About me

(Link to Google Scholar)

Datasets, benchmarking, and evaluation in ML:

1. Ali Shirali and Moritz Hardt. "What makes ImageNet look unlike LAION." (arXiv version, code)

2. Ali Shirali, Rediet Abebe, and Moritz Hardt. "A theory of dynamic benchmarks." In the Eleventh International Conference on Learning Representations (ICLR), 2023. (ICLR'23 version, arXiv version)

3. Ali Shirali. "Sequential nature of recommender systems disrupts the evaluation process."  In Advances in Bias and Fairness in Information Retrieval (BIAS), 2022. (BIAS'22 version, arXiv version, code, BIAS'22 talk, BIAS'22 slides)

Study of algorithms in socioeconomic contexts:

1. Ali Shirali, Rediet Abebe*, and Moritz Hardt*. "Allocation requires prediction only if inequality is low." (spotlight ⚡) In Forty-first International Conference on Machine Learning (ICML), 2024. (ICML'24 version, arXiv version)

2. Ali Shirali*, Jessie Finocchiaro*, and Rediet Abebe. "Participatory objective design via preference elicitation." In Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2024. (FAccT'24 version, FAccT'24 talk (email me if not accessible))

3. Rediet Abebe, Nicole Immorlica, Jon Kleinberg, Brendan Lucier, and Ali Shirali. "On the effects of triadic closure on network segregation." In Proceedings of the  ACM Conference on Economics and Computation (EC), 2022.  (EC'22 version, arXiv version, poster, short talk, EC'22 talk, TOC4Fairness talk)

Reliable algorithmic solutions:

1. Ali Shirali*, Alexander Schubert*, and Ahmed Alaa. "Pruning the way to reliable policies: a multi-objective deep Q-learning approach to critical care." In IEEE Journal of Biomedical and Health Informatics (JBHI), 2024 (arXiv version)

2. Mahdiyar Shahbazi*, Ali Shirali*, Hamid Aghajan, and Hamed Nili. "Using distance on the Riemannian manifold to compare representations in brain and in models." In NeuroImage, 2021. (NeuroImage version, code)

3. Ali Shirali, Reza Kazemi, and Arash Amini. "Collaborative filtering with representation learning in the frequency domain." (under review)

Thesis:

1. (In Persian) Ali Shirali. "Prediction of customer churn from subscription services in response to recommendations." [Masters dissertation, Sharif University]. (Sharif University Library)

* equal contribution