Workshop on Learning-augmented Algorithms: Theory
and Applications, ACM
SIGMETRICS 2023
This workshop will cover recent results, as well as new emerging directions in the rapidly-advancing field of learning-augmented algorithms, also known as algorithms with predictions or algorithms with ML advice. This nascent area, at the intersection of TCS and ML, studies the interplay between ML and the design/analysis of algorithms with strict, provable performance guarantees. Its aim is to provide insight into fundamental questions related to modeling, performance evaluation, and analysis techniques.
Call for Posters
Posters provide a platform for discussing works in progress, new research directions, and challenges related to learning-augmented algorithms. We solicit two types of posters. First, we invite poster submissions that present new ideas, work in progress, ongoing research directions, and preliminary results. Second, we invite authors of recently published papers on topics related to Algorithms with Predictions to present their work as a poster.
Important Dates:
Submission deadline: May 1st, 2023
Notification of poster acceptance: May 8th, 2023
Submission Requirements and Guidelines:
The submission should consist of a single pdf file containing the names and affiliations of the contributors, a title, a brief abstract, and a summary of the contributions. Preliminary results may also be included. In case the poster presents results of the recently-published papers, bibliographic references to the paper (authors, title, and venue) must be included. Poster abstracts must not be more than two pages, including references and figures, in the ACM small template format. Review process: Submitted posters will be reviewed within the period from April 10th to April 17th. The review will be minimal and focus primarily on relevance to the workshop focus and likelihood of leading to interesting discussions at the workshop. Accepted posters will be presented during the workshop breaks.
Organizers:
Student Organizers: Adam Lechowicz (UMass Amherst), Nicolas Christianson (Caltech), Jianyi Yang (UC Riverside), Georgii Melidi (Sorbonne)
This workshop will cover recent results, as well as new emerging directions in the rapidly-advancing field of learning-augmented algorithms, also known as algorithms with predictions or algorithms with ML advice. This nascent area, at the intersection of TCS and ML, studies the interplay between ML and the design/analysis of algorithms with strict, provable performance guarantees. Its aim is to provide insight into fundamental questions related to modeling, performance evaluation, and analysis techniques.
Call for Posters
Posters provide a platform for discussing works in progress, new research directions, and challenges related to learning-augmented algorithms. We solicit two types of posters. First, we invite poster submissions that present new ideas, work in progress, ongoing research directions, and preliminary results. Second, we invite authors of recently published papers on topics related to Algorithms with Predictions to present their work as a poster.
Important Dates:
Submission deadline: May 1st, 2023
Notification of poster acceptance: May 8th, 2023
Submission Requirements and Guidelines:
The submission should consist of a single pdf file containing the names and affiliations of the contributors, a title, a brief abstract, and a summary of the contributions. Preliminary results may also be included. In case the poster presents results of the recently-published papers, bibliographic references to the paper (authors, title, and venue) must be included. Poster abstracts must not be more than two pages, including references and figures, in the ACM small template format. Review process: Submitted posters will be reviewed within the period from April 10th to April 17th. The review will be minimal and focus primarily on relevance to the workshop focus and likelihood of leading to interesting discussions at the workshop. Accepted posters will be presented during the workshop breaks.
Organizers:
- Spyros Angelopoulos, CNRS and Sorbonne University, spyros.angelopoulos@lip6.fr
- Mohammad Hajiesmaili, UMass Amherst, hajiesmaili@cs.umass.edu
- Shahin Kamali, York University, kamalis@yorku.ca
- Shaolei Ren, UC Riverside, sren@ece.ucr.edu
- Adam Wierman, Caltech, adamw@caltech.edu
Student Organizers: Adam Lechowicz (UMass Amherst), Nicolas Christianson (Caltech), Jianyi Yang (UC Riverside), Georgii Melidi (Sorbonne)
Welcome to the SIGMETRIS workshop on Learning-augmented Algorithms: Theory and Applications (SIGMETRICS-LATA) submissions site. For general information, see https://learning-augmented-algorithms.github.io/.
Submissions
Submissions are currently closed.