Deep Dive Workshop
Machine Learning Theory
Organized by:
Friday 9 October 10.05
Lead organizers: Nirupam Gupta, Tenure-Track Assistant Professor and Christian Igel, Full Professor, Department of Computer Science, University of Copenhagen
We welcome submissions of ongoing, recently published, or accepted work across all areas of ML theory, targeting MSc, PhD students, and postdocs.
Authors may submit for a long talk (20-30 minutes) or a poster with spotlight presentation, and may indicate willingness for either format. Tentative topics include (but not limited to) design and analysis of learning algorithms, optimization, online and reinforcement learning, deep learning theory, privacy, fairness and robustness.
Download Call for Participation
Submit an abstract (max 3000 characters) via this submission website
The session follows a workshop-style format combining invited talks, poster spotlight presentations, and an extended poster and networking session.
Welcome and introductory remarks (5 minutes)
Section 1 – Invited talks (50 minutes)
–
Section 2 – Poster spotlight session 1 (15 minutes)
7–8 short spotlight talks (2 minutes each) by poster presenters, giving a brief overview of their work to the full audience.
Break (10 minutes)
Section 3 – Invited talk (25 minutes)
Long talk 3 (25 minutes): Invited speaker presentation (20 min) followed by Q&A (5 min).
Section 4 – Poster spotlight session 2 (15 minutes)
7–8 short spotlight talks (2 minutes each) by poster presenters.
Section 5 – Poster session and open networking (60 minutes)
All poster presenters display their work. Participants circulate freely for in-depth discussion, feedback, and networking. This extended session is designed to foster new collaborations, particularly for PhD students and postdocs presenting their work.
Poster and spotlight contributions will be solicited through an open call. We welcome ongoing research, recently published work, open problems, and “theory for practice” presentations where applied AI challenges are posed for theoretical treatment.
Rasmus Pagh, Professor, University of Copenhagen:
Differential privacy and randomized algorithms for ML
Aasa Feragen, Professor, Technical University of Denmark (DTU Compute):
Fairness, robustness, and geometric perspectives in ML
Andrea Paudice, Assistant Professor, Aarhus University:
Robust machine learning and optimization algorithms
Backups:
Amartya Sanyal, Assistant Professor, University of Copenhagen:
Privacy, robustness, and unlearning
Yevgeny Seldin, Professor, University of Copenhagen:
Online learning and PAC-Bayes generalization bounds
All tentative talk titles are indicative and will be finalized later.
Intermediate: For attendees who have basic understanding or some experience with the subject but are not yet advanced.