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First Online: 17 September 2023

Ethical Statement

: As researchers in the field of deep neural networks, we recognize the importance of developing methods that improve the generalization capabilities of these models, particularly for minority groups that may be underrepresented in training data. Our proposed reweighted sparse training framework, REST, aims to tackle the issue of bias-conflicting correlations in DNNs by reducing reliance on spurious correlations. We believe that this work has the potential to enhance the robustness of DNNs and improve their performance on out-of-distribution samples, which may have significant implications for various applications such as healthcare and criminal justice. However, we acknowledge that there may be ethical considerations associated with the development and deployment of machine learning algorithms, particularly those that may impact human lives. As such, we encourage the responsible use and evaluation of our proposed framework to ensure that it aligns with ethical standards and does not perpetuate biases or harm vulnerable populations.

Conference Information

Conference Acronym: ECML PKDD

Conference Name: Joint European Conference on Machine Learning and Knowledge Discovery in Databases

Conference City: Turin

Conference Country: Italy

Conference Year: 2023

Conference Start Date: 18 September 2023

Conference End Date: 22 September 2023

Conference Number: 23

Conference ID: ecml2023

Conference URL: https://2023.ecmlpkdd.org/

Peer Review Information (provided by the conference organizers)

Type: Double-blind

Conference Management System: CMT

Number of Submissions Sent for Review: 829

Number of Full Papers Accepted: 196

Number of Short Papers Accepted: 0

Acceptance Rate of Full Papers: 24% - The value is computed by the equation "Number of Full Papers Accepted / Number of Submissions Sent for Review * 100" and then rounded to a whole number.

Average Number of Reviews per Paper: 3.63

Average Number of Papers per Reviewer: 4.5

External Reviewers Involved: Yes

Additional Info on Review Process: Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.