LoG 2023 Reviewer Sign-up Form

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We are organizing the second Learning on Graphs (LoG) conference, and we are looking for reviewers to help us ensure the quality of the papers presented at the conference.

We have a special emphasis on review quality, a more focused conference topic, and a low reviewer load. Having excellent and constructive reviewers is our highest priority --- we would be delighted if you worked with us!

Reviews will be rated for their quality by authors and area chairs. The top reviewers will receive high monetary rewards!

As a reviewer, your responsibilities will include:

Reviewing papers assigned to you in a timely and constructive manner
Participating in discussion with authors and area chairs with the aim to help improve the papers
Making recommendations on whether the papers should be accepted, revised, or rejected

To be a reviewer, we expect that you meet the following criteria:

Have or be pursuing a higher degree in machine learning
Be willing to devote time and effort to reviewing duties
Have expertise in learning on graphs and geometry
Have experience in reviewing other top-tier venues (e.g., NeurIPS, ICML, and ICLR)

More details are available in our Call for Papers: https://logconference.org/cfp/

Name *

Openreview Email Address *

Google Scholar Profile

DBLP Profile

Personal Homepage

What is your current position? *

Undergraduate Student

Master Student

PhD Student

Industry Researcher

Postdoc

Professor

Other:

Have you served as a reviewer for a machine learning conference or journal before?
*

Yes

If yes, please describe your past experience in reviewing for machine learning venues: (e.g., ICML 2021/2022/2023)

How many papers are you expecting to review for LoG 2023?

1--2

3--4

More than 4

Clear selection

What area(s) are you willing to and able to review papers for? (a NON-exhaustive list of possible areas is in our call for papers: https://logconference.github.io/cfp/) *

Expressive Graph Neural Networks

GNN architectures (transformers, new positional encodings, …)

Equivariant architectures

Statistical theory on graphs

Causal inference (structural causal models, …)

Algorithmic reasoning

Geometry processing

Robustness and adversarial attacks on graphs

Combinatorial Optimization and Graph Algorithms

Graph Kernels

Graph Signal Processing/Spectral Methods

Graph Generative Models

Scalable Graph Learning Models and Methods

Graphs for Recommender Systems

Graph/Geometric ML for Computer Vision

Knowledge Graphs

Graph ML for Natural Language Processing

Graph/Geometric ML for Molecules (molecules, proteins, drug discovery, …)

Graph ML for Security

Graph ML for Health

Graph/Geometric ML for Physical sciences

Graph ML Platforms and Systems

Self-supervised learning on graphs

Trustworthy graph ML (fairness, privacy, …)

Graph/Geometric ML Infrastructures (datasets, benchmarks, libraries, …)

Other:

Required

Please provide any other relevant information about your qualifications as a reviewer, such as your experience in the field, your educational background, or your professional achievements. (Optional but highly recommended to answer if publication profile is none)

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