LXAI Workshop Program Committee Application

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Application to join the Program Committee for the Latinx in AI Research Workshop Colocated with NeurIPS 2023.

We welcome work in artificial intelligence, including, but not limited to, computer vision, deep learning, knowledge reasoning, machine learning, multi-agent systems, natural language processing, statistical reasoning, theory, robotics, as well as applications of AI to other domains such as health and education, and submissions concerning fairness, ethics, and transparency in AI.

Papers may introduce new theory, methodology, or applications. We also welcome position papers and demos related to these areas. Work may be previously published, completed, or ongoing. Submissions will be peer-reviewed by at least 2 reviewers in the area.

As a program committee member we expect you to be able to review 2-3 submissions.

Important Dates
Deadline to join the program committee: 11-Sep-2023.

Deadline to provide your review is 16-Oct-2023.

Email *

Full Name *

Institution/Company *

Highest Degree

If you are currently pursuing a degree, please choose the level of your current program.

PhD

MSc or equivalent

BSc or equivalent

Other:

Area of specialization of higest degree
*

Computer Science

Mathematics

Physics

Neuroscience

Statistics

Other:

Required

Professional Title

For example: Sr Research Scientist, PhD Student, Data Scientist

Country of Residence *

Link to CV *

Areas of Specialization *

N/A

Basic

Intermediate

Expert

Reinforcement Learning (e.g., decision and control, planning, hierarchical RL)

Representation learning and Theory (e.g., control theory, learning theory, algorithmic game theory)

Optimization (e.g., convex and non-convex optimization)

Applications (e.g., speech processing, computational biology, computer vision, NLP)

Deep Learning (e.g., architectures, generative models, optimization for deep networks)

General Machine Learning (e.g., classification, unsupervised learning, transfer learning)

Probabilistic Methods (e.g., variational inference, causal inference, Gaussian processes)

Social Aspects of Machine Learning (e.g., AI safety, fairness, privacy, interpretability)

Infrastructure (e.g., datasets, competitions, implementations, libraries)

Neuroscience and Cognitive Science (e.g., neural coding, brain-computer interfaces)

Large Language Models (e.g., training methodologies, content generation, code completion, distillation, biases)

Generative AI (e.g., model architectures, data synthesis, art generation, latent space analysis, applications)