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
- Technical University of Munich
- Mohamed bin Zayed University of Artificial Intelligence
- The Chinese University of Hong Kong
- Virginia Tech
- Industrial University of Santander
- Aalto University
- Indian Institute of Technology Delhi
- University of Pennsylvania
- University of Illinois Urbana-Champaign
- University of Surrey
- Polytechnique Montréal
- Roche
- École Polytechnique Fédérale de Lausanne
- West Virginia University
- 北京大学
- Emory University
- Massachusetts Institute of Technology
- Harvard University
- University of Tübingen
- National Taiwan University
- Uppsala University
- University of Maryland, College Park
- Birla Institute of Technology and Science, Pilani – Pilani Campus
- New York University
- University of Southern California
- INHA University
- University of California, Santa Barbara
- University of California, Irvine
- Pohang University of Science and Technology
- Ludwig-Maximilians-Universität München
- Mila
- Amazon
- University of California, Los Angeles
- ELLIS Institute Tübingen
- University of Moratuwa
- Shanghai Jiao Tong University
- Los Alamos National Laboratory
- Indian Institute of Technology Kharagpur
- Delhi Technological University
- ETH Zurich
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[PDF]Learning from Forecasting Errors: Separating Correctable Errors from Predictive Uncertainty in Time Series Forecasting
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[PDF]SAR-DiSCO: Differentiable Scattering Coefficient Optimization for 3D Adversarial Attacks on SAR Target Recognition
[PDF]Do Hyper-Connections Transfer to Reinforcement Learning? Sphere-Constrained Mixing and the Geometry of Depth.
[PDF]EvoTrace: Learning to Evolve Scientific Hypotheses from Trajectories of Agentic Symbolic Regression
[PDF]GaussianCollab: Communication-Efficient Latent Query Fusion via Tokenized Semantic Gaussians for Collaborative Occupancy Prediction
[PDF]CORE-Flow: Counterfactual Observation and Retrieval-Guided Exploration for Efficient Agentic Workflow Evolution
[PDF]From Sequence Uncertainty to Phenotype Hypotheses: Uncertainty-Conditioned Inference over Biomedical Knowledge Graphs
[PDF]Learning Discrete Semantic Invariance for Cross-Scene Hyperspectral Image Domain Generalization
[PDF]Clarifying the Concepts and Terminologies for representation of AI-Driven Parametric CAD Datasets
1 If this project is interesting to you, reach out at [email protected].