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
- Pohang University of Science and Technology
- Birla Institute of Technology and Science, Pilani – Pilani Campus
- University of Illinois Urbana-Champaign
- Amazon
- École Polytechnique Fédérale de Lausanne
- Harvard University
- Los Alamos National Laboratory
- Mila
- Roche
- Technical University of Munich
- The Chinese University of Hong Kong
- University of California, Irvine
- University of Tübingen
- West Virginia University
- Aalto University
- Delhi Technological University
- ELLIS Institute Tübingen
- Emory University
- Indian Institute of Technology Delhi
- Industrial University of Santander
- INHA University
- Massachusetts Institute of Technology
- New York University
- Polytechnique Montréal
- University of California, Santa Barbara
- University of Pennsylvania
- University of Southern California
- University of Surrey
- Uppsala University
- Virginia Tech
[PDF]Hilbert Operator for Progressive Encoding (HOPE): A Mathematical Framework for Deconstructing Learned Representations in Deep Networks
[PDF]MetaExp: Learning to Self-Improve through Experience via Verifiable Cross-Task Environment Scaling
[PDF]When Do Heavy Tails Help? Identifiability Limits and Predictive Uncertainty in Lévy-Driven Neural SDEs for Electricity Markets
[PDF]Disentangle to Correct: Separating Temporal Structure from Noise in Multi-Agent Reinforcement Learning
[PDF]Beyond Feature Shift: Measuring and Mitigating Output Distribution Collapse in Cross-Domain VQA
[PDF]Do AI Weather Models Miss Extremes? Evidence from Ten Months of Verification Against European Weather Stations
[PDF]Generalizable AI-Generated Image Detection via Neighborhood Dependency Representation and Hierarchical Dependency Modeling
[PDF]MUSE: Dependency-Aware Adaptation of a Frozen Vision Backbone for Multivariate Time Series Forecasting
[PDF]Linear CKA Conflates Class-Mean and Within-Class Agreement: An Exact Covariance Decomposition on Labeled Benchmarks
[PDF]Spectral Dynamics in Deep Networks: Feature Learning, Outlier Escape, and Learning Rate Transfer
[PDF]Bayesian Flow Networks for Combinatorial Optimization: An Edge-based Approach to the Traveling Salesman Problem
1 If this project is interesting to you, reach out at [email protected].