Daniel Alabi

According to our database, Daniel Alabi authored at least 19 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Lecture Notes from the NaijaCoder Summer Camp.

Daniel Alabi, Joseph Ekpenyong, Alida Monaco

CoRR, 2024

NaijaCoder: Participatory Design for Early Algorithms Education in the Global South.

Daniel Alabi, Atinuke Adegbile, Lekan Afuye, Philip Abel, Alida Monaco

Proceedings of the 55th ACM Technical Symposium on Computer Science Education, 2024

2023

Bounded Space Differentially Private Quantiles.

Daniel Alabi, Omri Ben-Eliezer, Anamay Chaturvedi

Trans. Mach. Learn. Res., 2023

Privacy Budget Tailoring in Private Data Analysis.

Daniel Alabi, Chris Wiggins

Trans. Mach. Learn. Res., 2023

Saibot: A Differentially Private Data Search Platform.

Zezhou Huang, Jiaxiang Liu, Daniel Alabi, Raul Castro Fernandez, Eugene Wu

Proc. VLDB Endow., 2023

Differentially Private Hypothesis Testing for Linear Regression.

Daniel Alabi, Salil P. Vadhan

J. Mach. Learn. Res., 2023

Degree Distribution Identifiability of Stochastic Kronecker Graphs.

Daniel Alabi, Dimitris Kalimeris

CoRR, 2023

Privately Estimating a Gaussian: Efficient, Robust, and Optimal.

Daniel Alabi, Pravesh K. Kothari, Pranay Tankala, Prayaag Venkat, Fred Zhang

Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 2023

2022

Differentially Private Simple Linear Regression.

Daniel Alabi, Audra McMillan, Jayshree Sarathy, Adam D. Smith, Salil P. Vadhan

Proc. Priv. Enhancing Technol., 2022

Hypothesis Testing for Differentially Private Linear Regression.

Daniel Alabi, Salil P. Vadhan

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Private Rank Aggregation in Central and Local Models.

Daniel Alabi, Badih Ghazi, Ravi Kumar, Pasin Manurangsi

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2019

The Cost of a Reductions Approach to Private Fair Optimization.

Daniel Alabi

CoRR, 2019

Learning to Prune: Speeding up Repeated Computations.

Daniel Alabi, Adam Tauman Kalai, Katrina Ligett, Cameron Musco, Christos Tzamos, Ellen Vitercik

Proceedings of the Conference on Learning Theory, 2019

2018

Systems Optimizations for Learning Certifiably Optimal Rule Lists.

Nicholas Larus-Stone, Elaine Angelino, Daniel Alabi, Margo Seltzer, Vassilios Kaxiras, Aditya Saligrama, Cynthia Rudin

Proceedings of the SysML Conference 2018, February, 2018

When optimizing nonlinear objectives is no harder than linear objectives.

Daniel Alabi, Nicole Immorlica, Adam Tauman Kalai

CoRR, 2018

Unleashing Linear Optimizers for Group-Fair Learning and Optimization.

Daniel Alabi, Nicole Immorlica, Adam Kalai

Proceedings of the Conference On Learning Theory, 2018

2017

Learning Certifiably Optimal Rule Lists for Categorical Data.

Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo I. Seltzer, Cynthia Rudin

J. Mach. Learn. Res., 2017

Learning Certifiably Optimal Rule Lists.

Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo I. Seltzer, Cynthia Rudin

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017

2016

PFunk-H: approximate query processing using perceptual models.

Daniel Alabi, Eugene Wu

Proceedings of the Workshop on Human-In-the-Loop Data Analytics, 2016