Mike Gartrell

AI researcher.

[Jean-Yves Franceschi @jy_franceschi]

Looking forward to present at #NeurIPS2023, NOLA & Paris, a unification framework for diffusion models and GANs.

It opens up the possibility to train generators with diffusion and GANs without generators!

📜 https://t.co/TRTjgFRFnW
🖥️ https://t.co/HOLfG2nTz2

[Amin Karbasi @aminkarbasi]

I think a revolution is happening in sampling (both in discrete and continuous settings). In our ICML paper, we try to better understand MCMC sampling for (non-symmetric) determinantal point processes (DPP). @insu_han @mikegartrell @dohmatobelvis https://t.co/mEpdfixdfU

[Criteo AI Lab @CriteoAILab]

❓Interested in exploring the intersection of #Bayesian inference and causal inference? Then Laplace's Causal Demon webinar series is for you! Join our next talks with Christopher Sims, Yixin Wang & Fan Li.
More information➡️https://t.co/iQDym5cOG3
@BayesianIn #machinelearning

[Criteo AI Lab @CriteoAILab]

Interested in #Bayesian causal inference for real-world #interactivesystems?
See you all at the #BCIRWIS Workshop at #KDD2021.
More info⬆️⬆️⬆️

[Criteo AI Lab @CriteoAILab]

Are you ready to explore Bayesian casual interference for real-world interactive systems?
Join the #KDD2021 workshop and submit your paper!

Deadline for papers: 🗓️May 27th
More info 👉https://t.co/hq3bfVOJ0i 👈

#MachineLearning

[Mike Gartrell @mikegartrell]

Our paper: https://t.co/TcowxotwNQ

[Mike Gartrell @mikegartrell]

Interested in a scalable approach for subset selection, with applications to recommendation, NLP, and many other areas? Check out our #ICLR2021 poster today at 10 AM - 12 PM CEST: https://t.co/SbDGm4HV3Z… ("Scalable Learning and MAP Inference for Nonsymmetric DPPs").

[Criteo AI Lab @CriteoAILab]

There is still⌛️time to join the next Laplace’s Demon webinar with @MeulenFrank!

On 🗓️April 21st we will discuss a structured way for efficient inference in probabilistic graphical models.
🎟️➡️https://t.co/Yu7is1l4pA

@BayesianIn #MachineLearning

[Criteo AI Lab @CriteoAILab]

Want to know more about Monte Carlo integration with repulsive point processes?

Join our next talk on April 7th, with Rémi Bardenet 👉 https://t.co/8ySrH1xWEk👈

@BayesianIn #MachineLearning

[Benjamin Ramm @BenjaminRamm]

Can't emphasis enough how serious this situation is for the government. Here, the organiser of the 'secret' luxury dinner parties, claims that SIX ministers (he names three of them) and the President's spokesperson, Gabriel Attal, are attendees at his soirées. #OnVeutLesNoms

[Criteo AI Lab @CriteoAILab]

📌Save your virtual seat on 🗓️April 7th for our next Laplace’s Demon webinar!

Our guest, Rémi Bardenet, will present 2 approaches to faster Monte Carlo rates using interacting particle systems.

🎟️↘️https://t.co/8ySrH1xWEk

@BayesianIn #MachineLearning

[Criteo AI Lab @CriteoAILab]

Thank you for joining our talk. If you missed it, watch the 🎥replay ➡️ https://t.co/NF8uiK5J7e

Want to join our next webinar?
On April 7, Rémi Bardenet will discuss Monte Carlo integration with repulsive point processes ➡️https://t.co/8ySrH1xWEk

@BayesianIn #MachineLearning

[Dave Keating @DaveKeating]

I keep seeing the line 'EU is having vaccine problems because it was too slow in negotiating contracts' repeated in 🇬🇧&🇺🇸 media.

I want to push back on this narrative because I think it's missing where real EU-level mistakes lie. Let's review what happened in past year (🧵1/17)

[Natasha Loder @natashaloder]

Side effects of the suspension of the AZ vaccine include severe to moderate covid-19, long-term organ damage, and death.

[Criteo AI Lab @CriteoAILab]

Are you eager😺 for our next Laplace’s Demon series talk? So are we! 🤗

Two more days till we can find more about a backfitting algorithm for large scale crossed random effects regressions. 💪
To register🎟️👉https://t.co/y9bhv0vCeH

@BayesianIn #MachineLearning

[Liva Ralaivola @LivaRalaivola]

Happy to see Criteo AI Lab researchers have 5 papers to @iclr_conf and 1 paper at #ALT-2021 (https://t.co/F8YyoQ4BXZ).
Details here:
#ICLR2021 #ALT2021 #DeepLearning #ML #Criteo

Kudos to Mike Gartrell, Elvis DOHMATOB, Sergey Iva…https://t.co/B6I9f6NuuD https://t.co/7RF8fjEZMQ

[Criteo AI Lab @CriteoAILab]

Laplace’s Demon series.

Want to know how to do MCMC sampling that is highly scalable in hierarchical, nested multilevel, and crossed-effects models?

Join our talk with Omiros Papaspiliopoulos on 🗓️Jan 27
👉https://t.co/vczKxD2xW4👈

@BayesianIn #MachineLearning

[Mike Gartrell @mikegartrell]

Congratulations to my coauthors (@insu_han, @dohmatobelvis, Jennifer Gillenwater, and Victor-Emmanuel Brunel) on our ICLR 2021 submission (https://t.co/TcowxotwNQ) that was accepted for an oral presentation!

[Elvis Dohmatob @dohmatobelvis]

Good news: Our paper on Scalable learning and MAP inference in nonsymmetric Determinantal Point Processes https://t.co/ZsJPo0WlDb has been accepted (oral) at #ICLR2021. Joint work with @mikegartrell, @insu_han, Victor-Emanuel Brunel, and Jennifer Gillenwater.

[Criteo AI Lab @CriteoAILab]

Thank you 🙏🥰for attending our Laplace’s Demon series talks. To watch our 🎥replays, discover our🔜 future talks and speakers, visit 👉 https://t.co/yBOTKmM7Vh

We wish you some great ❄️winter holidays and see you in 2021!

@BayesianIn #MachineLearning

[Criteo AI Lab @CriteoAILab]

Laplace’s Demon series
Want to find out how a pseudo-marginal approach to a Gaussian process with MCMC offers asymptotically exact inference as well as computational gains?

Join our talk and find out on Dec 16 👉https://t.co/dwoag0spDn

@BayesianIn @monterrubiok #MachineLearning