Daniel Alabi

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 25

Top Topics

Hypothesis Testing

Linear Regression

Degree Distribution

Differentially Private

Top Venues

Trans. Mach. Learn. Res.

Proc. VLDB Endow.

Publications

Daniel Alabi, Atinuke Adegbile, Lekan Afuye, Philip Abel, Alida Monaco
NaijaCoder: Participatory Design for Early Algorithms Education in the Global South. SIGCSE (1) (2024)
Zezhou Huang, Jiaxiang Liu, Daniel Alabi, Raul Castro Fernandez, Eugene Wu
Saibot: A Differentially Private Data Search Platform. Proc. VLDB Endow. 16 (11) (2023)
Daniel Alabi, Dimitris Kalimeris
Degree Distribution Identifiability of Stochastic Kronecker Graphs. CoRR (2023)
Daniel Alabi, Omri Ben-Eliezer, Anamay Chaturvedi
Bounded Space Differentially Private Quantiles. Trans. Mach. Learn. Res. 2023 (2023)
Daniel Alabi, Pravesh K. Kothari, Pranay Tankala, Prayaag Venkat, Fred Zhang
Privately Estimating a Gaussian: Efficient, Robust, and Optimal. STOC (2023)
Daniel Alabi, Atinuke Adegbile, Lekan Afuye, Philip Abel, Alida Monaco
NaijaCoder: Participatory Design for Early Algorithms Education in the Global South. CoRR (2023)
Zezhou Huang, Jiaxiang Liu, Daniel Alabi, Raul Castro Fernandez, Eugene Wu
Saibot: A Differentially Private Data Search Platform. CoRR (2023)
Daniel Alabi, Chris Wiggins
Privacy Budget Tailoring in Private Data Analysis. Trans. Mach. Learn. Res. 2023 (2023)
Daniel Alabi, Pravesh K. Kothari, Pranay Tankala, Prayaag Venkat, Fred Zhang
Privately Estimating a Gaussian: Efficient, Robust and Optimal. CoRR (2022)
Daniel Alabi, Badih Ghazi, Ravi Kumar, Pasin Manurangsi
Private Rank Aggregation in Central and Local Models. AAAI (2022)
Daniel Alabi, Salil P. Vadhan
Hypothesis Testing for Differentially Private Linear Regression. NeurIPS (2022)
Daniel Alabi, Audra McMillan, Jayshree Sarathy, Adam D. Smith, Salil P. Vadhan
Differentially Private Simple Linear Regression. Proc. Priv. Enhancing Technol. 2022 (2) (2022)
Daniel Alabi, Omri Ben-Eliezer, Anamay Chaturvedi
Bounded Space Differentially Private Quantiles. CoRR (2022)
Daniel Alabi, Salil P. Vadhan
Hypothesis Testing for Differentially Private Linear Regression. CoRR (2022)
Daniel Alabi, Badih Ghazi, Ravi Kumar, Pasin Manurangsi
Private Rank Aggregation in Central and Local Models. CoRR (2021)
Daniel Alabi, Audra McMillan, Jayshree Sarathy, Adam D. Smith, Salil P. Vadhan
Differentially Private Simple Linear Regression. CoRR (2020)
Daniel Alabi, Adam Tauman Kalai, Katrina Ligett, Cameron Musco, Christos Tzamos, Ellen Vitercik
Learning to Prune: Speeding up Repeated Computations. CoRR (2019)
Daniel Alabi
The Cost of a Reductions Approach to Private Fair Optimization. CoRR (2019)
Daniel Alabi, Adam Tauman Kalai, Katrina Ligett, Cameron Musco, Christos Tzamos, Ellen Vitercik
Learning to Prune: Speeding up Repeated Computations. COLT (2019)
Daniel Alabi, Nicole Immorlica, Adam Tauman Kalai
When optimizing nonlinear objectives is no harder than linear objectives. CoRR (2018)
Daniel Alabi, Nicole Immorlica, Adam Kalai
Unleashing Linear Optimizers for Group-Fair Learning and Optimization. COLT (2018)
Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo I. Seltzer, Cynthia Rudin
Learning Certifiably Optimal Rule Lists. KDD (2017)
Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo I. Seltzer, Cynthia Rudin
Learning Certifiably Optimal Rule Lists for Categorical Data. CoRR (2017)
Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo I. Seltzer, Cynthia Rudin
Learning Certifiably Optimal Rule Lists for Categorical Data. J. Mach. Learn. Res. 18 (2017)
Daniel Alabi, Eugene Wu
PFunk-H: approximate query processing using perceptual models. HILDA@SIGMOD (2016)