Daniel Alabi

According to our database, Daniel Alabi authored at least 14 papers between 2016 and 2023.

Collaborative distances:

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

2023

Saibot: A Differentially Private Data Search Platform.

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

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

Bounded Space Differentially Private Quantiles.

Daniel Alabi, Omri Ben-Eliezer, Anamay Chaturvedi

CoRR, 2022

Hypothesis Testing for Differentially Private Linear Regression.

Daniel Alabi, Salil P. Vadhan

NeurIPS, 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