acceptodds: Which papers will get accepted at ICLR 2027?
Abstract
A prediction market on peer review. Each paper has a market on its decision (accept or reject), priced by researchers who stake $rep, an in app currency, on what they expect. Everyone who signs up receives 1,000 $rep to trade with. Prices are probabilities, and every market settles when the venue publishes its decisions. It is a game made for fun, and has no connection to ICLR, OpenReview or any other organisation. acceptodds is an open source project, open to contributions, at github.com/benedict-armstrong/acceptodds.1
- Los Alamos National Laboratory
- Technical University of Munich
- Industrial University of Santander
- University of Maryland, College Park
- Amazon
- Mila
- Johannes Kepler University of Linz
- University of California, Los Angeles
- Uppsala University
- University of Surrey
- University of Delaware
- Ludwig-Maximilians-Universität München
- Birla Institute of Technology and Science, Pilani – Pilani Campus
- University of Pennsylvania
- Aalto University
- University of Moratuwa
- National Taiwan University
- University of California, Santa Barbara
- Indian Institute of Technology Delhi
- 北京大学
- Virginia Tech
- University of Illinois Urbana-Champaign
- Delhi Technological University
- University of Tübingen
- Massachusetts Institute of Technology
- ELLIS Institute Tübingen
- Indian Institute of Technology Kharagpur
- United International University
- University of California, Irvine
- École Polytechnique
- Indian Institute of Technology Dhanbad
- Pohang University of Science and Technology
- New York University
- École Polytechnique Fédérale de Lausanne
- Polytechnique Montréal
- West Virginia University
- Shanghai Jiao Tong University
- Harvard University
- Roche
- Mohamed bin Zayed University of Artificial Intelligence
- INHA University
- University of Southern California
- Emory University
- ETH Zurich
- The Chinese University of Hong Kong
- Georgia Institute of Technology
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[PDF]Introspection or Post-Hoc Rationalization? A Cross-Model and Cross-Domain Study of LLM Metacognition
[PDF]STAD: Transferable Active Defense against Generative Image Steganography via Structural Feature Perturbations
[PDF]On the effectiveness of reward functions in reinforcement learning for confidence calibration of large language models
[PDF]Compact Chain-of-Thought Distillation with Visual Grounding Guidance for Vision-Language Models
1 If this project is interesting to you, reach out at [email protected].