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Conferences / ICLR (International Conference on Learning Representations)

ICLR 2025

Apr 24–28, 2025 · Singapore EXPO, Singapore, Singapore

Conference
ICLR (International Conference on Learning Representations) Academic
Dates
Apr 24, 2025 – Apr 28, 2025
Venue
Singapore EXPO
Place
Singapore, Singapore
Format
Hybrid
Status
Past
Attendance
10,435
ICLR 2025 fact sheet: 11,039 participants from 85 countries, 10,435 in person and 604 virtual (in-person figure recorded; the 2026 fact sheet gives 11,137 total for 2025).
Website
iclr.cc
Recorded
60 sponsors · 6 speaker appearances
Notes
speakers: keynotes only (6 invited talks; affiliations from the ICLR blog keynote announcement). Bronze (no exhibit space, sponsored lounge) is priced above Silver (academic/startup-only booth) in the prospectus. DEI Action Fund levels normalized as other. Registration prices came from an interactive calculator that no longer returns past-year rates.

Ticket prices

No ticket prices recorded

Sponsorship packages

PackagePriceValid untilNotesSource
Publishers of Scientific Research Journals and Books$1,500—web.archive.org
Silver$12,500—web.archive.org
Bronze$15,000—web.archive.org
Groundbreaker DEI Action Fund$20,000—web.archive.org
Gold$22,500—web.archive.org
Trailblazer DEI Action Fund$40,000—web.archive.org
Platinum$45,000—web.archive.org
Pacesetter DEI Action Fund$60,000—web.archive.org
Champion DEI Action Fund$80,000—web.archive.org
Diamond$90,000—web.archive.org

Sponsors 60

Speakers and talks 6

SpeakerCompanyRoleTalkLinks
Danqi ChenPrinceton UniversityKeynoteTraining Language Models in Academia: Challenge or Calling?—
Dawn SongUniversity of California, BerkeleyKeynoteTowards Building Safe and Secure AI: Lessons and Open Challenges—
Song-Chun ZhuPeking University and Tsinghua UniversityKeynoteFramework, Prototype, Definition and Benchmark—
Tim RocktäschelGoogleKeynoteOpen-Endedness, World Models, and the Automation of Innovation—
Yi MaHong Kong UniversityKeynotePursuing the Nature of Intelligence—
Zico KolterCarnegie Mellon UniversityKeynoteBuilding Safe and Robust AI Systems—

Sources