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
- ETH Zurich
- University of Maryland, College Park
- University of Illinois Urbana-Champaign
- Birla Institute of Technology and Science, Pilani – Pilani Campus
- Pohang University of Science and Technology
- West Virginia University
- Amazon
- École Polytechnique Fédérale de Lausanne
- Harvard University
- Indian Institute of Technology Delhi
- Los Alamos National Laboratory
- Massachusetts Institute of Technology
- Mila
- Roche
- Technical University of Munich
- The Chinese University of Hong Kong
- University of California, Irvine
- University of Southern California
- University of Tübingen
- Virginia Tech
- 北京大学
- Aalto University
- Delhi Technological University
- ELLIS Institute Tübingen
- Emory University
- Indian Institute of Technology Kharagpur
- Industrial University of Santander
- INHA University
- Ludwig-Maximilians-Universität München
- National Taiwan University
- New York University
- Polytechnique Montréal
- University of California, Los Angeles
- University of California, Santa Barbara
- University of Moratuwa
- University of Pennsylvania
- University of Surrey
- Uppsala University
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[PDF]Recursive Transformer with State Injection and Per-Slice Supervision for Thermo-Mechanical Surrogate Modeling
[PDF]BASIL: Batch-Aware Single-Cell Perturbation Response Modeling with Conditional Distribution Alignment
[PDF]HIVE: History-Initialized Visuomotor Evolution with a Compact Visual Bottleneck for Robotic Manipulation
[PDF]ReCo:Training-Free Visual Token Pruning via Relational Transition Coreset in Large Vision-Language Models
[PDF]DR-VLA: Learning Human-Like Quadruped Navigation from Egocentric Web Videos via Domain-Invariant Scene Representations
[PDF]From Inverted Codes to Anisotropic Encoders: A Single Ratio Predicts Few-Shot Out-of-Distribution Accuracy
1 If this project is interesting to you, reach out at [email protected].