Jiaqi Ma
@jiaqima.bsky.social
Assistant Professor at UIUC. Interested in Trustworthy ML, Data Attribution, GenAI Copyright.
https://jiaqima.github.io/
https://jiaqima.github.io/
Announcing the 3rd Workshop on Regulatable ML @neuripsconf.bsky.social
Please consider submitting your work at the intersection between AI safety and regulatory frameworks to our workshop!
Submission Deadline: Aug 22
regulatableml.github.io
#NeurIPS2025 #CallForPapers #AISafety #AIRegulation
Please consider submitting your work at the intersection between AI safety and regulatory frameworks to our workshop!
Submission Deadline: Aug 22
regulatableml.github.io
#NeurIPS2025 #CallForPapers #AISafety #AIRegulation
August 4, 2025 at 4:19 PM
Announcing the 3rd Workshop on Regulatable ML @neuripsconf.bsky.social
Please consider submitting your work at the intersection between AI safety and regulatory frameworks to our workshop!
Submission Deadline: Aug 22
regulatableml.github.io
#NeurIPS2025 #CallForPapers #AISafety #AIRegulation
Please consider submitting your work at the intersection between AI safety and regulatory frameworks to our workshop!
Submission Deadline: Aug 22
regulatableml.github.io
#NeurIPS2025 #CallForPapers #AISafety #AIRegulation
Verifying AirPods Pro offer ... for making slides of an academic paper??
July 26, 2025 at 12:04 AM
Verifying AirPods Pro offer ... for making slides of an academic paper??
A Versatile Influence Function for Data Attribution with Non-Decomposable Loss: arxiv.org/pdf/2412.01335
We developed an efficient and easy-to-implement generalization of influence function applicable to machine learning objectives that are not decomposable to loss terms on individual data points.
We developed an efficient and easy-to-implement generalization of influence function applicable to machine learning objectives that are not decomposable to loss terms on individual data points.
July 16, 2025 at 4:10 PM
A Versatile Influence Function for Data Attribution with Non-Decomposable Loss: arxiv.org/pdf/2412.01335
We developed an efficient and easy-to-implement generalization of influence function applicable to machine learning objectives that are not decomposable to loss terms on individual data points.
We developed an efficient and easy-to-implement generalization of influence function applicable to machine learning objectives that are not decomposable to loss terms on individual data points.
More details about our poster sessions:
December 11, 2024 at 7:58 PM
More details about our poster sessions:
Join us at the #RegulatableML workshop at #NeurIPS2024 to learn about AI regulations and how to operationalize them in practice.
Date: Dec 15, 2024 (East Meeting Room 13)
Time: 8:15 am - 5:30 pm
Details: regulatableml.github.io
We have an exciting schedule (a thread):
1/
Date: Dec 15, 2024 (East Meeting Room 13)
Time: 8:15 am - 5:30 pm
Details: regulatableml.github.io
We have an exciting schedule (a thread):
1/
December 11, 2024 at 7:55 PM
Join us at the #RegulatableML workshop at #NeurIPS2024 to learn about AI regulations and how to operationalize them in practice.
Date: Dec 15, 2024 (East Meeting Room 13)
Time: 8:15 am - 5:30 pm
Details: regulatableml.github.io
We have an exciting schedule (a thread):
1/
Date: Dec 15, 2024 (East Meeting Room 13)
Time: 8:15 am - 5:30 pm
Details: regulatableml.github.io
We have an exciting schedule (a thread):
1/