AI&cyber research @microsoft, adjunct professor @Georgetown, co-founder @CamlisOrg. alum @wlunews, @UTAustin, @sfiscience. monoids, fixies, yes and.
Joined April 2014
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Keegan Hines retweeted
The #CAMLIS2023 Call for Papers link is now live! Submit your full length paper or extended abstract by Friday, August 4th to be considered for this year's conference! Learn more and submit here: lnkd.in/giYqtuhn
#LLMs #Analytics #Statistics #DataScience #infosec
Registration is open for CAMLIS 2022!
Two days of awesome talks on Cyber+ML and featuring two keynotes this year from Amanda Rousseau and Mikel Rodriguez.
Join us in Arlington or join the live stream for free.
Register now, limits spots for in-person!
camlis.org/
Keegan Hines retweeted
Oldies but goldies: N Metropolis, A Rosenbluth, M Rosenbluth, A Teller, E Teller, Equation of State Calculations by Fast Computing Machines, 1953. The (wrongly attributed …) Metropolis-Hasting algorithm samples from a density known only up to a constant. en.wikipedia.org/wiki/Equati…
Today @karthik6d and @Sahil1V are presenting at Ray Summit, sharing our work on Counterfactual Explanations. We're really excited to share this awesome work
👥: Arthur's Karthik Rao & @Sahil1V
📍: #RaySummit in San Francisco
🗓: Tuesday, August 23
⏰: 3:15 PM PDT
Don't miss this talk about FastCFE, an algorithm that uses reinforcement learning to provide real-time counterfactual explanations. More info here: bit.ly/3BV8h1J
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ARTIFICIAL NEURAL NETWORKS ACTUALLY ARE ONLY SLIGHTLY SIMILAR TO BIOLOGICAL NEURONS. THERE'S AN AMAZING AMOUNT OF DIVERSITY AND COMPLEXITY IN THE BIOLOGY (DENDRITE STRUCTURE, NON-LINEAR CONDUCTANCES, HOMEOSTASIS) THAT THE DEEP LEARNING PEOPLE AREN'T EVEN PUTTING INTO THEIR MODELS
This week at AIES2022, Arthur Research Fellow @ziebrah is presenting her paper "Equalizing Credit Opportunity in Algorithms".
If you work in ML and financial services, check this one out!
arthur.ai/blog/arthur-resear…
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Working on anomaly detection but worried about "contamination" anomalies in the training data set?
Cool idea from arxiv.org/abs/2202.08088 - treat as a latent var problem and estimate whether each item is anomalous or normal, while also training AD model
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Informative post on the three typical architectures in Feature Stores
featureform.com/post/feature…
Breaking: In a 6-3 decision, the Supreme Court has declared that correlation does imply causation.
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We're super excited to be welcoming these stars to Arthur this summer. Meet our ML Research Fellows -
arthur.ai/blog/meet-our-summ…
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We're thrilled that @itsArthurAI will be presenting three talks at ODSC this week. I'll be presenting about high dimensional data drift, @jessicadai_ will be talking about Fair ML, and @karthik6d will be talking about counterfactual explanations. Hope to see you there!
This week, three members of the Arthur team will be speaking at @ODSC East in Boston, MA about drift detection, operationalizing fair ML, and more.
Hope to see you there! Click here for more info: hubs.la/Q018gcVS0
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Loving this amazing talk from @BasicScienceSav about geometric deep learning in particle physics at LHC. 🤯🤯🤯Thanks for hanging with us today at @itsArthurAI !
Counterfactual explanations are an exciting development in explainable AI. In our recent AAAI paper, we present an efficient and flexible technique for computing counterfactuals in high-scale settings. @karthik6d @Sahil1V @johnpdickerson
In our newest blog post, learn how to build and scale novel reinforcement learning algorithms using state-of-the-art technologies. You'll also find some starter code to get you going: hubs.la/Q016nzmC0
Keegan Hines retweeted
Arthur has been named one of the @BuiltInNewYork 2022 Best Places to Work! 🎉🏆🙌
We're honored to be recognized & celebrate what makes us truly great: our team. hubs.la/Q011yLbs0
PS: We’re hiring! Join Team Arthur 💜 Open roles: hubs.la/Q011yKD40
#2022BuiltInBest
Are you a graduate student or undergraduate studying ML fairness, explainability or monitoring? Come work with us at @itsArthurAI !
Applications are now open for our ML Research Fellows for Summer 2022 in NYC. Send in your CV before Jan 15!
arthur.ai/job-req?gh_jid=427…
Algorithmic bias can slip into NLP systems in subtle (and not-so-subtle) ways. In this post, @jessicadai_ lays out a pragmatic framework for assessing and addressing bias in NLP.
arthur.ai/blog/crash-course-…
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you're monitoring for data stability in your ML systems right?
in this post, Kenny Chen walks us through more than just metrics and gives a framework for production monitoring and automation
towardsdatascience.com/autom…
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