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: https://t.co/NSkfk52srz
#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!
https://t.co/PxhfFMC6Y9
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 β¦) Metropolis-Hasting algorithm samples from a density known only up to a constant. https://t.co/l8FeCgahfV
Today @karthik6d and @Sahil1V are presenting at Ray Summit, sharing our work on Counterfactual Explanations. We're really excited to share this awesome work
π₯: Arthur's Karthik Rao & @Sahil1V
π: #RaySummit in San Francisco
π: Tuesday, August 23
β°: 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: https://t.co/QsGdfTItRl
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!
https://t.co/q8NQ9SRgJ6
Working on anomaly detection but worried about "contamination" anomalies in the training data set?
Cool idea from https://t.co/18TUAeivHH - treat as a latent var problem and estimate whether each item is anomalous or normal, while also training AD model
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: https://t.co/3To1lAXOqS
Loving this amazing talk from @BasicScienceSav about geometric deep learning in particle physics at LHC. π€―π€―π€―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: https://t.co/3ZfDqVuRoh
Arthur has been named one of the @BuiltInNewYork 2022 Best Places to Work! πππ
We're honored to be recognized & celebrate what makes us truly great: our team. https://t.co/TwvWhDDQtY
PS: Weβre hiring! Join Team Arthur π Open roles: https://t.co/2H4KvMARMj
#2022BuiltInBest