Any views expressed within media held on this service are those of the contributors, should not be taken as approved or endorsed by the University, and do not necessarily reflect the views of the University in respect of any particular issue.

【BAYES COFFEE HOUSE TECH TALK SERIES】On Learning Latent Models with Multi-Instance Weak Supervision

Efi Tsamoura will give a talk, in person and online, for the Coffee House Tech Talk Series. Details of the talk as below, lunch will be provided.

 

Title: On Learning Latent Models with Multi-Instance Weak Supervision.

Speaker: Efi Tsamoura, Samsung AI

When: 12nn Thu 23 Nov

Where: 4th floor Bayes Centre

External link:

https://meeting.huaweicloud.com/welink/j/94584057/Y636nF2lqRtlVZvXQdnkLmpJ34TfuNv7U

Meeting ID: 94584057
Passcode: 376147

 

Abstract:

We consider a weakly supervised learning scenario where the supervision signal is generated by a transition function σ of labels associated with multiple input instances. We formulate this problem as multi-instance Partial Label Learning (multi-instance PLL), which is an extension to the standard PLL problem. Our problem is met in different fields, including latent structural learning and neuro-symbolic integration. Despite the existence of many learning techniques, limited theoretical analysis has been dedicated to this problem. We provide the first theoretical study of multi-instance PLL with possibly an unknown transition σ. We make minimal assumptions on the data distributions. In fact, we prove learnability even under the “toughest” distributions that concentrate their mass on a single instance. In addition, we provide learning guarantees under widely used surrogate losses for training classifiers subject to logical theories. We are the first to provide this theoretical analysis, closing a gap in the neuro-symbolic and latent structural learning literature. This work will be presented in NeurIPS 2023: https://arxiv.org/pdf/2306.13796.pdf.

 

Bio:

Efi Tsamoura is a Senior Researcher at Samsung AI, Cambridge, UK. In 2016, she was awarded an early career fellowship from the Alan Turing Institute, UK, and before that, she was a Postdoctoral Researcher in the Department of Computer Science of the University of Oxford. Her main research interests lie in the areas of logic, knowledge representation and reasoning, and neuro-symbolic integration. Her research has been published in top-tier AI and database venues (SIGMOD, VLDB, PODS, AAAI, ICML, NeurIPS, etc.). Efi started the Samsung AI neuro-symbolic workshop series “When deep learning meets logic” and has been a keynote speaker in the 2023 Extended Semantic Web Conference.  

css.php

Report this page

To report inappropriate content on this page, please use the form below. Upon receiving your report, we will be in touch as per the Take Down Policy of the service.

Please note that personal data collected through this form is used and stored for the purposes of processing this report and communication with you.

If you are unable to report a concern about content via this form please contact the Service Owner.

Please enter an email address you wish to be contacted on. Please describe the unacceptable content in sufficient detail to allow us to locate it, and why you consider it to be unacceptable.
By submitting this report, you accept that it is accurate and that fraudulent or nuisance complaints may result in action by the University.

  Cancel