- heuristic
- hype
- overstated
- ai generated
- frame
- statistical robustness
Vision Language Model
- Evaluate separately by error type list with prompts using the whole paper
- Automatic score calculation (Aggregator)
- Automatic review generation with overall score and detected issues
AI service
Google PAT
ICML Experimental Program using Google’s Paper Assistant Tool (PAT)
Google Technical Staff: Rajesh Jayaram Vincent Cohen-Addad Drew Tyler Jieming Mao Jon Schneider
https://blog.icml.cc/2026/01/14/icml-experimental-program-using-googles-paper-assistant-tool-pat/
author ranking
Owner-assisted peer review via Isotonic Mechanism
You Are the Best Reviewer of Your Own Papers: An Owner-Assisted...
I consider a setting where reviewers offer very noisy scores for several items for the selection of high-quality ones (e.g., peer review of large conference proceedings), whereas the owner of...
https://arxiv.org/abs/2110.14802

You Are the Best Reviewer of Your Own Papers: The Isotonic Mechanism
Machine learning (ML) and artificial intelligence (AI) conferences including NeurIPS and ICML have experienced a significant decline in peer review quality in recent years. To address this growing...
https://arxiv.org/abs/2206.08149

Isotonic Mechanism for Exponential Family Estimation in Machine...
In 2023, the International Conference on Machine Learning (ICML) required authors with multiple submissions to rank their submissions based on perceived quality. In this paper, we aim to employ...
https://arxiv.org/abs/2304.11160

An Isotonic Mechanism for Overlapping Ownership
Motivated by the problem of improving peer review at large scientific conferences, this paper studies how to elicit self-evaluations to improve review scores in a natural many-to-many owner-item...
https://arxiv.org/abs/2306.11154

The ICML 2023 Ranking Experiment: Examining Author Self-Assessment...
We conducted an experiment during the review process of the 2023 International Conference on Machine Learning (ICML), asking authors with multiple submissions to rank their papers based on...
https://arxiv.org/abs/2408.13430


Seonglae Cho