We are excited to organize a workshop on "The Many Facets of
Preference-based Learning" at the International Conference of Machine
Learning (ICML), 2023, Hawaii.
The key focus of our workshop is to:
(A) Discuss and identify the different application areas of "learning
from preferences" feedback and
(B) Bridge and unify the techniques of "information aggregation from
preference feedback"
from different fields of study and exchange ideas.
*WORKSHOP WEBSITE:*
https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsites.google.com%2Fview%2Fmfpl-icml-2023&data=05%7C01%7Cuai%40engr.orst.edu%7C697ed806db684140371008db3d463845%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638171144019358120%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=iTtoHCGW%2FwvaypGOli6e0dAIjPDVfhVhq2Tr87ikSYo%3D&reserved=0
*OUR CONFIRMED INVITED SPEAKERS:*
Eytan Bakshy (Meta)
Chi Jin (Princeton)
Thorsten Joachims (Cornell)
Sanmi Koyejo (Stanford)
Dorsa Sadigh (Stanford)
Yisong Yue (Caltech)
We are accepting submissions now. Please go over the CALL FOR PAPERS below
and submit your best work!
*IMPORTANT DATES:*
Paper Submission Deadline: June 5, 2023
Notification of Acceptance: June 26, 2023
Workshop: July 28 (Friday) or 29 (Saturday), 2023
*WORKSHOP EMAIL (General queries):* learningprefere...@gmail.com
WORKSHOP OVERVIEW
Learning from human feedback has become increasingly important as the
complexity of problems solved by AI and machine learning grows. While
humans often find it difficult to provide demonstrations of the desired
system’s behavior or to quantify its responses using numerical values,
providing preferences (or comparisons) is natural. Therefore, it is not
surprising that learning from human preferences has been critical to major
recent advances in AI and machine learning, such as fine-tuning of large
language models, guided image generation, robotics, and self-driving cars.
Despite these ground-breaking successes, the most exciting opportunities
still lie ahead of us.
The goal of this workshop is to bring together scientists from communities
where preference-based learning has played a major role or has the
potential for making a breakthrough. We want to celebrate recent advances,
discuss main challenges and potential solutions, and pave the way for
future research directions. Additionally, we aim to strengthen the
connection between theory and practice by identifying real-world systems
that can benefit from incorporating preference feedback.
We cordially invite scientists who feel addressed by the theme of the
workshop to submit their latest works. Since preference-based learning had
impact on many communities, potential topics could be, but are not limited
to,
- Collaborative filtering
- Control theory
- Convex optimization
- Dueling and preference-based bandits
- Econometrics and assortment selection
- Explainability
- Fairness
- Game theory, equilibria, and multiplayer games
- Marketing and revenue management
- Multi-objective optimization
- Preference Elicitation
- Ranking aggregation
- Recommender systems
- Reinforcement learning
- Robotics
- Search engine optimization
- Social choice theory
SUBMISSION INSTRUCTIONS
Submitted papers should be in the ICML 2023 format (NOT ANONYMIZED) and up
to 6 pages long, excluding references and appendix. Accepted papers will be
presented as posters or contributed oral presentations.
Submissions should be uploaded as a single pdf file at:
https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fopenreview.net%2Fgroup%3Fid%3DICML.cc%2F2023%2FWorkshop%2FMFPL&data=05%7C01%7Cuai%40engr.orst.edu%7C697ed806db684140371008db3d463845%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638171144019358120%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=4c%2BVF4ifZEiPe0TUKvB%2BNlp6hMtZH8HO%2Fq9nJbfPNcI%3D&reserved=0
Sincerely,
ORGANIZERS
- Viktor Bengs (LMU, Germany)
- Robert Busa-Fekete (Google Research)
- Mohammad Ghavamzadeh (Google Research)
- Branislav Kveton (AWS AI Lab)
- Aadirupa Saha (Apple, ML Research)
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