
Kyunghyun Cho
@kchonyc
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a combination of a mediocre scientist, a mediocre manager, a mediocre advisor & a mediocre physicist at @nyuniversity (@CILVRatNYU) & @PrescientDesign
Manhattan, NY
Joined June 2009
btw, it took 12 years, but i did end up having a dinner with CL last year
some absolutely not-plagiarised text snippets in my dissertations:. * "it would not have been possible for me to survive long, freezing, snowy winter of Finland without songs from four girls of 2NE1 (especially, the leader CL)."
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vibe coding continues with a @MicrosoftEdge extension: the world's most expensive, slow and inaccurate ad blocker, featuring Gemini!
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the perfect response from @GoogleDeepMind cannot be simpler: just halve the price of everything. @orf_bnw @_angie_chen please start with the Gemini Pro subscription. pretty please?.
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RT @markchen90: Try GPT-5 today!. One of our top level goals was to marry the pretraining and reasoning paradigms - this required lots of h….
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RT @iclr_conf: ICLR 2026 will take place in. 📍Rio de Janeiro, Brazil.📅 April 23–27, 2026. Save the date - see you in Rio! #ICLR2026 https:….
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RT @Daniel_J_Im: It’s been a long road. Grateful to have worked on this with @kyunghyun_cho and coauthors. What excites me most is pushin….
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RT @OpenAI: Our open models are here. Both of them.
openai.com
Advanced open-weight reasoning models to customize for any use case and run anywhere.
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RT @stephenrra: I was reminded by @kchonyc that it's been exactly 4 years to the day since we landed at @genentech. A lot has happened pers….
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RT @ABosselut: The EPFL NLP lab is looking to hire a postdoctoral researcher on the topic of designing, training, and evaluating multilingu….
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yes, yes, no theory. but, i don't think we should shy away from real(ish) problems because we can't do theory on them. paper:
openreview.net
Existing causal inference (CI) models are often restricted to data with low-dimensional confounders and singleton actions. We propose an autoregressive (AR) CI framework capable of handling complex...
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together with @Daniel_J_Im , we worked on this for a couple of years already: use an autoregressive model à la LLM to model distributions over sequencified combinatorial variables and run MC to estimate causal effects. so many possibilities with such an approach (knowledge from
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RT @thoma_gu: Yeah… this is why we usually call it “Non-autoregressive sequence generation” in early days! 👀
nar-tutorial.github.io
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