Main Conference Timetable for Authors

Note: all deadlines are “anywhere on earth” (UTC-12)

July 4, 2023
AAAI-24 web site open for author registration

July 11, 2023
AAAI-24 web site open for paper submission

August 8, 2023
Abstracts due at 11:59 PM UTC-12

August 15, 2023
Full papers due at 11:59 PM UTC-12

August 18, 2023
Supplementary material and code due by 11:59 PM UTC-12

September 25, 2023
Registration, abstracts and full papers for NeurIPS fast track submissions due by 11:59 PM UTC-12

September 27, 2023
Notification of Phase 1 rejections

September 28, 2023
Supplementary material and code for NeurIPS fast track submissions due by 11:59 PM UTC-12

November 2-5, 2023
Author feedback window

December 9, 2023
Notification of final acceptance or rejection

December 19, 2023
Submission of paper preprints for inclusion in electronic conference materials

February 20 – February 27, 2024
AAAI-24 conference

Reproducibility Checklist

Unless specified otherwise, please answer “yes” to each question if the relevant information is described either in the paper itself or in a technical appendix with an explicit reference from the main paper. If you wish to explain an answer further, please do so in a section titled “Reproducibility Checklist” at the end of the technical appendix.

This paper

• Includes a conceptual outline and/or pseudocode description of AI methods introduced (yes/partial/no/NA)
• Clearly delineates statements that are opinions, hypothesis, and speculation from objective facts and results (yes/no)
• Provides well marked pedagogical references for less-familiare readers to gain background necessary to replicate the paper (yes/no)

Does this paper make theoretical contributions? (yes/no)

If yes, please complete the list below.

• All assumptions and restrictions are stated clearly and formally. (yes/partial/no)
• All novel claims are stated formally (e.g., in theorem statements). (yes/partial/no)
• Proofs of all novel claims are included. (yes/partial/no)
• Proof sketches or intuitions are given for complex and/or novel results. (yes/partial/no)
• Appropriate citations to theoretical tools used are given. (yes/partial/no)
• All theoretical claims are demonstrated empirically to hold. (yes/partial/no/NA)
• All experimental code used to eliminate or disprove claims is included. (yes/no/NA)

Does this paper rely on one or more datasets? (yes/no)

If yes, please complete the list below.

• A motivation is given for why the experiments are conducted on the selected datasets (yes/partial/no/NA)
• All novel datasets introduced in this paper are included in a data appendix. (yes/partial/no/NA)
• All novel datasets introduced in this paper will be made publicly available upon publication of the paper with a license that allows free usage for research purposes. (yes/partial/no/NA)
• All datasets drawn from the existing literature (potentially including authors’ own previously published work) are accompanied by appropriate citations. (yes/no/NA)
• All datasets drawn from the existing literature (potentially including authors’ own previously published work) are publicly available. (yes/partial/no/NA)
• All datasets that are not publicly available are described in detail, with explanation why publicly available alternatives are not scientifically satisficing. (yes/partial/no/NA)

Does this paper include computational experiments? (yes/no)

If yes, please complete the list below.

• Any code required for pre-processing data is included in the appendix. (yes/partial/no).
• All source code required for conducting and analyzing the experiments is included in a code appendix. (yes/partial/no)
• All source code required for conducting and analyzing the experiments will be made publicly available upon publication of the paper with a license that allows free usage for research purposes. (yes/partial/no)
• All source code implementing new methods have comments detailing the implementation, with references to the paper where each step comes from (yes/partial/no)
• If an algorithm depends on randomness, then the method used for setting seeds is described in a way sufficient to allow replication of results. (yes/partial/no/NA)
• This paper specifies the computing infrastructure used for running experiments (hardware and software), including GPU/CPU models; amount of memory; operating system; names and versions of relevant software libraries and frameworks. (yes/partial/no)
• This paper formally describes evaluation metrics used and explains the motivation for choosing these metrics. (yes/partial/no)
• This paper states the number of algorithm runs used to compute each reported result. (yes/no)
• Analysis of experiments goes beyond single-dimensional summaries of performance (e.g., average; median) to include measures of variation, confidence, or other distributional information. (yes/no)
• The significance of any improvement or decrease in performance is judged using appropriate statistical tests (e.g., Wilcoxon signed-rank). (yes/partial/no)
• This paper lists all final (hyper-)parameters used for each model/algorithm in the paper’s experiments. (yes/partial/no/NA)
• This paper states the number and range of values tried per (hyper-) parameter during development of the paper, along with the criterion used for selecting the final parameter setting. (yes/partial/no/NA)