Ever wondered how generalization bounds in quantum machine learning compare to classical #ML theory? In classical ML, tools like Hoeffding's inequality provide theoretical guarantees on out-of-sample risk based on data and model complexity. But in the noisy quantum era, does noise redefine these bounds? Dive into our latest survey exploring the state of generalization in #QML and the challenges noise introduces.
Read more:
Brier - https://buff.ly/3ZfrVQ0
Paper - https://buff.ly/3B62JUm

This Thanksgiving, we're honoring the ML breakthroughs shaping the field—Transformers, BERT, GANs, and more. From NLP to generative AI, these innovations are worth celebrating and using responsibly. Read more: https://buff.ly/3OtI9QH #AI #MachineLearning #Thanksgiving

D-ReLU: A breakthrough in robust AI, designed to defend against adversarial attacks while maintaining efficiency and scalability. This research, led by Korn Sooksatra (now at Meta), has implications for high-stakes AI applications. Blog: https://buff.ly/4fC9GeP Full paper: https://buff.ly/3UXzNVi #ResponsibleAI #SafeAI #AdversarialML

Optimizing Fairness and Robustness in Machine Learning Models. Keynote at #NeurIPS 2022 - #LXAI workshop. #TrustworthyAI #RobustAI #EthicalAI #SafeAI #ResponsibleAI
https://baylor.ai/?tag=ai-orthopraxy

🚗🔧 Our undergraduate research team at Baylor University is leveraging Vision Transformers (ViT) to detect patterns in online car part sales that could indicate illicit activity. Learn how we're making a difference in the fight against organized crime.
👉 Read more: https://buff.ly/3U5s6vz
#BaylorUniversity #AIResearch #MachineLearning #Cybersecurity #CarParts #Innovation

Dataset documentation fans, please check out "Data Statements: From Technical Concept to Community Practice" (McMillan-Major, Bender & Friedman 2023) -- reporting on how we took data statements v1 to v2 through learning with and from practitioners.

https://dl.acm.org/doi/10.1145/3594737

#nlp #DataDocumentation #ethnlp

@Riedl I couldn't agree more. Glad that this is being noticed.

Tomorrow, Monday, I'll give a keynote on Trustworthy AI from a fairness and adversarial robustness point of view at the #LXAI Workshop of the @NeuripsConf.
How does robustness relate to fairness?
How do you quantify robustness?
See you in New Orleans to chat!
#ResponsibleAI

Kitty Rivas is keeping company this #caturday morning as we ponder if we should write #iclr2023 rebuttals or call it quits.

Testing posting with #toot attaching an image.

It seems simple enough.

If you will be at #NeurIPS2022 don't be a stranger🙈 and come visit. Also come to this workshop, see, engage, and support #LatinX machine learning research. 🤓🐻
https://www.latinxinai.org/neurips-2022

Did you miss the third workshop of the National Institute of Standards and Technology (NIST) #AI Risk Management Framework? No worries! You can check out recordings from all of the talks & panels, as well as resources referenced during the panels. #ResponsibleAI
https://www.nist.gov/news-events/events/2022/10/building-nist-ai-risk-management-framework-workshop-3