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
- 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
- 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
- Virginia Tech
- 北京大学
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[PDF]Hill Sampling for Test-Time Scaling: A Simple and Better Alternative to Repeated Sampling, Evolution, and Training
[PDF]Towards Understanding Specialization in MoE: A Mean-Field Perspective on Routing-Expert Dynamics
[PDF]V-CUES: Improving Multimodal Search Agents by Centering Visual-Cue Utilization via Experience and Skills
[PDF]StitchFL: Heterogeneous Submodel Training in Federated Learning for Membership Inference Attacks Mitigation
[PDF]Analytical Neural Operator (ANNO): Learning Reusable Analytical Structures for Time Evolution in PDE Families
[PDF]Seeing Near and Far: A Neural Operator That Efficiently Tokenizes and Decodes on Industrial-Scale CFD Meshes
[PDF]Equivariance Begins at the Source: Characterizing Compatible Sources for Generative Robot Policies
[PDF]Moderating Covertly Sensitive Images Across Policies: Policy-Reconfigurable Benchmark and Zero-Shot Method
1 If this project is interesting to you, reach out at [email protected].