilen635 Takipçiler
Xinyi Chen
Xinyi Chen@XinyiChen2·
@hugo_larochelle Thank you so much for being an incredible mentor, and I’m excited for your next venture! Looking forward to hearing more about it!
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Hugo Larochelle
Hugo Larochelle@hugo_larochelle·
Today is my last day at Google. I started over 8 years ago, with a mandate to build a team doing bleeding edge AI research from Montreal, in what would be the first big tech AI research lab in the city. These years led to countless amazing scientific contributions from my team, to several initiatives nurturing the Montreal AI ecosystem, and to many new invaluable friendships across the globe at Google. It is with a heavy heart that I say goodbye, but I know I’m leaving behind an exceptionally strong Google DeepMind group in Montreal for which its best accomplishments are still ahead. There are too many people to thank, but I can’t pass on thanking Samy Bengio and @JeffDean who first believed in me and the opportunity of building a research lab in Montreal. I’m still working on determining the details of my next chapter, but certainly it will be grounded in my continuing motivation to leverage and make the most out of our enormous and talented local AI ecosystem.
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Xinyi Chen
Xinyi Chen@XinyiChen2·
Thank you @max_simchowitz for the shoutout! Making ML more efficient by reasoning about dynamical systems is a really exciting direction, and I look forward to advancing more in this space!
Max Simchowitz@max_simchowitz

As someone who loves dynamical systems and control, I've been really excited to see @XinyiChen2 and @HazanPrinceton 's recent papers making control work for deep learning! Very cool insights, both on the architecture and optimization side. I encourage you to check them out! Meta Optimization: arxiv.org/abs/2301.07902 Spectral Transformers: arxiv.org/abs/2312.06837

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Xinyi Chen
Xinyi Chen@XinyiChen2·
Together with @HazanPrinceton, Cong, @Zanette_ai, and Nati, we are organizing a long program on reinforcement learning and control at @IMSI_Institute! Join us for workshops on frontiers of online/offline RL, control, multi-agent RL, and opportunities to present your research. Application to be a research member is now open with a priority deadline of 1/10: imsi.institute/activities/the…
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Max Simchowitz
Max Simchowitz@max_simchowitz·
🚨 @SCSatCMU PhD applications close tomorrow, December 11, at 3:00pm ET! I’m actively recruiting masters and PhD students interested in the theory and practice of decision making with generative models, especially for robotics, RL and world models! CMU is one of the most exciting and forward-thinking institutions in AI. So if you’re itching to know what will happen after this “scale is all you need” phase of ML and Robotics research, join us for a PhD and help build what comes next! 🧵(1/n)
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Max Simchowitz
Max Simchowitz@max_simchowitz·
Want to learn about the math behind robot learning? I'll be presenting an invited talk on "Provable Guarantees for Generative Behavior Cloning" at 11:55am CEST at the 2024 ICML Workshop on Reinforcement Learning at Control (link in 🧵) icml.cc/virtual/2024/w…
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Angelica Chen
Angelica Chen@_angie_chen·
I'll be at @icmlconf next week! Giving a plenary talk at the HiLD workshop and an oral on our recent paper (arxiv.org/abs/2405.19534) at the MHFAIA workshop! Pls reach out to chat if you're also interested in any of these topics! 😊
Angelica Chen tweet mediaAngelica Chen tweet media
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Elad Hazan
Elad Hazan@HazanPrinceton·
Excited w. our first research in AI safety & alignment: A game theoretic approach for AI safety via debate: arxiv.org/abs/2312.04792 This is a collaboration with my student @XinyiChen2 , our alumna @_angie_chen from NYU, and Dean Foster:
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Xinyi Chen
Xinyi Chen@XinyiChen2·
w. @HazanPrinceton: Online control for meta-optimization (Wed 5PM, #2023) twitter.com/XinyiChen2/sta… We’re looking for student researchers to explore more application of this method! Please reach out if you're interested.
Xinyi Chen@XinyiChen2

Optimizer tuning can be manual and resource-intensive. Can we learn the best optimizer automatically with guarantees? With @HazanPrinceton, we give new provable methods for learning optimizers using a control approach. Excited about this result! buff.ly/3IoMOkN (1/n)

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Xinyi Chen
Xinyi Chen@XinyiChen2·
At NeurIPS 2023 until next Sunday! Excited to reconnect with friends, meet new ones, and chat about optimization/online control. I’ll be co-presenting Meta-optimization and Sketchy, details below:
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