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
- 北京大学
- Aalto University
- Technical University of Munich
- Indian Institute of Technology Delhi
- New York University
- University of California, Santa Barbara
- University of Tübingen
- University of Maryland, College Park
- The Chinese University of Hong Kong
- ELLIS Institute Tübingen
- Massachusetts Institute of Technology
- National Taiwan University
- West Virginia University
- Harvard University
- Mohamed bin Zayed University of Artificial Intelligence
- University of Surrey
- University of Illinois Urbana-Champaign
- École Polytechnique Fédérale de Lausanne
- ETH Zurich
- University of California, Los Angeles
- Polytechnique Montréal
- University of Southern California
- Indian Institute of Technology Dhanbad
- Ludwig-Maximilians-Universität München
- University of California, Irvine
- INHA University
- Emory University
- Mila
- Amazon
- Los Alamos National Laboratory
- Delhi Technological University
- Georgia Institute of Technology
- Roche
- University of Pennsylvania
- Shanghai Jiao Tong University
- University of Delaware
- United International University
- Indian Institute of Technology Kharagpur
- Virginia Tech
- Pohang University of Science and Technology
- Industrial University of Santander
- University of Moratuwa
- Birla Institute of Technology and Science, Pilani – Pilani Campus
- Uppsala University
[PDF]EMG-FoundBench: A Systematic Benchmark of Foundation Model Transfer and Adaptation for Electromyography
[PDF]CATOS: Compute–Accuracy Tradeoff Scalarization for Multiobjective Optimization in Neural architecture Search
[PDF]Optimizer-Model Consistency: Full Finetuning with the Same Optimizer as Pretraining Forgets Less
[PDF]Domain Generalization in-the-Wild: Disentangling Classification from Domain-Aware Representations
[PDF]DecayFM: A Physics-Structured Foundation Model for Data-Efficient Rare Particle Decay Identification
[PDF]Rethinking EEG-to-Image Decoding: From Fine-Grained Temporal Encoding to Hierarchy-Consistent Design
[PDF]Neutralizing 0th-Order Residual Interferences in Layer-wise Relevance Propagation via Geometric Trajectory Realignment
[PDF]Fragile Follow-Through: Uncovering How User Revisions Disrupt Long-Horizon Task Execution in LLM Agents
[PDF]Ground, Relate, and Match: Optimal-Transport-Guided Graph Grounding for Compositional Image–Text Matching
[PDF]Extracting Behavior from Frozen Latent World Models: Experience-Anchored Proposals with Offline Verifier Alignment
[PDF]AF-RAG: Active Falsification for Local Top-K Contamination in Literature-Grounded Question Answering
[PDF]Unveiling Lookahead’s Potential in Sharpness-Aware Minimization: Convergence and Generalization Revisited
1 If this project is interesting to you, reach out at [email protected].